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Introduction to genetics

Genetics is a field of biology that studies how traits are passed from parents to their offspring. The passing of traits from parents to offspring is known as heredity, therefore, genetics is the study of heredity. This introduction to genetics takes you through the basic components of genetics such as DNA, genes, chromosomes and genetic inheritance.

Genetics is built around molecules called DNA. DNA molecules hold all the genetic information for an organism. It provides cells with the information they need to perform tasks that allow an organism to grow, survive and reproduce. A gene is one particular section of a DNA molecule that tells a cell to perform one specific task.

Heredity is what makes children look like their parents. During reproduction, DNA is replicated and passed from a parent to their offspring. This inheritance of genetic material by offspring influences the appearance and behavior of the offspring. The environment that an organism lives in can also influence how genes are expressed.

A DNA molecule is a nucleic acid, one of the four molecules of life . It comes in the form of a long, linear molecule referred to as a strand. Each strand of DNA is bonded to a second strand of DNA to form a DNA double helix. In eukaryotic cells , DNA is found in the nucleus as a tightly coiled double helix.

DNA molecules are replicated during cell division. When a cell divides, the two new cells contain all the same DNA that the original cell had.

In sexual reproduction with two parents, half of the DNA of the offspring is provided by each of the parents. The genetic material of a child is made from 50% of their mother’s DNA and 50% their father’s DNA.

A gene is a specific segment of a DNA molecule that holds the information for one specific protein. DNA molecules have a unique code for each gene which codes for their specific protein. Some organisms can have more than 100,000 different genes so they will have 100,000 unique sequences of DNA ‘code’.

Genes are the basic unit of heredity. The genes of an individual are determined by their parent or parents. A bacteria that is born by one parent cell splitting into two cells and has the exact same genes as their one parent cell.

Physical traits such as eye color or height are often determined by the combination of multiple genes. The environment an individual lives in also impacts how genes are expressed.

Chromosomes

A chromosome is a structure made from tightly packed strands of DNA and proteins called histones. Strands of DNA are tightly wrapped around the histone proteins and form into long worm-shaped structures called ‘chromatids’. Two chromatids join together to form a chromosome.

Chromosomes are formed in the nucleus of a cell when a cell is dividing. It is possible to see chromosomes under an ordinary light microscope if the cell is in the right stage of cell division.

The number of chromosomes varies between species. Humans have 46 chromosomes. Some species can have many more than 100 chromosomes while others can have as little as two.

Genetic inheritance

Inheritance is the backbone of genetics and is an important topic to cover in an introduction to genetics. Long before DNA had been discovered and the word ‘genetics’ had been invented, people were studying the inheritance of traits from one generation to the next.

Genetic inheritance occurs both in sexual reproduction and asexual reproduction . In sexual reproduction, two organisms contribute DNA to produce a new organism. In asexual reproduction, one organism provides all the DNA and produces a clone of themselves. In either, genetic material is passed from one generation to the next.

Experiments performed by a monk named Gregor Mendel provided the foundations of our current understanding of how genetic material is passed from parents to their offspring.

Last edited: 31 August 2020

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This is world’s #1 textbook for beginning biologists and has been hugely valuable to me over the years. This is the resource that I recommend above anything else for aspiring biologists.

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Introduction to Genetics

Natasha Ramroop Singh, Kamloops, British Columbia

Copyright Year: 2009

Publisher: Thompson Rivers University

Language: English

Formats Available

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Learn more about reviews.

Reviewed by James Langeland, Professor, Kalamazoo College on 1/30/23

This text does what it claims to do. It provides an introductory overview of a broad swath of genetics. read more

Comprehensiveness rating: 4 see less

This text does what it claims to do. It provides an introductory overview of a broad swath of genetics.

Content Accuracy rating: 4

No glaring errors. One could always nitpick any text book.

Relevance/Longevity rating: 3

The text is relevant, but not particularly unique in any sense. One could find virtually the same information in any number of genetics textbooks, presented in largely the same way. A major problem here is that the filed is presented more or less historically with many of the experiments and concepts being described having little to no relevance to genetics today. This is a problem with many texts so I do not single this one out.

Clarity rating: 4

As with many open source texts, this one suffers from substandard figures, which directly influences clarity. The words on the age are fine, but the adage is true-a picture can be worth a thousand words. The mainstream publishers spent a lot of money on figures and it shows--they can be really good.

Consistency rating: 4

No comments here.

Modularity rating: 4

There seem to be appropriate and logical chapter and section breaks.

Organization/Structure/Flow rating: 3

The flow is the same as nearly any other genetics textbook. It suffers from a rigid historical framework. Better than most at Muller's morphs however!

Interface rating: 5

No problems here. I do really like the integrated you tube links. I did not dive into the content of those videos (beyond the scope of my review), but the fact that they are there in abundance is a good use of the open source approach.

Grammatical Errors rating: 5

No problems here.

Cultural Relevance rating: 3

No comment.

A very timely section on SARS-Cov-2 at the end! Rich with study questions and answers. Genetics is and should be very problem based, so this is good. I appreciate what is being offered here and I understand the market. There is nothing "wrong" with this textbook. There is also no wow factor that would cause me to adopt it at this time.

Table of Contents

• Chapter 1- Mendel's First Law and Meiosis
• Chapter 2- Mendel's Second Law: Independent Assortment
• Chapter 3- The Cell Cycle and Mitosis
• Chapter 4- Pedigree Analysis
• Chapter 5- The Complementation Test
• Chapter 6- Alleles at a Single Locus
• Chapter 7- The Central Dogma- Mutations and Biochemical Pathways
• Chapter 8- Gene Interactions 
• Chapter 9- Linkage and Recombination Frequency
• Chapter 10- Sex Chromosomes & Sex Linkage
• Chapter 11- Recombination Mapping of Gene Loci
• Chapter 12- Physical Mapping of Chromosomes and Genomes
• Chapter 13- Genes and COVID-19 Susceptibility in Humans 

Ancillary Material

About the book.

Genetics, otherwise known as the Science of Heredity, is the study of biological information, and how this information is stored, replicated, transmitted and used by subsequent generations. The study of genetics can be sub-divided into three main areas: Transmission Genetics, Molecular Genetics, and Population Genetics. In this Introductory text, the focus is on Transmission or Classical Genetics, which deals with the basic principles of heredity and the mechanisms by which traits are passed from one generation to the next. The work of Gregor Mendel is central to Transmission Genetics; as such, there is a discussion about the pioneering work performed by him along with Mendel’s Laws, as they pertain to inheritance. Other aspects of Classical Genetics are covered, including the relationship between chromosomes and heredity, the arrangement of genes on chromosomes, and the physical mapping of genes.

About the Contributors

Natasha Ramroop Singh , Thompson Rivers University

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• Biology Article

Genetics is the branch of biology that deals with the study of heredity and its biological process. It also involves the study of genes, genomes and the cell cycle.

What is Genetics?

Genetics is termed as the study to understand the functioning of inheritance of traits from parents to offspring. The groundwork on which heredity stands is known as inheritance. It is defined as the procedure by which characteristics are handed down from one generation to the other. Gregor Johann Mendel is known as the “Father of Modern Genetics” for his discoveries on the basic principles of heredity .

Variation, as the name suggests is the amount of dissimilarity that exists between children and their parentages. It can be determined to keep in view the behaviouristic, cytological, physiological, and morphological characters of individuals fitting into similar species.

Some of the major reasons that variation are

• Genetic/Chromosomal rearrangement.
• Mutated genes due to the influence of the ecosystem.
• Crossing over.

Let us have a detailed look at genetics notes to learn about genes and the principle of inheritance.

Law of Inheritance by Gregor Mendel

Garden Pea (Pisum Sativum) was the plant that Mendel experimented on for 7 years to get to the point to propose the laws of inheritance in live creatures. Mendel carefully chose seven distinct characteristics of Pisum Sativum for the investigation concerning hybridization. Mendel used true-breeding lines i.e. those that go through constant self-pollination and display steady characteristic inheritance.

Also Read:    Mendel’s Laws of Inheritance

Principles of Inheritance

When Mendel observed the monohybrid cross he proposed two laws of inheritance-

Law of Dominance – Distinct elements termed as factors control the characteristics. These factors at all times exist as a couple. One of the constituent genes of the couple dominates over the former.

Law of Segregation – Alleles don’t blend and the two characteristics are recuperated all through the gamete formation (in the F2 generation). The characters are apart from each other and pass on to diverse gametes. Comparable types of gametes are produced by Homozygous and Heterozygous produces diverse sorts of a gamete with varied characteristics.

Also Refer:  Principles of Heredity

Incomplete Dominance

It is the discovery that was done after Mendel’s work. Incomplete dominance is the situation in which both the alleles do not display a dominant trait resulting in a fine combination or a midway amid the characteristics of the alleles.

Explore more:  Incomplete dominance

Codominance

When two alleles lack the dominant-recessive association and thus the duo affects the creature together.

Law of Independent Assortment

The separation of one set of characteristics is autonomous of the other set of characters when they are pooled in a hybrid.

The Chromosomal Theory of Inheritance

Both genes and chromosomes exist in sets of two. The homologous chromosome contains the two alleles of a gene pair in the homologous sites. The coupling and split of a set of chromosomes will cause a split in the set of genes (factor) they carry. This united knowledge is termed the Chromosomal Theory of Inheritance.

Sex Determination

A particular nuclear arrangement was perceived by Henking. He perceived that this particular nuclear arrangement was found in only fifty per cent of sperms. He termed this body as x. Later, it was observed that the ova which only obtained the X chromosome matured and were born as females and those that didn’t receive only X chromosomes were born as males. Thus, the X- chromosome was termed a sex chromosome and the remaining ones were termed autosomes.

The occurrence due to which a modification in DNA happens and causes a variation in the phenotype and genotype of a creature is termed a Mutation .

Explore more:  Determination Of Sex

Genetic Disorders

Disorders of a Mendelian nature include:

• Haemophilia.
• Sickle Cell Anaemia.
• Phenylketonuria.

Disorders of a chromosomal nature include:

• Down’s syndrome.
• Klinefelter’s Syndrome.
• Turners Syndrome.

Explore more:  Chromosomal Abnormalities

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Who discovered the structure of DNA?

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• National Center for Biotechnology Information - The Structure and Function of DNA
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• Live Science - DNA: Definition, Structure & Discovery
• Genetic Home Reference - What Is DNA?
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What does DNA do?

Deoxyribonucleic acid (DNA) is an organic chemical that contains genetic information and instructions for protein synthesis . It is found in most cells of every organism. DNA is a key part of reproduction in which genetic heredity occurs through the passing down of DNA from parent or parents to offspring.

What is DNA made of?

DNA is made of nucleotides . A nucleotide has two components: a backbone, made from the sugar deoxyribose and phosphate groups, and nitrogenous bases, known as cytosine , thymine , adenine , and guanine . Genetic code is formed through different arrangements of the bases.

The discovery of DNA’s double-helix structure is credited to the researchers James Watson and Francis Crick , who, with fellow researcher Maurice Wilkins , received a Nobel Prize in 1962 for their work. Many believe that Rosalind Franklin should also be given credit, since she made the revolutionary photo of DNA’s double-helix structure, which was used as evidence without her permission.

Can you edit DNA?

Gene editing today is mostly done through a technique called Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), adopted from a bacterial mechanism that can cut out specific sections in DNA. One use of CRISPR is the creation of genetically modified organism (GMO) crops.

What’s the difference between DNA and RNA?

DNA is the master blueprint for life and constitutes the genetic material in all free-living organisms. RNA uses DNA to code for the structure of proteins synthesized in cells . Learn more about the differences between DNA and RNA.

Recent News

DNA , organic chemical of complex molecular structure that is found in all prokaryotic and eukaryotic cells and in many viruses . DNA codes genetic information for the transmission of inherited traits.

A brief treatment of DNA follows. For full treatment, see genetics: DNA and the genetic code .

The chemical DNA was first discovered in 1869, but its role in genetic inheritance was not demonstrated until 1943. In 1953 James Watson and Francis Crick , aided by the work of biophysicists Rosalind Franklin and Maurice Wilkins , determined that the structure of DNA is a double-helix polymer , a spiral consisting of two DNA strands wound around each other. The breakthrough led to significant advances in scientists’ understanding of DNA replication and hereditary control of cellular activities.

Each strand of a DNA molecule is composed of a long chain of monomer nucleotides . The nucleotides of DNA consist of a deoxyribose sugar molecule to which is attached a phosphate group and one of four nitrogenous bases : two purines ( adenine and guanine ) and two pyrimidines ( cytosine and thymine ). The nucleotides are joined together by covalent bonds between the phosphate of one nucleotide and the sugar of the next, forming a phosphate-sugar backbone from which the nitrogenous bases protrude. One strand is held to another by hydrogen bonds between the bases; the sequencing of this bonding is specific—i.e., adenine bonds only with thymine, and cytosine only with guanine.

The configuration of the DNA molecule is highly stable, allowing it to act as a template for the replication of new DNA molecules, as well as for the production ( transcription ) of the related RNA (ribonucleic acid) molecule. A segment of DNA that codes for the cell’s synthesis of a specific protein is called a gene .

DNA replicates by separating into two single strands, each of which serves as a template for a new strand. The new strands are copied by the same principle of hydrogen-bond pairing between bases that exists in the double helix. Two new double-stranded molecules of DNA are produced, each containing one of the original strands and one new strand. This “semiconservative” replication is the key to the stable inheritance of genetic traits.

Within a cell, DNA is organized into dense protein-DNA complexes called chromosomes . In eukaryotes , the chromosomes are located in the nucleus , although DNA also is found in mitochondria and chloroplasts . In prokaryotes , which do not have a membrane-bound nucleus, the DNA is found as a single circular chromosome in the cytoplasm . Some prokaryotes, such as bacteria , and a few eukaryotes have extrachromosomal DNA known as plasmids , which are autonomous , self-replicating genetic material. Plasmids have been used extensively in recombinant DNA technology to study gene expression.

The genetic material of viruses may be single- or double-stranded DNA or RNA. Retroviruses carry their genetic material as single-stranded RNA and produce the enzyme reverse transcriptase , which can generate DNA from the RNA strand. Four-stranded DNA complexes known as G-quadruplexes have been observed in guanine-rich areas of the human genome .

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Essay on DNA: Meaning, Features and Forms | Genetics

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In this article we will discuss about:- 1. Meaning of DNA 2. Features of DNA 3. Molecular Structure 4. Components 5. Forms.

• Essay on the Forms of DNA

Essay # 1. Meaning of DNA:

A nucleic acid that carries the genetic information in the cell and is capable of self-replication and RNA synthesis is referred to as DNA. In other words, DNA refers to the molecules inside cells that carry genetic information and pass it from one generation to the next. The scientific name for DNA is deoxyribonucleic acid.

Essay # 2. Features of DNA:

The main features of DNA are given below:

i. Location:

In eukaryotes, DNA is found both in nucleus and cytoplasm. In the nucleus it is a major component of chromosome, whereas in cytoplasm it is found in mitochondria and chloroplasts. In prokaryotes, it is found in the cytoplasm.

ii. Structure:

Mostly the DNA structure is double stranded in both eukaryotes and prokaryotes. However, in some viruses DNA is single stranded. DNA is a double stranded molecule held together by weak bonds between base pairs of nucleotides. The four nucleotides in DNA contain the bases: adenine (A), guanine (G), cytosine (C), and thymine (T).

iii. Shape:

In eukaryotes, the DNA is of linear shape. In prokaryotes and mitochondria; the DNA is circular.

iv. Replication:

The DNA is capable of self-replication. This is the only chemical which has self-replicating capacity. The DNA replicates in semi-conservative manner. In eukaryotes, DNA replicates during the S-Phase of the cell cycle.

There are different forms of DNA such as A, B, C, D and Z DNA. The DNA may be linear or circular. It may be double stranded or single stranded. It may be right handed or left handed. The DNA may be repetitive or unique. It may be nuclear or cytoplasmic.

vi. Functions:

DNA plays important role in various ways. It is used in transcription i.e. synthesis of mRNA which in turn is used in protein synthesis. It carries genetic information from one generation to the next generation. DNA stores and transmits the genetic information in cells.

It forms the basis for genetic code. The genes are made of DNA and are responsible for passing on traits from generation to generation. DNA contains the genetic instructions for the development and functioning of living organisms. Thus it is the substance of heredity.

Essay # 3. Molecular Structure of DNA :

The double helical structure of DNA was identified by Watson and Crick in 1953. This brilliant research work resulted in significant breakthrough in understanding the gene function. This structure has been verified in many different ways and is universally accepted. James Watson and Francis Crick were awarded Nobel prize in 1958 for this significant contribution in the field of molecular biology.

The important features of their model of DNA are as follows:

1. Two helical polynucleotide chains are coiled around a common axis, the chains run in opposite directions.

2. The purine and pyrimidine bases are on the inside of the helix whereas the phosphate and deoxyribose units are situated on the outside. Also the planes of the base residues are perpendicular to the helix axis. While the planes of the sugar residues are almost at right angles to those of the bases.

3. The diameter of the helix is 2 nm. Adjacent bases are separated by 0.34 nm along the helix axis. Hence the helix repeats itself every 10 residues on each chain at intervals of 3.4 nm.

4. The two chains are held together by hydrogen bonds formed between pairs of bases. Pairing is highly specific. Adenine pairs with thymine, guanine always pairs with cytosine. A = T, G = C.

5. The sequence of bases along the polynucleotide chain is not restricted. The precise sequence of bases carries the genetic information.

6. The sugar-phosphate backbones of the two DNA strands wind around the helix axis like the railing of a spiral staircase.

7. The bases of the individual nucleotides are on the inside of the helix, stacked on top of each other like the steps of a spiral staircase.

Essay # 4. Components of DNA :

DNA molecule is a polymer which is composed of several thousand pairs of nucleotide monomers. Union of several nucleotides together leads to the formation of polynucleotide chain. The monomer units of DNA are nucleotides, and the polymer is known as a “polynucleotide.” Each nucleotide consists of a 5-carbon sugar (deoxyribose), a nitrogen containing base attached to the sugar, and a phosphate group.

There are three components of DNA, viz:

(1) Nitrogenous bases,

(2) Deoxyribose sugar, and

(3) Phosphate group.

These are briefly discussed below.

i. Nitrogenous Bases :

Nucleotides are also known as nitrogenous bases or DNA bases. Nitrogenous base are of two types, viz. pyrimidines and purines.

Pyrimidines:

Main features of pyrimidines are given below:

(i) These are single ring structures,

(ii) These are of two types namely cytosine and thymine,

(iii) They occupy less space in DNA structure,

(iv) Pyrimidine is linked with deoxyribose sugar at position 3.

Main features or purines are given below:

(i) They are double ring compounds.

(ii) They are of two types, viz. adenine and guanine.

(iii) They occupy more space in DNA structure.

(iv) Deoxyribose sugar is linked at position 9 of purine.

Thus, in DNA there are four different types of nitrogenous bases, viz. adenine (A), guanine (G), cytosine (C) and thymine (T). In RNA, the pyrimidine base thymine is replace by uracil.

Base Pairing:

The purine and pyrimidine bases always pair in a definite fashion. Adenine will always pair with thymine and guanine with cytosine. Adenine and thymine are joined by double hydrogen bonds while guanine and cytosine are joined by triple hydrogen bonds. However, these bonds are weak which help in separation of DNA strands during replication.

ii. Deoxyribose Sugar :

This is a pentose sugar having five carbon atoms. The four carbon atoms are inside the ring and the fifth one is with CH 2 group. This has three OH groups on 1, 3 and 5 carbon positions. Hydrogen atoms are attached to carbon atoms one to four. In RNA, the sugar ribose is similar to deoxyribose except that it has OH group on carbon atom 2 instead of H group.

iii. Phosphate :

The phosphate molecule is arranged in an alternate manner to deoxyribose molecule. Thus there is deoxyribose on both sides of phosphate. The phosphate is joined with carbon atom 3 of deoxyribose at one side and with carbon atom 5 of deoxyribose on the other side.

Nucleosides and Nucleotides :

A combination of deoxyribose sugar and nitrogenous base is known as nucleoside and a combination of nucleoside and phosphate is called nucleotide.

Nucleoside = Deoxyribose Sugar + Nitrogenous base

Nucleotide = Deoxyribose + Nitrogenous base + Phosphate

Thus, a nucleotide is a nucleoside with one or more phosphate groups covalently attached to it. Nucleosides differ from nucleotides in that they lack phosphate groups. The four different nucleosides of DNA are deoxyadenosine (dA), deoxyguanosine (dG), deoxycytosine (dC), and deoxythymidine (dT).

DNA Backbone :

The DNA backbone is a polymer with an alternating sugar-phosphate sequence. The deoxyribose sugars are joined at both the 3′-hydroxyl and 5′-hydroxyl groups to phosphate groups in ester links, also known as “phosphodiester” bonds.

Essay # 5. Forms of DNA :

Depending upon the nucleotide base per turn of the helix, pitch of the helix, tilt of the base pair and humidity of the sample, the DNA can be observed in four different forms namely, A, B, C and D. The comparison of A, B and Z forms of DNA is presented in Table 15.1.

i. B-form :

This is the same form of DNA proposed by Watson and Crick.

Main features of B form of DNA are given below:

1. This is the most common form of DNA.

2. It is observed when humidity is 92% and salt concentration is high.

3. The coiling is in the right direction.

4. The number of base is 10 per turn of helix.

5. The pitch is 3.4 nm.

6. The sugar phosphate linkage is normal.

7. The helix is narrower and more elongated than A form.

8. The major groove is wide which is easily accessible to proteins.

9. The minor groove is narrow.

10. The conformation is favored at high water concentrations.

11. The base pairing is nearly perpendicular to helix axis.

12. The sugar puckering is C2′-endo.

ii. A-form :

1. This form is observed when the humidity of the sample is 75%.

2. The coiling is in the right direction.

3. The number of bases is 10.7 per turn of helix.

4. The pitch is 2.8 nm.

5. The sugar phosphate linkage is normal.

6. The major groove is deep and narrow which is not easily accessible to proteins.

7. The minor groove is wide and shallow which is accessible to proteins, but information content is lower than major groove.

8. The helix is shorter and wider than B form.

9. The conformation is favored at low water concentrations.

10. The base pairs are tilted to helix axis.

11. The sugar puckering is C3′-endo.

iii. Z-form :

1. The helix has left-handed coiling pattern.

2. The number of bases is 12 per turn of helix.

3. The pitch is 4.5 nm.

4. The sugar phosphate linkage is zigzag.

5. The major “groove” is not really a groove.

6. The minor groove is narrow.

7. The helix is narrower and more elongated than A or B form.

8. The base pairing is nearly perpendicular to helix axis.

9. The conformation is favored by high salt concentrations.

10. The sugar puckering is C: C2 endo, G : C2 exo.

Related Articles:

• Structural Features of DNA | Genetics
• Watson-Crick Model of DNA| Genetics

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Nature vs. Nurture Essay

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Nature is the influence of genetics or hereditary factors in determining the individual’s behavior. In other words, it is how natural factors shape the behavior or personality of an individual. In most cases, nature determines the physical characteristics which in effect influence the behavior of an individual. Physical characteristics such as physical appearance, type of voice and sex which are determined by hereditary factors influences the way people behave.

Nurture on the other is the upbringing of an individual according to the environmental conditions. That is, the way individuals are socialized. Basically, nurture is the influence of environmental factors on an individual’s behavior.

According to this paradigm, an individual’s behavior can be conditioned depending on the way one would like it to be. Often, individuals’ behaviors are conditioned by the socio-cultural environmental factors. It is because of socio-cultural environmental conditions that the differences in the behavior of individuals occur.

Nature determines individual traits that are hereditary. In other words, human characteristics are determined by genetic predispositions which are largely natural. Hereditary traits are normally being passed from the parents to the offspring. They include characteristics that determine sex and physical make up. According to natural behaviorists, it is the genes that will determine the physical trait an individual will have. These are encoded on the individuals DNA.

Therefore, behavioral traits such as sexual orientation, aggression, personality and intelligence are also encoded in the DNA. However, scientists believe that these characteristics are evolutionary. That is, they change over time depending on the physical environment adaptability. Evolutionary scientists argue that changes in genes are as a result of mutations which are caused by environmental factors. Thus, natural environment determines individual characteristics which are genetically encoded in the DNA.

Conversely, individuals possess traits that are not naturally determined. These are characteristics that are learnt rather than being born with. These are traits which largely determined by the socio-cultural environmental factors or the way the individuals are socialized within the society depending on the societal values.

These traits are learnt as an individual develops and can easily be changed by the socio-cultural environment where the individual is currently staying. These characteristics include temperament, ability to master a language and sense of humor. Behavioral theorists believe that these traits can be conditioned and altered much like the way animal behavior can be conditioned.

From the discussion it can be deduced that individuals’ traits are determined by hereditary genes and at the same time can be natured. There are those traits that cannot be changed in an individual no matter what condition the person is exposed to. These traits are inborn and embed within the individual hereditary factors.

In most cases, they constitute the physical characteristics of an individual. They also determine the physical behaviors such as walking style, physical appearance and eating habits. At the same time there are learned characteristics which are normally being conditioned by the socio-cultural values. Individuals learn these traits from the way they are socialized within the immediate social or cultural environment. In other words, such behaviors are conditioned by the cultural values encouraged by the immediate society.

In conclusion, nature vs. nurture debate still remains controversial. However, all agree that nature and nurture play a crucial role in determining an individual’s behavior. Nature is associated with heredity roles in determining the individuals characteristics where as nurture is associated with the role of socio-cultural environment in determining the individuals behavior.

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IvyPanda. (2018, November 6). Nature vs. Nurture. https://ivypanda.com/essays/nature-vs-nurture/

"Nature vs. Nurture." IvyPanda , 6 Nov. 2018, ivypanda.com/essays/nature-vs-nurture/.

IvyPanda . (2018) 'Nature vs. Nurture'. 6 November.

IvyPanda . 2018. "Nature vs. Nurture." November 6, 2018. https://ivypanda.com/essays/nature-vs-nurture/.

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Introduction to Gene Ontology

Gene ontology is an expanding knowledgebase that provides scientists with universal definitions to describe the functions of genes and their products. .

Uzma Rentia is a third-year medical student at George Washington University. She has a background in bioinformatics techniques and has published work on ovarian cancer epigenetics, researched CAR T-cell therapies...

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This article was review by Paul Sternberg , PhD from the California Institute of Technology and the Gene Ontology Consortium (GOC).

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What Is Gene Ontology?

The Gene Ontology (GO) database is an effort to create an explicitly defined dictionary to describe the roles of gene products and their relationships to each other. 1,2   “It is essentially a vocabulary that allows scientists or biologists across all domains of life to describe what they are seeing… Every term is defined and has a relation to other terms,” said Paul Sternberg , a molecular biologist and geneticist at the California Institute of Technology and a principal investigator with the Gene Ontology Consortium (GOC) . 3 The GOC consists of a group of scientists dedicated to developing and maintaining the GO. Through the GOC-run GO, all biologists have a universal vocabulary to discuss the function of genes, even if they are working with vastly different species.

What Information Is Included in an Individual Ontology?  

The most basic unit of a specific ontology is a “ term ,” which refers to an attribute used to describe a specific gene function or product. 3 GO terms are themselves composed of several core elements.

• An alphanumeric ID, which serves as a unique identifier
• A human-readable name
• The domain to which it belongs (molecular function, cellular component, or biological process)
• A definition of the function along with a cited source
• Its relationship to other terms in the ontology

All of the information held in a GO about a certain term falls under one of three broader biological domains . 4

• Molecular Function: Activities conducted by gene products at the molecular level.
• Cellular Component: The cellular entity in which the gene product carries out its function, or the larger complex the gene product is a part of.
• Biological Process: The overarching process to which the gene product contributes.

Specific ontologies are loosely hierarchal structures that graph the relationships between these terms. They are represented using directed acyclic graphs (DAGs), a system in which general “parent” terms give rise to more specific “child” terms, like a flowchart. The terms are called “nodes,” and their relationships are described using “edges.” Common edge phrases include the following.

• Is_a: This indicates that a “child” node is a subclass of a “parent” node. For example: the endoplasmic reticulum is an  organelle.
• Part_of: This indicates a “child” node is a component of a “parent” node. In these designations, the “child” cannot exist without the “parent.” For example, the endoplasmic reticulum is part of the cell.
• Regulates: This indicates that one process directly modifies another.   For example, certain terms may regulate transcription or regulate apoptosis. Edges that specify positive or negative regulations also exist.
• Has_part: This indicates that a “parent” node absolutely requires the “child” node to exist or function. For example, the electron transport chain has part Coenzyme Q (CoQ). 

Gene Ontology annotations 

Another vital aspect of GO are annotation s, which establish a connection between a GO term (a function) and the associated gene or gene product it describes. 5 In simpler terms, GO annotations utilize GO terms to attribute a function to a gene or gene product. For instance, tumor protein 53 ( TP53 ), breast cancer gene 1 ( BRCA1 ), ABL proto-oncogene 1 ( ABL1 ), and vascular endothelial growth factor ( VEGF ) are all genes or gene products. The GO terms included in their respective annotations could encompass DNA-binding transcription factor activity (GO:0003700), DNA repair (GO:0006281), protein tyrosine kinase activity (GO:0004713, and positive regulation of angiogenesis (GO:0045766). To ensure accuracy, annotations must reference published scientific literature that backs the association between a GO term and a gene or gene product. 

Gene Ontology Knowledgebase 

Altogether, the standard ontologies, GO-CAM, and annotations make up the GO Knowledgebase. As of April 2024, the GO Knowledgebase contained 42,255 GO terms, 7,671,375 annotations, 1,536,921 gene products, and 5,404 species. 7 Included species range from model organisms such as Schizosaccharomyces pombe (fission yeast), Danio rerio (zebrafish), and Saccharomyces cerevisiae (brewer's or baker's yeast) to lesser-known organisms such as Gallus gallus (red junglefowl) and Dictyostelium discoideum (an amoeba).

The enormous range of terms, annotations, gene products, and represented species illustrates the significant expansion GO has undergone since its creation in 1998 . 1 It continues to grow to this day; ontologies are considered “dynamic” in that they grow and shift as scientific knowledge expands. Members of the GO Consortium collaborate with genomic databases, such as UniProt, MGI, and Reactome, to constantly update, review, and revise the information included in the Knowledgebase. The Consortium also welcomes community feedback for revisions and submissions for new annotations.

Using GO for research

Scientists mostly use GO to analyze the results of high-throughput data, such as next-generation sequencing and microarray results. Most often, GO is used in descriptive “-omics” papers that compare the genomic, proteomic, or transcriptomic differences between two cell types or conditions. Most of these experiments will yield thousands upon thousands of genes, making it unreasonable to sift through individual genes and provide a function for each.  In that case, analysis tools are employed to initially narrow down the results to a subset of critical genes exhibiting significant differential expression. Subsequently, researchers utilize GO-specific programs to infer conclusions regarding represented pathways and processes. Two foundational GO analyses are as follows.

• Over Representation Analysis (ORA) / Enrichment Analysis : 8 This is one of the most common and simple GO analyses conducted. This analysis assigns inputted genes of interest to pathways and calculates whether each pathway is more or less represented than would be expected. This first requires placing differential expression results in the context of the original raw inputs. For example, say an analysis started with 1000 genes, 100 of which were related to apoptosis. If 100 significantly differentially expressed genes were identified, 10 would be expected to be related to apoptosis. A deviation from that expected number would be flagged, but it could also happen purely by chance. To determine which over- or under-representations are truly statistically significant, GO tools use statistical models such as hypergeometric distribution, Fisher’s exact test, or chi-square test to calculate a p-value. 
• Functional Class Scoring (FCS) : 9 Functional class scoring refers to a class of methodologies, but the most well-known one is gene set enrichment analysis (GSEA). This approach takes ORA analysis further by ranking genes based on their differential expression value and direction of change. Next, an enrichment score for various pathways is determined based on where their associated genes fall on the ranked list. Unlike ORA, GSEA is largely unbiased because it takes all genes as inputs, not just those that were significantly differentially expressed. 

Commonly Used Tools for Gene Ontology Analysis 

There are a variety of Web-, R-, Python-, and even Java-based tools to conduct ORA, FCS, and other analyses. Below is a table describing some of the commonly used tools in GO analysis, though is by no means an exhaustive list

Table: Commonly-used GO analysis tools 

Drawbacks and GO Research Pitfalls

One of the drawbacks of GO is that it is incomplete. “If you ask most experts to look at the Gene Ontology and the relationship with the terms and see if their domain is covered [they will say] it is not covered that well,” said Sternberg. Despite the considerable effort put into integrating new literature into the GO, there is always more ground to cover. Consequently, the biological snapshot provided by GO annotations and GO-CAMs likely only represents part of the story. Researchers still need to conduct their investigations and utilize their knowledge base when interpreting the results of a GO analysis. Additionally, researchers must also consider what a GO analysis might be leaving out. A single gene that is differentially expressed and responsible for a vital function may be overlooked in a GO analysis if the larger gene set it belongs to is not enriched. Conversely, a pathway may be enriched in a GO analysis, but that does not guarantee that it is contributing to a significant or interesting function. 

While GO serves as a valuable tool, it should not stop researchers from applying their expertise to interpret their data and results. Additionally, encouraging investigators to contribute to GO annotations themselves enhances the quality and comprehensiveness of the GO database. Sharing knowledge and contributing to a community resource are critical aspects of promoting the utility of GO.

1. Ashburner M, et al. Gene Ontology: tool for the unification of biology . Nat Genet . 2000;25(1):25-29.

2. Aleksander SA, et al. The Gene Ontology knowledgebase in 2023 . Genetics . 2023;224(1):iyad031.

3. About the GO . Gene Ontology. Published May 30, 2024. Accessed June 22, 2024. http://geneontology.org/docs/introduction-to-go

4. GO term elements . Gene Ontology Resource. Published May 30, 2024. Accessed June 12, 2024. http://geneontology.org/docs/GO-term-elements

5. Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource . Nucleic Acids Res . 2004;32(suppl_1):D258-D261. 

6. Hill DP, et al. Gene Ontology annotations: what they mean and where they come from . BMC Bioinformatics . 2008;9(5):S2.

7. Thomas PD, et al. Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems . Nat Genet . 2019;51(10):1429-1433.

8. Gene Ontology Resource . Gene Ontology Resource. Accessed June 12, 2024. http://geneontology.org/

9. Reimand J, et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap . Nat Protoc . 2019;14(2):482-517.

10. Pathways and gene sets: What is functional enrichment analysis? NIH Center for Cancer Research. Accessed May 28, 2024. https://bioinformatics.ccr.cancer.gov/btep/pathways-and-gene-sets-what-is-functional-enrichment-analysis/

11. Yu G, et al. clusterProfiler: an R package for comparing biological themes among gene clusters . OMICS J Integr Biol . 2012;16(5):284-287.

12. Reimand J, et al. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments . Nucleic Acids Res . 2007;35(suppl_2):W193-W200.

13. Chen EY, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics . 2013;14:128.

14. Huang DW, et al. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources . Nat Protoc . 2009;4(1):44-57.

15. Thomas PD, et al. PANTHER: A library of protein families and subfamilies indexed by function . Genome Res . 2003;13(9):2129-2141.

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National Academies of Sciences, Engineering, and Medicine; National Academy of Medicine; National Academy of Sciences; Committee on Human Gene Editing: Scientific, Medical, and Ethical Considerations. Human Genome Editing: Science, Ethics, and Governance. Washington (DC): National Academies Press (US); 2017 Feb 14.

Human Genome Editing: Science, Ethics, and Governance.

• Hardcopy Version at National Academies Press

1 Introduction

Genome editing 1 is a powerful new tool for making precise additions, deletions, and alterations to the genome—an organism's complete set of genetic material. The development of new approaches—involving the use of meganucleases; zinc finger nucleases (ZFNs); transcription activator-like effector nucleases (TALENs); and, most recently, the CRISPR/Cas9 system—has made editing of the genome much more precise, efficient, flexible, and less expensive relative to previous strategies. With these advances has come an explosion of interest in the possible applications of genome editing, both in conducting fundamental research and potentially in promoting human health through the treatment or prevention of disease and disability. The latter possibilities range from editing somatic cells to restore normal function in diseased organs to editing the human germline to prevent genetic diseases in future children and their descendants.

As with other medical advances, each application comes with its own set of benefits, risks, regulatory questions, ethical issues, and societal implications. Important questions raised with respect to genome editing include how to balance potential benefits against the risk of unintended harms; how to govern the use of these technologies; how to incorporate societal values into salient clinical and policy considerations; and how to respect the inevitable differences, rooted in national cultures, that will shape perspectives on whether and how to use these technologies.

Recognizing both the promise and concerns related to human genome editing, the National Academy of Sciences (NAS) and the National Academy of Medicine (NAM) 2 convened the Committee on Human Gene Editing: Scientific, Medical, and Ethical Considerations to carry out the study that is documented in this report. While genome editing has potential applications for use in agriculture and nonhuman animals, 3 this committee's task (see Box 1-1 ) was focused on human applications. 4 The charge to the committee included elements pertaining to the state of the science in genome editing, possible clinical applications of these technologies, potential risks and benefits, whether standards can be established for quantifying unintended effects, whether current regulatory frameworks provide adequate oversight, and what overarching principles should guide the regulation of genome editing in humans.

Statement of Task.

• STUDY CONTEXT

The NAS and the NAM Human Gene-Editing Initiative

In light of the promise of genome editing and the associated regulatory and ethical issues, the NAS and the NAM established an initiative to explore these issues in greater depth and facilitate U.S. and international dialogue on how to address them. The first activity of this Human Gene-Editing Initiative was the convening of the International Summit on Human Gene Editing: A Global Discussion jointly with the Chinese Academy of Sciences and The Royal Society of the United Kingdom. This 3-day event addressed a number of scientific advances in the development of modern genome-editing tools, potential medical uses of these tools in human patients, and ethical and social issues their uses might pose. The organizing committee released a statement that summarized its conclusions from the meeting ( NASEM, 2016d ). Panel chair David Baltimore also noted “we hope that our discussion here will serve as a foundation for a meaningful and ongoing global dialogue” ( NASEM, 2016d , p. 6). All three nations embraced the statement's call for continued research on gene editing, further deliberation with regard to heritable changes, and a continued public discourse on the topic. 5 The summit provided important input to the present study, as did other studies by the NAS and the NAM on related topics (see Box 1-2 ).

Related Studies of the NAS and the NAM.

This committee was convened to continue the dialogue initiated by the International Summit and to undertake a year-long, in-depth consensus study. As specified in its statement of task (see Box 1-1 ), the committee examined the state of the science in human genome editing, its potential applications, and the ethical issues that need to be considered in deciding how to govern the use of these powerful new tools. This report is the product of that study and, as with all other National Academies consensus studies, underwent peer review by an independent panel of experts. Additional activities of the Chinese Academy of Sciences and The Royal Society of the United Kingdom are anticipated, including another international summit to take place in China in 2018.

U.S. and International Policy Discussions

Among the earliest calls for a detailed examination of the implications of genome-editing technologies were those made by members of the scientific community engaged in developing these tools and advancing their clinical applications. In 2015 a group of investigators and ethicists, including CRISPR/Cas9 developers, met in Napa, California, and subsequently published a request for the community to explore the nature of human genome editing and provide guidance on its acceptable uses ( Baltimore et al., 2015 ). That same year, a number of articles and commentaries appearing in scientific journals and the popular press called attention to scientific and ethical challenges that would be posed by CRISPR/Cas9 and similar genetic tools ( Bosley et al., 2015 ; Editing Humanity, 2015 ; Lanphier et al., 2015 ; Maxmen, 2015 ; Specter, 2015 ).

Professional bodies, international organizations, and national academies of sciences and medicine further raised the profile of genome editing by issuing statements on its appropriate uses, particularly in reference to the potential for creating heritable genetic modifications. Among others, they included the U.K. Academy of Medical Sciences and a number of collaborative partners; the European Group on Ethics in Science and New Technologies, an advisory body to the president of the European Commission; the Council of Europe; and the International Society for Stem Cell Research ( AMS et al., 2015 ; Council of Europe, 2015 ; EGE, 2016 ; Friedmann et al., 2015 ; Hinxton Group, 2015 ; ISSCR, 2015 ). The United Nations Educational, Scientific, and Cultural Organization (UNESCO) (2015) issued updated guidance to reflect genome-editing advances. Others launched activities to examine the implications of genome editing in greater detail, including the Académie Nationale de Médecine (France) ( ANM, 2016 ); Institut Nationale de la Santé et de la Récherche Médicale (France) (INSERM; Hirsch et al., 2017 ); Berlin-Brandenburg Academy of Sciences and Humanities ( BBAW, 2015 ); National Academy of Sciences Leopoldina, in partnership with the Deutsche Akademie der Technikwissenshaften (National Academy of Science and Engineering: “acatech”), Deutsche Forschungsgemeinschaft (German Research Foundation: DFG), and Union der deutschen Akademien der Wissenschaften (Union of German Academies of Sciences and Humanities: Academien Union) ( National Academy of Sciences Leopoldina et al., 2015 ); Federation of European Academies of Medicine ( FEAM, UKAMS, and ANM, 2017 ); Royal Netherlands Academy of Arts and Sciences ( KNAW, 2016 ); Nuffield Council on Bioethics ( Nuffield Council, 2016b ); and others (see Box 1-3 ).

Excerpts from Selected Calls Around the World for Continued Study and Public Discussion.

The Technologies

New or improved tools facilitate scientific progress by making it possible to investigate new kinds of questions and to generate new solutions. In the area of health and medicine, scientists and clinicians have long sought to apply the techniques of molecular biology to understand basic biology—including embryonic development, physiology, and the immune and nervous systems—and to treat or prevent disease. Much progress has been made in elucidating the role of genetics in diseases, ranging from sickle-cell anemia, muscular dystrophy, and cystic fibrosis, to such conditions as deafness, short stature, and blindness. The development of many such diseases and conditions has a genetic component. Some result from straightforward single-gene changes, but most involve a complex interplay of genetic, environmental, and other factors that remain only imperfectly understood. Furthermore, genetic sequences themselves paint only part of the biological picture. Regulation of how and when genes are turned on and off, including the role of the epigenome, 6 continues to be actively explored. Controlled gene expression and epigenetic alterations influence how tissues develop and differentiate and have clinical ramifications in such areas as cancer and embryonic development.

Tools that enable investigators to alter DNA sequences in order to understand or improve their function are not new. Recent years, however, have seen the development of a suite of genome-editing tools that allow for easier, better controlled, and more accurate changes to DNA inside cells. These tools are based on exogenous enzymes that cut DNA at specific locations, combined with endogenous processes that repair the broken DNA, thereby enabling letters of the genetic code to be added, modified, or deleted. The speed with which this technology has been adopted in research laboratories and further adapted to tackle additional scientific challenges is a reflection of how powerful a technique the editing of genes and genomes will be for the scientific and clinical communities.

The earliest applications of nuclease-based genome-editing methods employed targeted recognition of specific DNA sequences by proteins: homing nucleases (also known as meganucleases), ZFNs, and TALENs. However, the recent development of RNA-based targeting has greatly simplified the process of genome editing. The first publications on the subject, in 2012-2013, explained how the CRISPR/Cas9 system, derived from a natural bacterial defense mechanism against infecting viruses, can be harnessed to make controlled genetic changes in any DNA, including that of human cells ( Cho et al., 2013 ; Cong et al., 2013 ; Jinek et al., 2012 , 2013 ; Mali et al., 2013 ). This was a game-changing advance. These methods have rapidly been adopted by scientists worldwide and have greatly accelerated fundamental research that has included altering cells in the laboratory to study the functions of particular genes, developing models for studies of human diseases using stem cells or laboratory animals, creating modified plants and animals to improve food production, and developing therapeutic uses in humans. Genome editing has rapidly become an invaluable core technology in research laboratories and biotechnology companies, and is already moving into clinical trials (e.g., Cyranoski, 2016 ; Reardon, 2016 ; Urnov et al., 2010 ).

Individual-Level Concerns

As with other types of medical interventions, whether genome editing can be used in patients will depend largely on understanding the safety and efficacy of the treatment and evaluating whether the anticipated benefits are reasonable with respect to the risk of adverse effects. Treatments based on genome editing are intended to make controlled modifications to specific portions of the DNA that affect the functions of their target(s) while avoiding changes to other portions whose alteration is not desired. The latter alterations, referred to as off-target events, could have consequences, many unnoticeable but others damaging, depending on their location and their effects. In general, human genome editing raises questions common to the process of researching and developing new treatments: which conditions or diseases are most suitable to address with these technologies, how to identify and evaluate off-target events and other potential side effects, and which patients are most appropriate for studies. As described in this report, regulatory systems for addressing the individual-level concerns associated with genome editing already exist in the United States and many other countries, but can be improved.

Societal-Level Concerns

The use of genome editing also has significant social dimensions that vary depending on the proposed application. The use of a genome-editing treatment whose effects are nonheritable and are restricted to an individual patient may not differ greatly from the use of a traditional drug or medical device. By contrast, making changes that may be inherited by future generations raises questions about the extent to which the long-term effects of proposed edits can be predicted and whether it is appropriate for humans to purposely alter any aspect of their genetic future ( Frankel and Chapman, 2000 ; Juengst, 1991 ; Parens, 1995 ). In addition, identifying the increased range of applications made possible by genome editing may be yet another challenge to conventional conceptions of what constitutes a disease or disability. Societal-level concerns are particularly acute with respect to genome-editing interventions aimed at enhancing human capabilities. Such applications also raise questions about how to define and promote fairness and equity ( President's Council on Bioethics, 2003 ). Moreover, as with other genetic technologies, such genome-editing applications may raise concerns about coercive and abusive eugenics programs of the past, which were based on faulty science and served discriminatory political goals ( Wailoo et al., 2012 ).

Looking Beyond Safety and Efficacy

Although the nature of the debate surrounding genome editing is not new, the tools available in the past for making genetic modifications in human cells were time-consuming, difficult, and expensive, and were unlikely to be used outside of specialized medical applications. Recent genome-editing technologies, particularly the CRISPR/Cas9 system, have greatly expanded the landscape of potential applications and potential users. Their rapid development and adoption also have shortened the timeline for discussion of what appropriate governance structures need to be identified or developed. As the safety and efficacy of these technologies continue to improve, the critical question will become not whether scientists and clinicians can use genome editing to make a certain change, but whether they should. There is already discussion of do-it-yourself (DIY) editing and the use of genome-editing tools by the biohacker and DIY biology communities, albeit in nonhuman organisms ( Brown, 2016 ; Ledford, 2015 ). Thorny issues around acceptable uses of the technology in humans will depend on more than scientific considerations, and may increasingly involve weighing factors beyond individual-level risks and benefits ( NRC, 1996 ).

Layered on the scientific and ethical issues associated with human genome editing is the question of how to govern its application so as to facilitate its appropriate use and avoid its misuse. Determining the limits of the technologies' uses and the regulatory mechanisms needed to enforce these limits will vary according to each nation's cultural, political, and legal context. But whether and how best to move human genome editing forward has implications for transnational scientific cooperation that require ongoing public discussion and input into policy making. There is ample precedent for scientists and other stakeholders to engage in just such activities, and this report is intended to build on points raised by a number of international conventions and declarations, such as the Oviedo Convention (1997), the International Declaration on Human Genetic Data (2003), and the Universal Declaration on Bioethics and Human Rights (2005) ( Andorno, 2005a , b ; UNESCO, 2004a , 2005 ).

• STUDY APPROACH

To address its complex task (see Box 1-1 ), the committee included members with expertise in basic and clinical research, in the development of human genetic therapies, and in U.S. and international legal and regulatory frameworks. It included biologists, bioethicists, and social scientists, and incorporated perspectives from potentially affected patient and stakeholder communities. Because the ethical and social issues posed by human genome editing transcend national boundaries, the committee included not only U.S. members but also those who are citizens of or are currently working in Canada, China, Egypt, France, Germany, Israel, Italy, Spain, and the United Kingdom. Brief biographies of the committee members are found in Appendix D .

This study was informed not only by the International Summit described earlier, which immediately preceded the committee's first meeting, but also by review of the salient literature, additional meetings, and speakers who generously shared their knowledge with the committee. Further information on the process by which the committee conducted this study is provided in Appendix C .

In evaluating the implications of new genome-editing tools, the committee also reviewed scientific progress, ethical debates, and regulatory structures related to the use in humans of medical developments such as assisted reproductive technologies, stem cell therapies, gene transfer, and mitochondrial replacement techniques. These developments interface with those of genome editing because editing of stem cells has potential clinical applications for treating or preventing disease, and reproductive technologies would have to be used in combination with genome editing for any heritable application of the latter technologies. As these other technologies have advanced, legal and regulatory frameworks and ethical norms of conduct have been developed to provide guidance on their appropriate human uses and oversight ( Health Canada, 2016 ; HFEA, 2014 ; IOM, 2005 ; NASEM, 2016e ; NRC and IOM, 2007 , 2008 ; Nuffield Council, 2016a ; Präg and Mills, 2015 ; Qiao and Feng, 2014 ). The reports cited here helped provide a basis for the committee's assessment of the use of genome-editing tools in humans and are referenced in subsequent chapters where relevant.

• ORGANIZATION OF THE REPORT

The report begins by reviewing international norms that are embodied in the set of overarching principles adopted by the committee for governance of human genome editing ( Chapter 2 ). The chapter continues with an overview of the U.S. regulation of research and clinical application of genome editing, drawing comparisons where appropriate to other national systems of oversight.

With this grounding in principles and regulation, Chapters 3 - 6 delve into human genome-editing technology and the scientific issues, regulatory context, and ethical implications of four specific applications. Laboratory research conducted in somatic cells and nonheritable laboratory research in human germ cells, gametes, or early-stage embryos is covered in Chapter 3 . Chapter 4 examines the uses of genome editing for somatic interventions focused on therapy, including fetal therapy. Chapter 5 addresses the use of genome-editing technology in germline cells for potential research and clinical therapeutic applications in human patients. Chapter 6 considers the potential use of human genome editing to enhance human functions rather than to treat or prevent disease or disability.

The subsequent chapter ( Chapter 7 ) turns from analysis of these categories of application to the role of public input in determining how genome-editing technology should be governed in the future, both in the United States and in other countries. The chapter considers public engagement for different categories of genome-editing applications and explores strengths and limitations of potential models for undertaking such public engagement.

Finally, Chapter 8 returns to the set of overarching principles and the responsibilities that flow from them in the context of human genome editing. The chapter pulls together the report's conclusions and recommendations in light of these fundamental concepts.

The term “genome editing” is used throughout this report to refer to the processes by which the genome sequence is changed by adding, replacing, or removing DNA base pairs. This term is used in lieu of “gene editing” because it is more accurate, as the editing could be targeted to sequences that are not part of genes themselves, such as areas that regulate gene expression.

The NAS and the NAM are referred to throughout this report simply as the National Academies, or the U.S. National Academies when discussed in relation to the academies of other nations. Until 2016, the NAM was known as the Institute of Medicine (IOM).

In January 2017, the U.S. Food and Drug Administration (FDA) issued revised draft guidance addressing the regulatory pathway for intentionally altered genomic DNA in plants and nonhuman animals. This would include DNA intentionally altered through genome editing. The guidance does not affect the regulatory pathway for human applications that are regulated as human drugs, devices, and biologics. See FDA “Regulation of Intentionally Altered Genomic DNA in Animals—Draft Guidance” (January 2017) at http://www ​.fda.gov/downloads ​/AnimalVeterinary ​/GuidanceComplianceEnforcement ​/GuidanceforIndustry ​/ucm113903.pdf (accessed January 30, 2017) and “Genome Editing in New Plant Varieties Used for Foods; Request for Comments” at https://www ​.regulations ​.gov/document?D=FDA-2016-N-4389-0001 (accessed January 30, 2017).

The regulatory roles of the federal departments and agencies and the overall framework for regulation of applications of biotechnology are outlined in “Modernizing the Regulatory System for Biotechnology Products: Final Version of the 2017 Update to the Coordinated Framework for the Regulation of Biotechnology” (January 4, 2017) and “National Strategy for Modernizing the Regulatory System for Biotechnology Products” (September 2016) ( https: ​//obamawhitehouse ​.archives.gov/blog ​/2017/01/04/increasing-transparency-coordination-and-predictability-biotechnology-regulatory [accessed January 30, 2017]).

Statement by Ralph J. Cicerone, President, U.S. National Academy of Sciences; Victor J. Dzau, President, U.S. National Academy of Medicine; Chunli Bai, President, Chinese Academy of Sciences; and Venki Ramakrishnan, President, The Royal Society ( http://www8 ​.nationalacademies ​.org/onpinews/newsitem ​.aspx?RecordID=12032015b [accessed January 24, 2017]).

The term “epigenome” refers to a set of chemical modifications to the DNA of the genome and to proteins and RNAs that bind to DNA in the chromosomes to affect whether and how genes are expressed.

• Cite this Page National Academies of Sciences, Engineering, and Medicine; National Academy of Medicine; National Academy of Sciences; Committee on Human Gene Editing: Scientific, Medical, and Ethical Considerations. Human Genome Editing: Science, Ethics, and Governance. Washington (DC): National Academies Press (US); 2017 Feb 14. 1, Introduction.
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Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster

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The pervasiveness of gene expression variation and its contribution to phenotypic variation and evolution is well known. This gene expression variation is context dependent, with differences in regulatory architecture often associated with intrinsic and environmental factors, and is modulated by regulatory elements that can act in cis (linked) or in trans (unlinked) relative to the genes they affect. So far, little is known about how this genetic variation affects the evolution of regulatory architecture among closely related tissues during population divergence. To address this question, we analyzed gene expression in the midgut, hindgut, and Malpighian tubule as well as microbiome composition in the two gut tissues in four Drosophila melanogaster strains and their F1 hybrids from two divergent populations: one from the derived, European range and one from the ancestral, African range. In both the transcriptome and microbiome data, we detected extensive tissue- and genetic background-specific effects, including effects of genetic background on overall tissue specificity. Tissue-specific effects were typically stronger than genetic background-specific effects, although the two gut tissues were not more similar to each other than to the Malpighian tubules. An examination of allele specific expression revealed that, while both cis and trans effects were more tissue-specific in genes expressed differentially between populations than genes with conserved expression, trans effects were more tissue-specific than cis effects. Despite there being highly variable regulatory architecture, this observation was robust across tissues and genetic backgrounds, suggesting that the expression of trans variation can be spatially fine-tuned as well as or better than cis variation during population divergence and yielding new insights into cis and trans regulatory evolution.

Author summary

Genetic variants regulating gene expression can act in cis (linked) or in trans (unlinked) relative to the genes they affect and are thought to be important during adaptation because they can spatially and temporally fine-tune gene expression. In this study, we used the fruit fly Drosophila melanogaster to compare gene expression between inbred parental strains and their offspring in order to characterize the basis of gene expression regulation and inheritance. We examined gene expression in three tissues (midgut, hindgut, and Malpighian tubule) and four genetic backgrounds stemming from Europe and the ancestral range in Africa. Additionally, we characterized the bacterial community composition in the two gut tissues. We detected extensive tissue- and genetic background-specific effects on gene expression and bacterial community composition, although tissue-specific effects were typically stronger than genetic background effects. Genes with cis and trans regulatory effects were more tissue-specific than genes with conserved expression, while those with trans effects were more tissue-specific than those with cis effects. These results suggest that the expression of trans variation can be spatially fine-tuned as well as (or better than) cis variation as populations diverge from one another. Our study yields novel insight into the genetic basis of gene regulatory evolution.

Citation: Glaser-Schmitt A, Lemoine M, Kaltenpoth M, Parsch J (2024) Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster . PLoS Genet 20(8): e1011257. https://doi.org/10.1371/journal.pgen.1011257

Editor: Trudy F. C. Mackay, Clemson University, UNITED STATES OF AMERICA

Received: April 14, 2024; Accepted: July 30, 2024; Published: August 23, 2024

Copyright: © 2024 Glaser-Schmitt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The gut RNA-seq and microbiome data that support the findings of this study are publicly available from National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA1094401. Gut non-ASE gene count and expression data are available from NCBI Gene Expression Omnibus (GEO) under the series accession number GSE263264. Malpighian tubule RNA-seq data are available under the NCBI GEO accession number GSE103645. All other relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by a Deutsche Forschungsgemeinschaft (DFG, www.dfg.de ) grant (number 274388701) to JP as part of the priority program “SPP 1819: Rapid evolutionary adaptation”, and a DFG grant (KA2846/5-1, project number 347368302) to MK as part of FOR2682 “Seasonal temperature acclimation in Drosophila”. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Gene expression variation is extensive at all organismal levels, including among tissues [ 1 – 2 ], cells [ 3 – 4 ], or alleles [ 5 – 6 ] of the same individual, and underlies much of the phenotypic variation that we see among individuals, populations, and species [ 7 – 9 ]. A long-standing challenge in evolutionary genetics has been to identify and characterize this variation. Indeed, elucidating the scope and architecture of gene expression variation as well as the mechanisms that shape it is an integral part of better understanding complex phenotypic traits [ 10 – 12 ], such as body size or disease susceptibility, and their evolution.

At the DNA sequence level, genetically heritable variants can modulate expression in two general ways: cis -regulatory variants, such as those within enhancers or promoters, affect the expression of linked, nearby genes, while trans -regulatory variants, such as those affecting transcription factors or regulatory RNAs, affect the expression of unlinked genes that can be located anywhere in the genome (reviewed in [ 13 – 14 ]). One way to interrogate the relative contribution of these types of regulatory variants to gene expression variation in species such as Drosophila , where inbred, relatively isogenic strains are available, is to compare gene expression of two parental strains or species as well as expression of their alleles in F1 hybrids [ 15 ]. Due to linkage with the allele they regulate, cis -regulatory variants affect only one of the two F1 hybrid alleles, leading to allele-specific expression (ASE), while trans -regulatory variants equally affect both alleles in the hybrid and do not lead to ASE. While cis -regulatory variation is thought to accumulate and become more predominant over larger evolutionary distances, i.e. between species [ 16 – 18 ], trans -regulatory variation tends to be more common among individuals within a species [ 5 – 6 , 19 ]. However, deviations from this pattern of regulatory variation have been documented in Drosophila [ 20 – 23 ] as well as other species [ 24 – 25 ], which underscores that there remains much to learn about the evolution of gene expression regulation, especially over short evolutionary distances.

An advantage of utilizing ASE to investigate the regulation of gene expression is that both the genetic basis of expression variation (e.g. cis versus trans ) and the mode of expression inheritance (e.g., dominance versus additivity) can be assessed. Indeed, previous studies of ASE in Drosophila utilizing expression in F1 hybrids have found that environment [ 6 , 26 ], sex [ 27 – 28 ], genetic background [ 19 , 21 , 28 ], and body part or tissue [ 21 – 22 , 28 ] can affect regulatory architecture. However, previous studies have largely focused on single populations, long term lab strains, or comparatively closely related populations [ 5 – 6 , 20 – 22 , 26 – 28 ] (for an exception see [ 19 ]). Moreover, previous studies measured expression in whole animals, body parts (e.g. heads), single tissues, and/or highly functionally diverged tissues (i.e. testes versus ovaries or heads); thus, little is known about how regulatory architecture and inheritance vary among individual tissues that are spatially and/or functionally proximate. To investigate the effect of natural genetic variation from divergent populations on regulatory architecture in multiple functionally related, interconnected tissues, we analyzed messenger RNA-sequencing (mRNA-seq) data of midgut, hindgut, and Malpighian tubule tissues in four D . melanogaster strains and their F1 hybrids. Two of the strains were from a population in Umeå, Sweden [ 29 ], representing the northern edge of the species’ derived distribution, while the other two strains were from a population in Siavonga, Zambia, representing the species’ inferred ancestral range [ 30 ]. Since their divergence from ancestral populations ~12,000 years ago [ 31 ], derived D . melanogaster populations have had to adapt to new habitats, and previous studies have found evidence that at least some of the expression divergence detected between derived and ancestral African populations is adaptive [ 32 – 36 ].

The midgut, hindgut, and Malpighian tubules, which are analogous to the mammalian small and large intestines and kidneys, respectively, physically connect to and interact with one another at the midgut-hindgut junction and are part of the D . melanogaster digestive tract (midgut and hindgut, together with the foregut) and excretory system (hindgut and Malpighian tubules). Both systems play important roles in the regulation of homeostasis as well as the immune response [ 37 – 38 ] and the investigated tissues are known to engage in interorgan communication with each other, as well as with other tissues [ 37 – 39 ]. The excretory system is involved in waste excretion as well as ionic- and osmoregulation [ 38 ], while the digestive tract is an important modulator of food intake, nutrient absorption, energy homeostasis, and insulin secretion that can shape physiology and behavior through its interaction with the microbiome [ 37 , 40 ]. To investigate the effect of natural genetic variation from divergent populations on digestive tract microbiome composition, we further performed microbiome sequencing on the same gut samples for which we performed mRNA-seq. In both the mRNA-seq and microbiome data, we found extensive tissue- and genetic background-specific effects. From the ASE data, we found that genes with both cis and trans effects were more tissue-specific than genes with no differential expression regulation, although trans effects were more tissue-specific than cis effects. Despite the context specificity that we detected for regulatory architecture across tissues and genetic backgrounds, the increased specificity of trans effects was consistent, suggesting that trans- regulatory variation can be spatially fine-tuned as well as or, potentially, better than cis- regulatory variation.

We performed mRNA-seq in the midgut and hindgut of two isofemale D . melanogaster strains from the northern limit of the derived species range in Sweden (SU26 and SU58) and two strains from the ancestral species range in Zambia (ZI418 and ZI197) as well as F1 hybrids between the Swedish and Zambian strains (SU26xZI418, SU26xZI197, SU58xZI418, and SU58xZI197). We additionally reanalyzed previously published mRNA-seq data from the Malpighian tubule [ 19 ] in a subset of these genotypes (SU26, SU58, ZI418, SU26xZI418, and SU58xZI418). We detected 7,675–8,209 genes as expressed in the individual tissues, with 6,894 genes that could be analyzed in all genotypes in all tissues. We focus on the genes that could be analyzed in all examined genotypes and tissues unless otherwise indicated. When considering gene expression variation across all samples, biological replicates clustered strongly by tissue type ( Fig 1A ). Within tissues, replicates mostly clustered by genotype, although in the hindgut there was some overlap between SU58, ZI418 and their F1 hybrid, as well as SU26 and one of its F1 hybrids ( Fig 1 ).

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The legend on the right indicates that replicates of each genotype share the same color, while shape indicates tissue.

https://doi.org/10.1371/journal.pgen.1011257.g001

Differential expression among tissues and genotypes

We detected 116–2,589 (mean 961–1,398) genes as differentially expressed between genotypes within each tissue ( Fig 2A ). However, gene expression divergence (i.e. the cumulative differences in expression across all analyzed genes, as measured by 1 –Spearman’s rho, ρ) between genotypes within each tissue was not significantly different among tissues ( t -test; Bonferroni-corrected P > 0.8 for all; S1A Fig ). Expression divergence tended to be lower between strains derived from the same population (i.e. Swedish strains were more similar to each other than to the Zambian strains and vice versa), although in the hindgut and Malpighian tubule, SU58 was equally or more similar to one or both Zambian strains than to the SU26 strain ( Fig 2A ). This pattern was not evident in the Malpighian tubule when all genes that could be analyzed in this tissue were included in the analysis ( S2 Fig ). When we compared expression within the same genotype among tissues, we detected 4,524–5,139 (mean 4,844) genes as differentially expressed between any two tissues ( Fig 2B ), 50–58% of which were differentially expressed in all pairwise tissue comparisons within a genotype and 1,619–1,880 of which were shared among at least two genotypes, with 1,045 genes differentially expressed among all tissues within all genotypes ( S1 Table ). Of these shared differentially expressed genes, 1,243–1,594 were consistently upregulated in the same tissue within the same genotype, with 700–924 genes consistently upregulated in the same tissue in all genotypes ( S1 Table ). Interestingly, overall gene expression divergence within the same genotype between the midgut and Malpighian tubule was significantly lower than gene expression divergence between either of these two tissues and the hindgut ( t -test; Bonferroni-corrected P < 5 x 10 −5 for both; S1C Fig ), suggesting that among these three tissues, expression within the same genetic background is most similar between the Malpighian tubule and the midgut. When we compared gene expression divergence among genotypes within tissues to gene expression divergence within the same genotype among tissues, gene expression divergence was higher among than within tissues (Bonferroni-corrected P = 8.58 x 10 −15 ; Figs 2C and S1A ). Thus, expression diverges more within a genotype among tissues than among genotypes within a tissue, suggesting that tissue is more predictive of gene expression than genotype.

A) The numbers of differentially expressed (DE) genes between genotypes within the midgut (triangles), hindgut (circles), and Malpighian tubule (squares) are shown above the diagonal, while expression divergence (as measured by 1 –ρ) between genotypes is shown below the diagonal. B) The numbers of differentially expressed genes between the same genotype among midgut (MG), hindgut (HG), and Malpighian tubule (MT) tissues are shown. Dashes indicate missing data. C) Expression divergence among genotypes within the same tissue (across genotype) versus expression divergence between the same genotype among tissues (across tissue). Significance was assessed with a t -test. *** Bonferroni-corrected P < 10 −14 .

https://doi.org/10.1371/journal.pgen.1011257.g002

Mode of expression inheritance is highly tissue- and genetic background-specific

In order to understand how the mode of expression inheritance varies among genotypes and tissues, we categorized genes according to their expression in the two parental strains and the respective F1 hybrid into the following categories (see Methods for more details): similar, P1 dominant, P2 dominant, additive, overdominant, and underdominant, with the Swedish strains being P1 and the Zambian strains P2 ( Fig 3 ; S2 and S3 Tables). For all backgrounds and tissues, the similar category (i.e. genes with similar expression in parents and hybrids) was the largest ( Fig 3A ) and showed the greatest overlap among tissues ( S3 and S4 Figs). The basic expression inheritance categories (those with genes showing additive or P1 or P2 dominant expression in hybrids in comparison to parents) were the next largest categories (Figs 3A , S3 , and S4 ), and typically similar numbers of genes were classified into these categories. However, there were some exceptions depending on category, background, and tissue ( Fig 3A ). For example, 1.5-fold more genes were categorized as dominant in either Swedish strain in comparison to the ZI418 strain in the midgut in comparison to the other tissues, while 3.3–4.6-fold more genes were categorized as dominant in the ZI418 strain in comparison to the SU26 strain in the midgut and Malpighian tubule in comparison to the hindgut. Similarly, 1.8–2.5-fold more genes were categorized as dominant in the ZI197 strain in comparison to either Swedish strain in the midgut than in the hindgut. Genes in these basic inheritance categories were often unique to both the tissue and category (Figs 3B , 3C , S3 and S4 ), with little overlap within each category across all three examined tissues (Figs 3 and S3 ). Unsurprisingly, in background combinations for which we only had data for two tissues, the overlap we detected within categories across tissues was higher ( S4 Fig ). The smallest number of genes were categorized into misexpression categories, i.e. genes showing either over- or underdominance in the hybrid in comparison to the parents (Figs 3A , 3C , S3 and S4 ). Genes in misexpression categories tended to be tissue-specific with little or no overlap among the examined tissues (Figs 3A , 3C , S3 and S4 ). Similar to what we observed for basic inheritance categories, certain combinations of genetic backgrounds and tissues showed larger numbers of misexpressed genes than others (Figs 3A , S3 , and S4 ). For example, we detected relatively high levels of misexpression in the SU26xZI418 background in the midgut and Malpighian tubule, but not the hindgut ( Fig 3A ). Taken together, our results suggest that the mode of expression inheritance is both tissue- and genetic background-specific.

A) The number of genes in each mode of expression inheritance category within the midgut (triangles), hindgut (circles), and Malpighian tubule (squares) at a 1.25-fold change cut-off. Results using alternative cut-offs or for individual tissue analyses can be found in S2 and S3 Tables (see Methods ). Dashes indicate missing data. B, C) Upset plots showing unique and overlapping genes within each tissue in the SU26xZI418 background as an example. Upset plots for the other genotypes can be found in S3 and S4 Figs. Horizontal bars represent the total number (num.) of genes in a tissue and inheritance category combination. Vertical bars represent the number of genes in an intersection (intersect.) class. A filled circle underneath a vertical bar indicates that a tissue and inheritance category combination is included in an intersection class. A single filled circle represents an intersection class containing only genes unique to a single tissue and inheritance category combination, while filled circles connected by a line indicate that multiple tissue and inheritance category combinations are included in an intersection class. Genes categorized into B) basic expression inheritance (inherit.), i.e. P1 dominant (P1 dom.), P2 dominant (P2 dom.), and additive (add.) and C) misexpression (misexpress.) categories are shown. Only intersection classes comprised of either a single tissue and inheritance category combination or an inheritance category in all examined tissues are shown. Additional intersection classes and upset plots for genes categorized into the similar category are shown in S3 Fig D) Phenotypic dominance ( h ) divergence (as measured by 1 –ρ) among backgrounds within the same tissue (across genotype) versus dominance divergence between the same background among tissues (across tissue). Significance was assessed with a t -test. The Bonferroni-corrected P value is shown.

https://doi.org/10.1371/journal.pgen.1011257.g003

Phenotypic dominance and the mode of expression inheritance.

In order to better understand potential variation in the magnitude of phenotypic dominance during expression inheritance, we calculated the degree of dominance, h . In order to compare the magnitude of dominance regardless of which allele was dominant, we calculated h such that values between 0 and 1 or 0 and -1 represent varying degrees of additivity and dominance, with values closer to -1 representing complete dominance of the Swedish background and 1 representing complete dominance of the Zambian background, while values outside this range represent cases of overdominance of the respective background (see Methods for more details). For all genetic backgrounds and tissues, we did not detect any significant difference in the overall magnitude of phenotypic dominance between the two parental backgrounds ( t -test, Bonferroni-corrected P > 0.6 for all). For the majority of tissues and genetic backgrounds, we did not detect differences in the magnitude of dominance within the same genetic background between tissues (Bonferroni-corrected P > 0.26 for all comparisons). We only detected a significant difference in the overall magnitude of dominance within the SU26xZI418 background between the midgut and the Malpighian tubule (Bonferroni-corrected P = 0.015), which may be driven by the large amount of misexpression that we detected in this background, particularly in the Malpighian tubule ( Fig 3A ). Overall, these results suggest that the differences we detected in the mode of expression inheritance among genetic backgrounds and tissues occur on the individual gene level rather than being driven by general, genome-wide changes in dominance. Overall dominance divergence among genetic backgrounds (i.e. the cumulative differences in dominance across all analyzed genes, as measured by 1 –ρ) was not significantly different between the midgut and hindgut (Bonferroni-corrected P = 0.264, S1B Fig ), but could not be compared to the Malpighian tubule for which only 2 background combinations were available. When we compared overall dominance divergence among genetic backgrounds within tissues to dominance divergence within the same genetic background among tissues, dominance divergence was significantly higher among than within tissues (Bonferroni-corrected P = 0.012, Fig 3D ). We observed a similar pattern when we examined gene expression divergence ( Fig 2C ), suggesting that in general divergence is higher among tissues than among different genetic backgrounds within a tissue. However, divergence was higher for dominance than for gene expression ( t -test, Bonferroni-corrected P < 10 −14 ), suggesting that phenotypic dominance of expression is much less conserved among tissues and genotypes than expression itself, although it is possible that this difference can be explained in part by differences in how each trait was measured as expression was measured in a single genotype but dominance was calculated based on three genotypes.

Genetic basis of expression variation is highly tissue- and genetic background-specific

In order to identify genes in our dataset with any level of cis -regulatory divergence between the parental alleles in any genetic background and tissue, we tested for ASE in genes for which we could distinguish between the parental alleles in the hybrid (see Methods ). Of the 4,305–4,592 genes we were able to analyze in all tissues of a genetic background, we detected 80–370 genes showing significant ASE (FDR <5%) depending on genetic background and tissue ( Table 1 ), with a total of 356, 408, 460, and 256 non-redundant genes detected as having ASE in any tissue in the SU26xZI418, SU58xZI418, SU26xZI197, and SU58xZI197 backgrounds, respectively, and a total of 958 genes in all tissues and backgrounds. Within each genetic background 55–86% of genes showing ASE in a particular tissue were unique to that tissue, while, within each tissue, 55–76% of ASE genes were unique to a single genetic background. Indeed, within each genetic background, only 9–73 genes were detected as having ASE in all examined tissues, with backgrounds in which only 2 tissues were examined sharing more ASE genes ( Table 1 ). Thus, allele-specific expression is largely tissue- and genetic background-specific.

https://doi.org/10.1371/journal.pgen.1011257.t001

In order to further understand how the genetic basis of expression varies among genetic backgrounds and tissues, we classified genes in each genetic background and tissue combination into six regulatory categories: “conserved”, “ cis -only”, “ trans -only”, “ cis + trans “, “ cis x trans “, “compensatory“, and “ambiguous”[ 5 ] (see Methods for more details). The proportion of genes falling into each regulatory category was dependent upon tissue and genetic background, although, in general, when considering genes with non-ambiguous regulatory divergence in all tissues and genetic backgrounds, the largest proportion fell into the trans -only category which contained 2.9–30.6-fold more genes than the cis -only category ( Fig 4A ). The midgut had a higher proportion of ambiguous genes and a smaller proportion of conserved genes than the other examined tissues ( Fig 4A ). We detected the most cis -only genes in the hindgut, with 2.3–4.2 fold more genes categorized as cis -only in comparison to the other examined tissues ( Fig 4A ). In comparison to other genetic backgrounds, the SU58xZI197 background had a higher proportion of ambiguous genes and a smaller proportion of conserved genes as well as 2.2–4.7- and 2.4–10.4-fold fewer genes categorized as cis -only and compensatory, respectively ( Fig 4A ). Within each genotype, genes with non-ambiguous regulatory divergence were often unique to both the tissue and regulatory category (Figs 4 , S5 , and S6 ), with little overlap within each category across all three examined tissues (Figs 4 and S5 ). Similarly, within each tissue, genes with non-ambiguous regulatory divergence were often unique to a genetic background, with 35–91% of genes unique to a single genetic background within a regulatory class and tissue, while 31–87% of genes were unique to the genetic background and tissue within a regulatory class ( S7 Fig ). Overlap among all genotypes within a tissue was highest for genes in the trans -only category with 2.8–30.7-fold more shared genes categorized as trans -only in comparison to other non-ambiguous regulatory divergence categories ( S7 Fig ). Overall, our results suggest that the genetic basis of expression inheritance is both tissue- and genetic background-specific.

A) The number of genes in each regulatory class within the midgut (triangles), hindgut (circles), and Malpighian tubule (squares). Results for individual tissue analyses can be found in S5 Table (see Methods ). Dashes indicate missing data. B) Upset plot showing unique and overlapping genes with non-ambiguous regulatory divergence within each tissue in the SU26xZI418 background. Upset plots for other genotypes can be found in S5 and S6 Figs. Only intersection classes comprised of either a single tissue and regulatory category combination or a regulatory category in all examined tissues are shown. Additional intersection classes are shown in S5 Fig . C) Dominance and D) magnitude of dominance h for genes categorized as cis -only ( c , light) and trans -only ( t , dark) in each background and tissue. Magnitude of dominance was calculated as the absolute value of dominance h . Only h values with magnitudes of 5 or below are shown. Boxplots including more extreme h values can be found in S8 Fig . Significance was assessed with a t -test. Bonferroni-corrected P values are shown in grey. *** P < 0.005, ** P < 0.01, * P < 0.05.

https://doi.org/10.1371/journal.pgen.1011257.g004

Phenotypic dominance and the genetic basis of expression variation.

Previous studies have found that cis -regulatory variation tends to be more additive [ 20 , 27 – 28 ], while trans variation tends to be more dominant [ 27 ]. In order to better understand the relationship between the genetic underpinnings of expression variation and dominance, we examined phenotypic dominance ( h ; see Methods ) in genes categorized as cis -only or trans -only. Overall dominance in genes categorized as trans -only was often biased towards one parent, with 5 out of 10 tissue and genetic background combinations significantly more dominant in one parental background than the other (Figs 4C and S8 ; t -test), and the more dominant parent dependent on the tissue and genetic background ( Fig 4C ). On the other hand, overall phenotypic dominance in genes categorized as cis -only was not significantly biased towards one parental background for any of the examined tissue and genetic background combinations ( Fig 4C ; t -test, P > 0.5 for all). When we compared the overall magnitude of dominance (as measured by the absolute value of dominance h ), trans -only genes were only sometimes more dominant than cis -only genes and this was significant after multiple test correction only in the midgut of the SU26xZI418 background ( Fig 4D ; t -test, P < 0.05). However, this lack of significance might be due to lack of power, particularly for cis -only genes, of which we detected fewer ( Fig 3A ). When we included all genes that could be analyzed in each individual tissue and genotype in the analysis, we were able to examine phenotypic dominance in 1.9–3.7-fold more cis -only and 1.6–2.5-fold more trans -only genes ( S6 Table ). The results, however, remained similar, with no increased detection of differences in dominance ( S6 Table ), which suggests that although we cannot completely rule it out, these results are unlikely to be due to a lack of statistical power. Thus, although we detected trans -regulatory variants as more dominant and cis -regulatory variants as more additive, which has been reported by previous studies [ 20 , 27 – 28 ], we only detected this trend in a single background and tissue. Moreover, the phenotypic dominance of trans - but not cis -regulatory variants tended to be biased toward one parental background.

Functional classification of genes displaying ASE.

In order to understand the types of genes showing ASE in our dataset, we tested for an enrichment of gene ontology (GO) biological process and molecular function terms for genes with ASE in each background and tissue. The most commonly enriched GO terms across all backgrounds and tissues were related to oxidoreductase activity ( S7 Table ). Indeed, we detected at least one oxidoreductase activity term for every genetic background and tissue combination in which we detected enriched GO terms. For genes displaying ASE in all examined tissues within a genotype, we could only detect two enriched GO terms, oxidoreductase activity and response to toxic substance, in the SU26xZI197 background ( S7 Table ). Thus, despite ASE genes tending to be tissue- and genetic background-specific, in general ASE genes tended to be enriched for genes predicted to be involved in oxidoreductase activity.

ASE genes are enriched for sex-biased gene expression in a context-dependent manner.

A previous study on ASE in D . melanogaster found differences in the relative contribution of cis -regulatory effects among genes with different levels of sex bias and among two tissues/body parts [ 28 ], while another using hybrids between D . simulans and D . mauritiana found that sexually dimorphic regulatory effects are often in cis [ 27 ]. In order to better understand the relationship between sex-biased expression and cis -regulatory variation in the midgut, hindgut, and Malpighian tubule, we categorized genes according to their level of sex bias using data from FlyAtlas2 [ 41 ] (see Methods ). When considering genes displaying ASE ( Table 1 ), in the Malpighian tubule we detected a significant enrichment of sex-biased genes for both genetic backgrounds (χ 2 test, P = 0.001 for both; S8 Table ), and in the hindgut we detected an enrichment of sex-biased genes in all genetic backgrounds ( P < 0.02 for all; S8 Table ) except SU26xZI418 ( P = 0.2929; S8 Table ). In the midgut, we detected a significant enrichment of sex-biased genes only in the SU26xZI197 background ( P = 0.0116; S8 Table ), despite the midgut having more sex-biased genes, particularly male-biased genes, than the other tissues ( S8 Table ). In the tissues and genetic backgrounds where we detected an enrichment of sex-biased ASE genes, the enrichment did not appear to be driven by bias towards one particular sex (<1.63-fold difference in the prevalence of male- and female-biased genes for all), with the exception of the SU26xZI197 background in the midgut, where male-biased genes were 6-fold more prevalent than female-biased genes ( S8 Table ). It should be noted, however, that only females were used in our experiments to measure ASE. Overall, genes displaying ASE were enriched for sex-biased gene expression, however this enrichment was dependent upon the tissue and genetic background in which they were detected.

The effects of inversions on regulatory variation.

Chromosomal inversions have been shown to affect expression in Drosophila [ 42 ]. Two inversions that are at high frequency worldwide and in sub-Saharan Africa, respectively [ 43 ], were present in our study: In(2L)t in SU26 [ 19 ] and In(3R)K in ZI197 [ 44 ]. A previous study utilizing the current Malpighian tubule data found that while In(2L)t made a minor contribution to gene expression variation, its presence could not explain the patterns of gene expression detected in F1 hybrids with SU26 as a parent [ 19 ]. To better understand how the presence of these inversions potentially affects expression and regulatory variation, we tested for a significant over- or underrepresentation of genes differentially expressed or displaying ASE between parental strains among genes located within these inversions. Genes differentially expressed between parental strains were significantly enriched for genes located within both inversions for all comparisons, including comparisons between strains that did not contain the focal or any inversion (χ 2 test, P < 10 −15 for all; S9 Table ). This finding suggests that the 2L and 3R chromosome arms are enriched for differentially expressed genes, rather than the inversions themselves, which is in line with a previous study that found that linked genetic variation within inversions is what drives differential expression rather than the structural variation itself [ 45 ]. For genes displaying ASE ( Table 1 ), we did not find any dearth or enrichment for genes located within either inversion ( P > 0.29; S9 Table ), with the exception of the SU58xZI418 genetic background in the Malpighian tubule, which was significantly enriched for genes located on the In(2L)t ( P = 0.003; S9 Table ), despite having the standard chromosomal arrangement. Thus, the presence of the In(2L)t and In(3R)K inversions does not appear to explain the regulatory patterns that we detected.

Tissue specificity varies depending on regulatory type and genetic background

For genetic background combinations for which we had transcriptome data in all three tissues (SU26xZI418 and SU58xZI418), we were able to examine the relationship between regulatory variation and tissue specificity. To do so, for every gene in each strain we calculated the tissue specificity index τ, which ranges in value from 0 to 1, with higher numbers indicating higher tissue specificity. When we compared overall τ among all genetic backgrounds, tissue specificity in ZI418 was higher than in either Swedish background as well as both F1 hybrids, although this difference was not statistically significant for SU26 ( Fig 5A ). On the other hand, tissue specificity in the Swedish strains was not significantly different from each other or their respective hybrid ( Fig 5A ). Thus, tissue specificity in F1 hybrids was more similar to the Swedish than the Zambian parent. In order to better understand how tissue specificity varies based on regulatory variation type, we performed pairwise comparisons of τ between genes with trans -only variation, cis -only variation, and conserved gene regulation. Both cis - and trans -regulated genes were significantly more tissue-specific than conserved genes for all strains in all backgrounds ( Fig 5B ). Interestingly, genes with trans -only regulatory variation were more tissue-specific than genes with cis -only regulatory variation, although this difference was not significant for SU58 and the F1 hybrid in the SU58xZI418 background ( P = 0.099 and 0.076, respectively; Fig 5B , S10 Table ). Thus, trans effects were more tissue-specific than cis effects in our dataset. Unlike for the genetic basis of expression variation itself ( Fig 4 ), we detected very few tissue-specific or tissue-by-regulatory type interaction effects on tissue specificity ( S10 Table ). Thus, the genetic basis of regulation (i.e. cis versus trans ) and, to a lesser degree, the genetic background are predictive of tissue specificity, while the tissue in which the regulatory variation was detected tends not to be. Indeed, the type of regulatory variation appears to have the largest influence on tissue specificity, as we were able to detect consistent patterns across tissues and genetic backgrounds ( Fig 5B ).

A) Overall tissue specificity as measured by τ in the examined strains. Significance was assessed with a t -test and Bonferroni-corrected P values are shown. B) Tissue specificity τ in each strain for genes categorized into cis -only (light), trans -only ( tran , dark), and conserved (cons, grey) regulatory (reg.) classes for each genetic background in the midgut (triangles), hindgut (circles), and Malpighian tubule (squares). Significance was assessed with an ANOVA ( S10 Table ). *** P < 0.005, ** P < 0.01, * P < 0.05, ns not significant ( P > 0.05).

https://doi.org/10.1371/journal.pgen.1011257.g005

Microbiome composition varies depending on tissue and genetic background

Bacterial community composition has been shown to affect host gene expression in the digestive tract depending upon host genotype [ 46 ]. In order to better understand the relationship between genetic background and microbiome composition, we performed microbiome sequencing in the midgut and hindgut for the same RNA samples for we which we performed mRNA-seq (see Methods ). For all samples, Wolbachia was highly predominant in the bacterial community (10.36–99.37%; S9 Fig ). In order to ensure its presence did not mask more subtle differences in diversity, we focus on analyses with Wolbachia removed (Figs 6 and 7 ), but results including Wolbachia were qualitatively similar ( S9 and S10 Figs, S11 – S13 Tables), and we did not detect any pattern of relative Wolbachia abundance among genotypes (lmer, P = 0.246; S14 Table ). After removal of Wolbachia , Acetobacter , one of the most common D . melanogaster gut microbial taxa [ 47 – 48 ], remained predominant in the midgut (54.7–99.8%; Fig 6 ). In the hindgut, where the microbiome composition was more diverse, Acetobacter was only dominant in a subset of individuals (1.44–78.52%; Fig 6 ). Interestingly, we did not detect Lactobacillus , another of the most common gut microbial taxa [ 47 – 48 ]. However, because we performed amplicon sequencing using RNA rather than DNA as the starting material (see Methods ), the microbiome composition we detected is representative of metabolic activity rather than presence. Thus, it may be that Lactobacillus was present but its metabolic activity was not high enough for us to detect.

Colored sections of each bar show bacterial genera (excluding Wolbachia ) with a relative abundance above 5% in each sample. The remaining genera are compiled in the “Others” category. Bacterial community composition including Wolbachia can be found in S9 Fig .

https://doi.org/10.1371/journal.pgen.1011257.g006

In order to detect differences in gut bacterial community composition, we computed the Bray-Curtis index (see Methods ). We detected significant differences in gut bacterial communities between the midgut and hindgut (Figs 6 and 7 , S11 Table ; PERMANOVA, P = 0.001), which is unsurprising given that these gut regions differ in their pH and associated digestive functions [ 37 ]. We also detected a significant effect of the genetic background (i.e. strain) as well as a significant interaction effect between the examined strain and gut region on the bacterial community ( Fig 7 , S11 Table ; PERMANOVA, P ≤ 0.015 for both), suggesting that genetic background affects microbiome composition, and this effect is at least partially tissue-dependent. Indeed, when we examined community composition within each tissue individually, the genetic background significantly influenced the gut bacterial community in the hindgut while it had no significant effect on the structure of the community in the midgut ( Fig 7 , S11 Table ; PERMANOVA, P = 0.002 and P = 0.89 respectively). We also detected significant tissue and genetic background effects on bacterial alpha-diversity ( S11 Fig , S12 and S13 Tables; LMM, P = 0.017 and P < 0.001), with the hindgut being more diverse and displaying stronger differentiation between parental and F1 strains ( S11 Fig ). Thus, the diverse bacterial community of the hindgut offered more possibility for differentiation while the midgut community was dominated by Acetobacter among all samples. In contrast, gene expression in the hindgut was less differentiated among genetic backgrounds but more differentiated from the other tissues while the midgut showed the opposite pattern (Figs 1 and S1C ), suggesting that the expression and regulatory variation we detected in these tissues is unlikely to be driven by bacterial community composition. Thus overall, tissue type (i.e. gut region) had the largest impact on microbial community composition and diversity, with genetic background also affecting microbiome variation to a lesser degree, especially within the hindgut; however, these genetic background effects do not appear to be related to host gene expression variation.

Principal coordinate analysis of bacterial communities in A) both midgut and hindgut samples, B) hindgut, and C) midgut. The legend indicates that replicates of each genotype share the same color, while shape indicates tissue. Wolbachia was excluded from the analysis. Results including Wolbachia can be found in S10 Fig .

https://doi.org/10.1371/journal.pgen.1011257.g007

Using transcriptome data from parental and F1 hybrid D . melanogaster strains from an ancestral and a derived population in the midgut, hindgut and Malpighian tubule, we found that both the genetic basis of expression variation (i.e. cis versus trans ) and the mode of expression inheritance (i.e. dominant versus additive) were highly tissue- and genetic background-specific (Figs 3 and 4 , Table 1 ). Previous studies using F1 hybrids in Drosophila have found that genetic background [ 19 , 21 , 28 ] and body part or tissue [ 21 – 22 , 28 ] can have large effects on regulatory architecture; however, to our knowledge, this is the first study examining highly spatially and functionally proximate tissues that not only communicate with each other but also functionally and physically interact. Thus, our results demonstrate that even functionally related, interconnected tissues can show highly divergent regulatory architecture among tissues and genetic backgrounds. Indeed, overall gene expression was most similar between the Malpighian tubule and the midgut ( Fig 1 ), despite these tissues being part of the excretory and digestive system, respectively, while the two gut tissues are part of the same alimentary canal. Thus, our results suggest that the level of functional and physical interconnectivity between tissues may not necessarily be predictive of similarity in gene expression or regulatory architecture. Consistent with this interpretation, we detected similar fold-size differences in the proportion of cis -regulated genes in the hindgut versus the Malpighian tubule or midgut ( Fig 4A ) as has previously been reported in the testes versus the head or ovaries [ 28 ]. However, we should note that the tissues we examined in this study, and the midgut in particular, are known to be regionalized [ 37 – 38 , 40 ] with an estimated 22, 4, and 5 distinct cell types currently described in the midgut, hindgut, and Malpighian tubules, respectively [ 49 – 50 ]; therefore, it is possible that we may have missed some of the more subtle differences in gene regulation that occur among individual regions or cell types.

It has long been thought that regulatory changes and particularly cis -regulatory changes are important during adaptation as they can fine-tune gene expression both temporally and spatially [ 51 – 52 ]. Indeed, we found that genes with trans and cis effects were more tissue-specific than genes with no differential expression regulation ( Fig 5 ), suggesting that regulatory changes between diverged populations are often tissue-specific, which is likely driven by spatial fine-tuning of gene expression. Interestingly and somewhat surprisingly, we found that trans effects were more tissue-specific than cis -effects and this finding was consistent across tissues and genetic backgrounds ( Fig 5B ). Thus, our results reveal that trans -regulatory changes can be as or, potentially, even more tissue-specific than cis -regulatory changes that occur as populations diverge. In contrast to our findings, a recent study on ASE in two mouse tissues found that tissue-specific genes were largely cis -regulated during population divergence [ 25 ]. Indeed, cis -regulatory changes have long been thought to be more common during adaptation due to lower pleiotropy [ 53 ]. One of the disadvantages of our methods is that we were unable to assign regulatory effects to their underlying genetic variants and do not know the location or identity of the genetic variants underlying the detected tissue-specific trans effects. Thus, it is possible that the tissue-specific trans effects we detected are driven by cis -regulatory changes in the transcription factors or other regulators driving these trans effects.

Previous studies found that cis -regulatory variation tends to be more additive [ 20 , 27 , 28 ] than trans variation, which tends to be more dominant [ 27 ]. However, we found little evidence for this pattern in our dataset ( Fig 4D ). The discrepancy between the current study and previous ones may be due to differences in methods or the examined genetic background and/or body parts/tissues, suggesting that differences in additivity and dominance between cis and trans variation may be context-specific. Interestingly, we found that the phenotypic dominance of trans - but not cis -regulatory variation tended to be biased toward one parental background, with the direction of the bias variable among tissues and genetic backgrounds ( Fig 4C ). The context-dependent nature of this finding suggests that this bias may be driven by one or several trans factors affecting the expression of multiple genes in individual tissues and genetic backgrounds, which underscores the importance of taking genetic background and tissue into account when attempting to identify general patterns and trends in gene expression and its regulation. When we examined divergence in gene expression and phenotypic dominance (i.e. the cumulative differences in each trait across all analyzed genes), we found that divergence was higher among than within tissues (Figs 2C and 3D ), suggesting that although both are pervasive, tissue-specific effects outnumber or are larger than genetic background-specific effects, and these effects may be magnified when considering the phenotypic dominance of gene expression, as our findings suggest that it is much less conserved than expression itself.

A previous study in D . melanogaster larvae found that overall developmental (i.e. temporal) gene expression specificity increased during adaptation in a derived population [ 36 ]. In contrast, in our dataset the ancestral ZI418 genetic background showed the highest tissue (i.e. spatial) gene expression specificity ( Fig 5A ); however, it is possible that overall changes in gene expression specificity driven by adaptation are only detectable at the population rather than the individual level. Because we identified regulatory variation between an ancestral and a derived D . melanogaster population that had to adapt to new habitats, some, although not necessarily all, of the regulatory variation we identified may be adaptive. Indeed, one recent study examining ASE between warm- and cold-adapted mouse strains found signs of selection on ASE genes in the cold-adapted mice [ 25 ]. Genes we identified as showing ASE included several for which adaptive cis -regulatory divergence has previously been documented, such as MtnA [ 54 ], Cyp6g1 [ 55 ], Cyp6a20 [ 19 ], and Cyp12a4 [ 19 ]. We also detected an enrichment of oxidoreductase activity and response to toxic substance among ASE genes ( S7 Table ), suggesting any genes with adaptive cis -regulatory variation that we detected may be related to these processes. Indeed, the detected selection on Cyp6g1 expression is thought to have been driven by resistance to the pesticide DDT [ 55 ], while selection on MtnA is thought to be driven by increased oxidative stress resistance [ 34 , 54 ]. Indeed, the digestive system’s direct interaction with the external environment [ 37 ] and the excretory system’s role in detoxification and waste excretion [ 38 ] suggest that many of the ASE genes we identified may be candidates for adaptation.

Similar to our findings for the genetic basis of expression variation, the mode of expression inheritance, and phenotypic dominance (Figs 3 – 5 ), we detected significant effects of tissue and genetic background on bacterial community composition in our microbiome analysis, although the detected genetic background effects did not appear to explain host gene expression variation (Figs 6 , 7 , and S11 ). The endosymbiont Wolbachia , which is known to affect microbiome composition but is not present in the gut lumen [ 56 ], was predominant in all of our samples ( S9 Fig ) but we did not detect the very common Lactobacillus ( Fig 6 ), which suggests that physical abundance within the gut may not be predictive of metabolic activity levels and some bacterial community members may be more or less active than predicted by their physical abundance. Bacterial community composition was highly divergent between the two gut tissues, and the effect of genetic background appeared to be driven by higher diversity in the hindgut, which also showed more differentiation among strains (Figs 6 , 7 and S11 ). Because all flies were reared in the same lab environment, a large portion of the detected bacterial community was likely acquired in the lab. Rearing environment greatly influences bacterial community composition, with communities of lab-reared strains less diverse than their natural-reared counterparts [ 57 ]. Thus, it is difficult to draw conclusions about how the genetic background effects we detected might influence bacterial community composition in nature, although genetic differentiation among natural D . melanogaster populations is known to shape bacterial community structure [ 58 ].

Overall, our findings yield insight into the evolution of regulatory architecture, the effects of regulatory variation on tissue specificity, the effects of genetic background on expression and microbiome variation, as well as the importance of accounting for context-specificity in evolutionary studies.

Materials and methods

D . melanogaster samples and sequencing.

All D . melanogaster strains were reared on cornmeal-molasses medium under standard lab conditions (21°C, 14 hour light: 10 hour dark cycle). mRNA-seq and microbiome sequencing were performed for four isofemale strains, two from Umeå, Sweden (SU26 and SU58) [ 29 ] and two from Siavonga, Zambia (ZI418 and ZI197) [ 30 ] as well as F1 hybrids between the Swedish and Zambian parental lines (SU58xZI418, SU58xZI197, SU26xZI418, SU26xZI197). The SU58 and ZI418 strains have the standard arrangement for all known chromosomal inversion polymorphisms [ 19 , 44 ], while SU26 and ZI197 have the standard arrangement with the exception of In(2L)t , which was present in SU26 [ 19 ] and In(3R)K , which was present in ZI197 [ 44 ]. To determine if inversion status affected our findings, we tested for a significant over- or underrepresentation of genes differentially expressed or displaying ASE between parental strains among genes located within the In(2L)t or In(3R)K inversion using a χ 2 test. Reciprocal F1 hybrids were generated in both directions (i.e. parental genotypes were switched) by crossing 2–3 virgin females of one line with 4–5 males of the other line. Crosses were carried out in 8–13 replicate vials and parental strains were similarly reared (2–3 females and 3–5 males per vial with 8–12 replicate vials) in order to control for rearing density among genotypes.

Midguts (from below the cardia to the midgut/hindgut junction, 20 per biological replicate) and hindguts (from the midgut/hindgut junction to the anus, 60 per biological replicate) were dissected from 6-day-old females in cold 1X PBS and stored in RNA/DNA shield (Zymo Research Europe; Freiburg, Germany) at -80°C until RNA extraction. For F1 hybrids, half of the tissues were dissected from each of two reciprocal crosses in order to avoid potential parent-of-origin effects, although such effects are expected to be absent or very rare in D . melanogaster [ 6 , 59 ]. RNA was extracted from three biological replicates per genotype and tissue type (48 samples in total) with the RNeasy Mini kit (Qiagen; Hilden, Germany) as directed by the manufacturer. mRNA-seq and microbiome sequencing were performed using the same RNA extractions. Poly-A selection, fragmentation, reverse transcription, library construction, and high- throughput sequencing was performed by Novogene (Hong Kong) using the Illumina HiSeq 2500 platform (Illumina; San Diego, CA) with 150-bp paired reads. Malpighian tubule 125-bp paired read data for SU58, SU26, ZI418 and F1 hybrids (SU58xZI418, SU26xZI418), which was composed of 2 biological replicates per genotype (10 in total; 58 libraries in total across all tissues) was downloaded from Gene Expression Omnibus (accession number GSE103645).

Microbiome sequencing and analysis

Reverse transcription was carried out to generate complementary DNA (cDNA) which was used for amplicon sequencing targeting the V4 region of the 16S rRNA bacterial gene. First, template RNA was cleaned of potential residual genomic DNA with the PerfeCta DNase I (Quantabio; Beverly, MA) following the manufacturer’s instructions. Reverse transcription was performed using the FIREScript RT cDNA Synthesis (Solis BioDyne; Tartu, Estonia) with specific bacterial primers, 515F (5’-GTGYCAGCMGCCGCGGTAA-3’) and 806R (5’- GGACTACNVGGGTWTCTAAT-3’), also following the manufacturer’s instructions. The V4 region of the 16S rRNA gene was sequenced from the resulting cDNA on an Illumina Miseq platform using the 515F and 806R primer pair. Using the R package DADA2 (version 1.26.0) [ 60 ], Amplicon Sequence Variants (ASVs) were inferred after trimming (length of 240nt for forward reads and 180nt for reverse reads). Dereplication and chimera removal were performed using default parameters of DADA2. Each ASV was assigned taxonomically using the Silva classifier (version 138.1) [ 61 ]. ASVs assigned to the Eukaryotic and Archeal kingdoms were removed. Given that the gut bacterial community was highly dominated by one ASV assigned to the genus Wolbachia ( S9 Fig ), a known intracellular symbiont of Drosophila melanogaster , we chose to remove it for further statistical analysis, revealing the underlying diversity in the gut bacterial community.

All statistical analyses were performed in R-4.2.2 and each graph was generated with the ggplot2 package [ 62 ]. The composition of the bacterial gut community was analyzed using the Phyloseq package [ 63 ]. ASVs not present in more than 6.25% (3 replicates/48 samples = 0.00625) of the samples were removed for visualization purposes but kept in the data for the remaining analyses. Differences in beta-diversity were tested with permutational multivariate analysis of variance ( vegan package version 2.6–4) [ 64 ] on a Bray-Curtis dissimilarities matrix and Principal coordinate analyses (PCoA) was performed for visualization using the vegan package. Differences in bacterial alpha diversity (species richness, Shannon index, Simpson index and inverse Simpson index generated with vegan package) were tested with linear mixed models (lmer, lme4 package) [ 65 ] and pairwise comparisons were tested following the Tukey method ( emmeans package) [ 66 ]. The RNA extraction batch had no significant effect on differences in alpha and beta-diversity of the bacterial community.

mRNA-seq analyses

Reference genomes for each parental strain were constructed using published genome sequence assemblies of SU26 and SU58 [ 29 ], and ZI197 and ZI418 [ 44 , 67 ] as described in [ 19 ]. Briefly, if a nucleotide sequence difference on the major chromosome arms (X, 2R, 2L, 3R, 3L) occurred between a parental strain and the D . melanogaster reference genome (release 6) [ 68 ], the parental nucleotide variant was included in the new reference transcriptome. If the parental sequence contained an uncalled base (“N”), the reference sequence was used. All transcribed regions (including rRNAs, non-coding RNAs, and mRNAs) were then extracted from each parental reference genome using FlyBase annotation version 6.29 [ 68 ]. For each parental strain library, mRNA-seq reads were mapped to the corresponding parental reference genome. In order to prevent mapping bias for genes with greater sequence similarity to one of the parental reference genomes, reads for F1 hybrids were mapped to the combined parental reference genomes.

Reads were mapped to the reference transcriptomes using NextGenMap [ 69 ] in paired-end mode. Read pairs matching more than one transcript of a gene were randomly assigned to one of the transcripts of that gene. For downstream analyses, we analyzed the sum of read counts across all of a gene’s transcripts (across all annotated exons), i.e. on the individual gene-level. To identify genes with poor mapping quality, for each parental transcriptome, we simulated mRNA-seq data with 200 reads per transcript and either 125 bp or 150 bp reads, then mapped the reads back to the corresponding transcriptome. Genes for which more than 5% of reads mapped incorrectly were removed from the analyses of the corresponding read length (125 bp for Malpighian tubule and 150 bp for midgut and hindgut). Library size ranged from 34.7 to 55.0 million paired end reads, 97.0–98.6% of which could be mapped ( S15 Table ).

ASE and mode of expression inheritance analyses were performed within individual tissues as well as for all tissues together. Analyses for individual tissues were qualitatively similar to our analyses including all tissues; therefore, we focus in the main text on analyses including all tissues ( S1 and S2 Data) and have included individual tissue analyses as Supplementary material ( S3 – S5 Data , S3 – S6 Tables). To standardize statistical power across all libraries included in the analysis, we held the total number of mapped reads constant by setting the maximum number of mapped reads per sample to that of the library with the fewest mapped reads and randomly subsampling reads (without replacement) until the total number of mapped reads for each sample equaled the maximum. The number of reads we subsampled for each dataset were as follows: 34,009,757 in midgut, 31,611,417 in hindgut, and 30,820,759 in Malpighian tubule as well as for analyses including all tissues. We identified differentially expressed genes using a negative binomial test as implemented in DESeq2 [ 70 ]. Gene expression divergence between two strains or tissues was calculated as 1 –Spearman’s ρ between the mean normalized gene counts of the two. Significant differences in divergence were assessed with a t -test. To be considered as expressed in our dataset, we required that a gene have a minimum of 15 reads in each sample, which resulted in 7,684, 8,209, 7,675, and 6,894 genes in the midgut, hindgut, Malpighian tubule, and all tissues, respectively, that could be used in analyses.

Calculation of tissue specificity and phenotypic dominance h

Inference of the mode of expression inheritance

To infer the mode of expression inheritance in F1 hybrids, we compared F1 hybrid expression to parental expression and classified genes into six categories: “similar,” “P1 dominant”, “P2 dominant”, “additive,” “overdominant,” and “underdominant” [ 5 ]. To do so, we compared the fold-change difference in expression as calculated by DESeq2 [ 70 ] for each gene between genotypes to a fold-change cutoff threshold. Genes where all expression differences were below the cutoff were classified as “similar”, while genes for which the expression difference was greater than the cutoff between the hybrid and only one parent were classified as dominant for that parent. Genes were categorized as additive if the expression differences between the hybrid and both parents was above the cutoff and the hybrid expression was between the expression of the two parental strains, or if the difference in expression between the two parents was above the cutoff and hybrid expression was between the two parental strains. Genes were categorized as overdominant if the expression difference between the hybrid and both parents was above the cutoff and hybrid expression was greater than that of both parents. A gene was categorized as underdominant if the expression difference between the hybrid and both parents was above the cutoff and hybrid expression was lower than that of both parents. We employed three fold-change cutoffs (1.25, 1.5, and 2) as well as a negative binomial test [ 70 ] and a 5% FDR cutoff, for which we also included an ambiguous category for genes that did not fit into the other categories. In the main text, we focus on the 1.25-fold cutoff as i) the relative proportion of genes falling into each of the non-similar categories was qualitatively similar for all cutoffs ( S2 Table ), ii) a fold-change cutoff (rather than a statistical test) should avoid bias in detecting differential expression between alleles/genes with higher expression, as the power of statistical tests increases with increasing read counts, iii) the 1.25-fold cutoff has been employed in several previous studies with the justification that most of the significant expression differences detected between samples tend to be of this magnitude [ 5 , 20 , 74 ], and iv) previous work using the Malpighian tubule data we use here empirically determined it to be a reasonable cutoff for this analysis [ 19 ]. The results for the other cut-offs and individual tissues are provided in S2 and S3 Tables.

ASE analysis

In order to detect expression differences between the two alleles in each F1 hybrid, we compiled lists of diagnostic SNPs that could be used to distinguish between transcripts for each pairwise combination of parental alleles in each examined tissue ( S16 Table ). To do so, we first compared the two parental reference genome sequences over all transcribed regions annotated in the D . melanogaster reference genome (version 6.29) [ 68 ] to compile an initial list of diagnostic SNPs. In order to exclude sites with potential residual heterozygosity or sequencing errors, for each tissue, we required that all SNPs inferred from the genome sequences be confirmed in the parental mRNA-seq data with a coverage of ≥ 20 reads and the expected variant in ≥ 95% of the mapped reads of each parent. Next, we called new SNPs from the parental mRNA-seq data if a site was not polymorphic (or contained an N) in the parental genome sequences, but had ≥ 20 mapped reads in each parent with ≥ 95% having the same base in one parent but a different base the other parent. The total number of high-confidence diagnostic SNPs meeting these criteria was 66,030–89,497, 74,388–105,634, and 59,294–60,668 for midgut, hindgut, and Malpighian tubule, covering 6,937–7,474, 7,399–8,166, and 6,394–6,412 genes, respectively.

To assess ASE in the F1 hybrids, we used the mapping data described in the mRNA-seq analyses section above, but used only reads containing at least one diagnostic SNP (i.e. reads that could be assigned to a parental allele). As described above, counts were summed over all transcripts of a gene and the ASE analysis was carried out on a per gene basis. To standardize statistical power between genetic background combinations and/or tissues while maximizing the number of reads that could be included in our analysis, the maximum number of diagnostic reads per sample (i.e. the maximum number of reads for 2 alleles) was set to that of the F1 hybrid with the fewest diagnostic reads. For all other samples, reads were randomly subsampled (without replacement) until the total number of diagnostic reads equaled the maximum for F1 hybrids or half of the maximum for parents. The maximum number of diagnostic reads was set to 15,446,286, 11,058,789, 12,477,714, and 11,058,789 for midgut, hindgut, Malpighian tubule, and all tissues, respectively. We tested for differences in allelic expression using a negative binomial test as implemented in DESeq2 [ 70 ], using only genes with a minimum of 15 diagnostic reads for each allele replicate, resulting in a total of 5,060–5,590, 5,650–6,141, 5,097–5,133, and 4,035–4,592 genes depending on genetic background combination that could be analyzed in the midgut, hindgut, Malpighian tubule, and all tissues, respectively, of which 4,228, 4,800, 4,397, and 2,845 genes could be analyzed in all genetic background combinations. In the main text, we focus on the 2,845 genes that could be directly compared across all genetic background combinations and tissues, although results for individual tissues and genetic background combinations were qualitatively similar ( S5 Table ).

Inference of the genetic basis of expression variation

We determined the genetic basis of expression variation for each gene using the outcome of three statistical tests: a negative binomial test for differential expression between the two parental strains, a negative binomial test for ASE in the F 1 hybrid, and a Cochran–Mantel–Haenszel (CMH) test of the ratio of expression between the two parents and the ratio between the two alleles in the hybrid. For all tests, P -values were adjusted for multiple testing [ 75 ] and an FDR cutoff of 5% was used to define significant differences. We employed the same subsampling procedure as described in the ASE analysis section above in order to balance statistical power between parents and hybrids. We classified genes into regulatory classes [ 5 ] as follows: “conserved” genes showed no significant difference in any test; “all cis ” genes showed significant ASE in hybrids and significant DE between parents, but the CMH test was not significant; “all trans ” genes showed significant DE between the parents and a significant CMH test, but no ASE; “compensatory” genes had no DE between parents, but showed significant ASE in hybrids and a significant CMH test; “ cis + trans ” genes were significant result for all three tests with the expression difference between the parents greater than the difference between the two alleles in the hybrid; “ cis × trans ” genes also had three significant tests, but the expression difference between the parents was less than the difference between the two alleles in the hybrid; and “ambiguous” genes were significant for only one test.

Gene set enrichment analysis

We used InterMine [ 76 ] to search for an enrichment of gene ontology (GO) biological process and molecular function terms for genes displaying ASE in each genetic background and tissue as well as in all tissues.

Sex-biased gene analysis

In order to calculate sex bias for each gene in each examined tissue, we downloaded male (M) and female (F) FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values from FlyAtlas2 [ 41 ] and calculated sex bias as log2(FPKM M /FPKM F ). Based on this log fold-change (LFC), we categorized genes as either sex-biased (LFC ≥ 0.5 or LFC ≤ -0.5) or unbiased (LFC ≤ 0.5 or LFC ≥ -0.5). We tested for a significant over- or underrepresentation of sex-biased genes among genes displaying ASE using a χ 2 test with a Benjamini-Hochberg multiple test correction. To better understand how these sex-biased genes were distributed among different levels of sex bias, we further categorized genes as strongly female-biased (FS; LFC ≤ -1.5), moderately female-biased (FB; -0.5 ≤ LFC ≤ -1.5), unbiased (UB; -0.5 > LFC > 0.5), moderately male-biased (MB; 0.5 ≥ LFC ≥ 1.5), or strongly male-biased (MS; LFC ≥ 1.5; S8 Table ).

Supporting information

S1 fig. expression and dominance ( h ) divergence within and among tissues..

A) Expression and B) dominance ( h ) divergence among genotypes within the midgut (MG), hindgut (HG), and Malpighian tubule (MT) versus divergence between the same genotype among tissues (across). C) Expression and D) dominance ( h ) divergence within the same genotype among tissues. A–C) Significance was assessed with a t -test. D) Significance was not assessed due to the low number of comparisons. Bonferroni-corrected P values are shown. * P < 0.05, ** P < 5 x 10 −5 , *** P < 10 −14 , ns not significant, nt not tested.

https://doi.org/10.1371/journal.pgen.1011257.s001

S2 Fig. Differential expression and divergence within tissues.

The total number of differentially expressed (DE) genes between genotypes within the A) hindgut (HG), B) midgut (MG), and C) Malpighian tubule (MT) are shown above the diagonal, while expression divergence (as measured by ρ subtracted from one) between genotypes is shown below the diagonal. Analysis was performed in each tissue individually. The numbers of genes that could be included in the analysis for each tissue were 8,209 in the hindgut, 7,684 in the midgut, and 7,675 in the Malpighian tubule.

https://doi.org/10.1371/journal.pgen.1011257.s002

S3 Fig. Mode of expression Inheritance in SU26xZI418 and SU58xZI418 backgrounds.

Upset plots showing unique and overlapping genes within the hindgut (circles), midgut (triangles), and Malpighian tubule (squares) in the A,C,E) SU26xZI418 or B,D,F) SU58xZI418 backgrounds. Horizontal bars represent the total number (num.) of genes in a tissue and inheritance category combination. Vertical bars represent the number of genes in an intersection class. A filled circle underneath a vertical bar indicates that a tissue and inheritance category combination is included in an intersection class. A single filled circle represents an intersection class containing only genes unique to a single tissue and inheritance category combination. Filled circles connected by a line indicate that multiple tissue and inheritance category combinations are included in an intersection class. Genes categorized into A,B) basic expression inheritance (inherit.), i.e. P1 dominant (P1 dom.), P2 dominant (P2 dom.), and additive (add.), C,D) misexpression (misexpress.), and E,F) similar categories are shown.

https://doi.org/10.1371/journal.pgen.1011257.s003

S4 Fig. Mode of expression inheritance in SU26xZI197 and SU58xZI197 backgrounds.

Upset plots showing unique and overlapping genes within the hindgut (circles) and midgut (triangles) in the A,B,E) SU26xZI197 or C,D,F) SU58xZI197 backgrounds. Horizontal bars represent the total number (num.) of genes in a tissue and inheritance category combination. Vertical bars represent the number of genes in an intersection class. A filled circle underneath a vertical bar indicates that a tissue and inheritance category combination is included in an intersection class. A single filled circle represents an intersection class containing only genes unique to a single tissue and inheritance category combination. Filled circles connected by a line indicate that multiple tissue and inheritance category combinations are included in an intersection class. Genes categorized into A,C) similar, B,D) basic expression inheritance (inherit.), i.e. P1 dominant (P1 dom.), P2 dominant (P2 dom.), and additive (add.), and E,F) misexpression (mis-express.) categories are shown.

https://doi.org/10.1371/journal.pgen.1011257.s004

S5 Fig. Genetic basis of expression inheritance in SU26xZI418 and SU58xZI418 backgrounds.

Upset plots showing unique and overlapping genes with non-ambiguous regulatory divergence in the hindgut (circles), midgut (triangles), and Malpighian tubule (squares) in the A) SU26xZI418 or B) SU58xZI418 backgrounds. Horizontal bars represent the total number of genes in a tissue and regulatory category combination. Vertical bars represent the number of genes in an intersection class. A filled circle underneath a vertical bar indicates that a tissue and inheritance category combination is included in an intersection class. A single filled circle represents an intersection class containing only genes unique to a single tissue and regulatory category combination. Filled circles connected by a line indicate that multiple tissue and regulatory category combinations are included in an intersection class.

https://doi.org/10.1371/journal.pgen.1011257.s005

S6 Fig. Genetic basis of expression inheritance in SU26xZI197 and SU58xZI197 backgrounds.

Upset plots showing unique and overlapping genes with non-ambiguous regulatory divergence in the hindgut (circles) and midgut (triangles) in the A) SU26xZI197 or B) SU58xZI197 backgrounds. Horizontal bars represent the total number (num.) of genes in a tissue and regulatory category combination. Vertical bars represent the number of genes in an intersection class. A filled circle underneath a vertical bar indicates that a tissue and inheritance category combination is included in an intersection class. A single filled circle represents an intersection class containing only genes unique to a single tissue and regulatory category combination. Filled circles connected by a line indicate that multiple tissue and regulatory category combinations are included in an intersection class.

https://doi.org/10.1371/journal.pgen.1011257.s006

S7 Fig. Genetic basis of expression inheritance across examined tissues and backgrounds.

Shown are A,B) unique and C,D) overlapping genes in each regulatory category. Shown are A) the number of genes unique to each tissue within each regulatory category and genetic background, B) the number of genes unique to each genetic background and tissue within each regulatory category, C) the number of genes in each regulatory category detected in all examined tissues for each genetic background, and D) the number of genes in each regulatory category detected in all genetic backgrounds for each tissue. Asterisks (*) indicate comparisons using only C) two tissues or D) two genetic backgrounds.

https://doi.org/10.1371/journal.pgen.1011257.s007

S8 Fig. All dominance in cis -only versus trans -only genes.

A) Dominance and B) magnitude of dominance h for genes categorized as cis -only ( c , light) and trans -only ( t , dark) in each background and tissue. Significance was assessed with a t -test. Bonferroni-corrected P values are shown in grey. *** P < 0.005, ** P < 0.01, * P < 0.05, ms P marginally significant after multiple test correction ( P < 0.1), ns P not significant after multiple test correction.

https://doi.org/10.1371/journal.pgen.1011257.s008

S9 Fig. Composition of the bacterial communities in the midgut and hindgut of each genotype, including Wolbachia ASVs.

Colored sections of each bar show bacterial genera with a relative abundance superior to 5% in each sample. The remaining genera are compiled in the “Others” category.

https://doi.org/10.1371/journal.pgen.1011257.s009

S10 Fig. Principal coordinate analysis of bacterial communities in A) both midgut and hindgut samples, B) hindgut, and C) midgut, including Wolbachia ASVs.

The legend indicates that replicates of each genotype share the same color, while shape indicates tissue.

https://doi.org/10.1371/journal.pgen.1011257.s010

S11 Fig. Shannon diversity index of the bacterial community in the midgut and hindgut excluding (A) or including (B) Wolbachia ASVs.

* indicates significant differences of the Shannon index between groups (lmer, P < 0.05).

https://doi.org/10.1371/journal.pgen.1011257.s011

S1 Table. Shared differentially expressed genes among tissues and genotypes.

The numbers of overlapping differentially expressed (DE) genes within or among genotypes and/or tissues for all DE genes (All) or genes upregulated in the midgut (MG up ), hindgut (HG up ), and Malpighian tubule (MT up ) tissues are shown. Only genotypes for which data was available in all examined tissues are shown.

https://doi.org/10.1371/journal.pgen.1011257.s012

S2 Table. Mode of expression inheritance in combined tissue analysis.

Numbers of genes in each mode of expression inheritance category within the hindgut, midgut, and Malpighian tubule at a 1.25-, 1.5-, and 2-fold change or 5% FDR cut-off (see Methods ) are shown. 6,894 genes could be included in the analysis. The ambiguous category is only necessary for the 5% FDR cutoff and comprises genes which could not be assigned into other categories.

https://doi.org/10.1371/journal.pgen.1011257.s013

S3 Table. Mode of expression inheritance in individual tissue analysis.

Numbers of genes in each mode of expression inheritance category within the hindgut, midgut, and Malpighian tubule at a 1.25-, 1.5-, and 2-fold change or 5% FDR cut-off (see Methods ) are shown. 7,684, 8,209, and 7,675 genes could be included in the analysis in the midgut, hindgut and the Malpighian tubule, respectively. The ambiguous category is only necessary for the 5% FDR cutoff and comprises genes which could not be assigned into other categories.

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S4 Table. ASE genes identified in individual tissue analyses.

Number of differentially expressed (DE) genes between the parental strains (P) and alleles within the F1 hybrid (H) as well as allele specific genes (ASE) are shown for hindgut (HG), midgut (MG), Malpighian tubule (MT), and shared across all tissues (All). Dashes indicate missing data.

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S5 Table. The genetic basis of expression inheritance in individual tissue analyses.

Numbers of genes in each regulatory category within the hindgut, midgut, and Malpighian tubule are shown. 4,228, 4,800, and 4,397 genes could be included in the analysis in the midgut, hindgut and the Malpighian tubule, respectively.

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S6 Table. Phenotypic dominance in all cis and all trans groups including all genes that could be analyzed in each individual tissue and genotype.

The mean and mean of the absolute value of phenotypic dominance ( h ) are shown. Significance was assessed using a t -test. Significant P -values are in bold and values non-significant after multiple test correction are shown in grey.

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S7 Table. Enriched GO terms in genes with ASE.

Enriched molecular function and biological process GO terms for genes showing ASE in the Malpighian tubule (MT), midgut (MG), hindgut (HG), and all examined tissues. Number (num) of contributing terms and Holm-Bonferroni-adjusted P -values are shown. Genetic background and tissue combinations with no enriched GO terms are not shown.

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S8 Table. Sex bias in genes displaying ASE.

The number of genes categorized as strongly female-biased (FS; LFC ≤ -1.5), moderately female-biased (FB; -0.5 ≤ LFC ≤ -1.5), unbiased (UB; -0.5 > LFC > 0.5), moderately male-biased (MB; 0.5 ≥ LFC ≥ 1.5), strongly male-biased (MS; LFC ≥ 1.5), and generally sex-biased (SB; LFC ≥ 0.5 or LFC ≤ -0.5) are shown. Significant deviations from the expected number of sex-biased genes (SB EXP) were assessed with a χ 2 test. P - and Benjamini-Hochberg-corrected P -values are shown.

https://doi.org/10.1371/journal.pgen.1011257.s019

S9 Table. The effect of In(2L)t and In(3R)K inversion status.

The number of genes differentially expressed (DE) or displaying allele specific expression (ASE) between parental strains along with the number of genes located within an inversion are shown. The strain containing an inversion is shown in parentheses. Significant deviations from the expected number of DE or ASE genes were assessed with a χ 2 test.

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S10 Table. ANOVA results for pairwise comparisons between genes categorized as cis -only, trans -only, and conserved.

Results for ANOVAs with τ of the indicated strain as the response variable and regulatory (reg.) variant type, tissue, and the interaction between them as factors.

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S11 Table. PERMANOVA results on comparison of Bray-Curtis distances of the bacterial community including or excluding Wolbachia ASVs.

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S12 Table. Summary statistics of the LMER ANOVA comparing Shannon indices of the bacterial community including or excluding Wolbachia ASVs.

The mixed models included the genotype and tissue as fixed factors and the batch and sample ID as random factors. The interaction term in between the two fixed effect did not have a significant effect on the Shannon indices and was subsequently removed from the models. LMER: Shannon index ~ genotype + tissue + 1| batch + 1| sample ID.

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S13 Table. Pairwise comparisons of the Shannon index for the bacterial communities including or excluding Wolbachia ASVs.

The comparisons were performed following the Tukey method and the P -values were adjusted via the Benjamini-Hochberg (BH) method.

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S14 Table. Results of lmer comparing the relative abundance of Wolbachia in the bacterial communities.

https://doi.org/10.1371/journal.pgen.1011257.s025

S15 Table. Library size and mapping efficiency for all mRNA-seq libraries.

To prevent mapping bias, all libraries were simultaneously mapped to at least one pair of parental strains (P1 and P2). The number of mapped, paired mapped, unmapped, and discarded reads as well as total reads, read pairs, and proportion (prop) of mapped reads are shown.

https://doi.org/10.1371/journal.pgen.1011257.s026

S16 Table. Number of diagnostic SNPs in each tissue and comparison.

Shown are the number of diagnostic SNPs determined from the respective reference genome (Ref) and with the mRNA-seq data included as well as the number of genes covered by the diagnostic SNPs.

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S1 Data. ASE gene counts and expression in analyses including all tissues.

https://doi.org/10.1371/journal.pgen.1011257.s028

S2 Data. Phenotypic dominance, sex-biased gene expression, and τ in analyses including all tissues.

https://doi.org/10.1371/journal.pgen.1011257.s029

S3 Data. ASE gene counts and expression, and phenotypic dominance in midgut analyses.

https://doi.org/10.1371/journal.pgen.1011257.s030

S4 Data. ASE gene counts and expression, and phenotypic dominance in hindgut analyses.

https://doi.org/10.1371/journal.pgen.1011257.s031

S5 Data. Gene counts, expression and phenotypic dominance in Malpighian tubule analyses.

https://doi.org/10.1371/journal.pgen.1011257.s032

S6 Data. ASV and taxonomy tables of the bacterial communities in the midgut and hindgut.

https://doi.org/10.1371/journal.pgen.1011257.s033

Acknowledgments

We thank Hilde Lainer for excellent technical assistance in the lab as well as Dr. Grit Kunert (Department of Biochemistry, Max-Planck-Institute for Chemical Ecology) for advice on statistical models. We also thank the LMU Evolutionary Biology department for helpful suggestions and discussions.

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• Published: 24 August 2024

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

• Xiaojie Liu 1 , 3 , 4 ,
• Ting Peng 1 , 3 , 4 ,
• Miaochun Xu 1 , 3 , 4 ,
• Shitong Lin 1 , 3 , 4 ,
• Bai Hu 2 , 3 , 4 ,
• Tian Chu 2 , 3 , 4 ,
• Binghan Liu 1 , 3 , 4 ,
• Yashi Xu 1 , 3 , 4 ,
• Wencheng Ding 2 , 3 , 4 ,
• Li Li 2 , 3 , 4 ,
• Canhui Cao 2 , 3 , 4 &
• Peng Wu 1 , 3 , 4  

Journal of Hematology & Oncology volume  17 , Article number:  72 ( 2024 ) Cite this article

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The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Introduction

Single-cell sequencing has been instrumental in providing detailed insights into gene expression at the individual cell level for decades. This technique has revealed the complexity of cellular diversity, exacerbated by processes such as cell proliferation, differentiation, and death, particularly in relation to the local and distant environment of the cell [ 1 ]. Single-cell sequencing can detect cellular heterogeneity, enabling detailed analysis of individual cell behavior, mechanisms, and relationships. The high resolution of these methods has allowed for the extensive exploration and characterization of cell diversity on a large scale. However, despite these advantages, single-cell sequencing often fails to retain critical spatial information about cell populations, resulting in the loss of crucial spatial context [ 1 , 2 ].

To overcome this limitation, spatial multi-omics has emerged as a transformative technology, enabling the precise localization of cells within tissues and the quantitative measurement of gene expression in situ. This advancement marks an important technological breakthrough in life sciences and biomedicine, with wide-ranging applications in neuroscience, developmental biology, and cancer research [ 3 ]. Furthermore, spatial multi-omics allows researchers to investigate the development of multicellular organisms from single totipotent cells, as well as their function, aging, and disease progression. High-throughput multi-omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have also facilitated the mapping of diverse molecular layers, significantly broadening the scope of biological analysis and our understanding of complex biological systems. In the current review, we trace the developmental timeline of spatial multi-omics technologies, highlighting their evolution and substantial contributions to modern science. Furthermore, we discuss the current state of these technologies, their integration into research, and their significant applicative value in enhancing our understanding of biological complexity.

Technologies for spatial omics

Spatial mono-omics, such as spatial transcriptomics, was recognized as the “Technology of the Year 2020” by Nature Methods magazine [ 4 ] (Fig.  1 ). Although single-cell sequencing technology has provided valuable insights into cellular heterogeneity, it lacks spatial context. Spatial multi-omics overcomes this limitation by enabling the precise localization and molecular characterization of individual cells within their tissue environments [ 5 ]. The innovation in spatial multi-omics builds upon foundational spatial mono-omics methods. In this section, we introduce key spatial mono-omics techniques (Table  1 ) and discuss their pivotal role in advancing the field of spatial multi-omics.

Timeline of spatial multi-omics. Transcriptomics, genomics, proteomics, metabolomics, and epigenomics are included. In addition to the frequently used techniques, some emerging methods are mentioned

Spatial transcriptomics

Spatial transcriptomics has significantly enhanced our understanding of cellular organization and intra-tissue interactions based on the systematic measurement of gene expression levels across tissue space. Recent advancements in spatial transcriptomics sequencing have focused on increasing the number of detectable genes or proteins, enhancing sensitivity and resolution, simplifying operation, and expanding the size of the analyzed area. Spatial transcriptomics has been used in various fields, including cancer research [ 6 ], developmental biology [ 7 ], and disease studies [ 8 ]. Fundamentally, spatial transcriptomics technology has the ability to reveal the precise spatial localization of RNA molecules within tissues. In this section, we provide a comprehensive overview of mainstream spatial transcriptomics research strategies and summarize the strengths and limitations of these approaches.

(1) Image-based in situ transcriptomics . Image-based spatial transcriptomics primarily includes fluorescence in situ hybridization (FISH) and in situ sequencing (ISS). Recent advancements feature highly multiplexed single‐molecule FISH (smFISH), which uses reverse complementary oligo probes conjugated with fluorophores [ 9 ] for precise mRNA quantification and localization at the single-cell level [ 10 ]. The specificity of fluorescent probes to their RNA targets is critical for reliable smFISH results [ 11 ]. While smFISH can detect many transcripts due to high hybridization efficiency, signal overlap complicates barcode deconvolution. To address this issue, single-molecule imaging and multiplexed error-robust FISH (MERFISH) (Fig.  2 A) have been developed, allowing the identification of thousands of RNA species in single cells by reducing optical crowding, albeit at the cost of increased imaging rounds and time [ 12 ]. Sequential FISH (seqFISH) [ 13 , 14 ] (Fig.  2 B), an in situ three-dimensional (3D) multiplexed imaging method, also addresses optical crowding by decreasing the number of transcripts per image, requiring additional imaging rounds. Despite these advancements, smFISH is limited by the spectral overlap of fluorophores, restricting its multiplexing capabilities and its effectiveness in analyzing cell heterogeneity in complex tissues [ 15 ]. For example, Long et al. utilized seqFISH to analyze the hippocampus, identifying distinct transcriptional states by quantifying and clustering 249 genes in 16,958 cells [ 14 ], thereby demonstrating the effectiveness of this method for detailed transcriptional profiling in complex tissues.

Technologies of spatial techniques. A The MERFISH technology, a binary barcode scheme that employs different fluorescent probes to sequentially detect each bit. B The seqFISH technology. Complete RNA in cells/tissues was imaged by multiple rounds of hybridization. Each round obtains a coded message, corresponding to a bit in the digital code, and then decodes it to correspond to each RNA. C FISSEQ incorporates amplification after reverse transcription of cellular RNA into cDNA. D STARmap is based on DNA tandem sequencing technology, using complementary pairing principle of DNA and fluorescent dye labeled nucleotide probe for sequence determination. E LCM-seq utilizes a laser beam to microdissect tissue regions under a microscope. F IGS combines in situ sequencing with high-throughput paired-end DNA sequencing. G Slide-DNA-seq is used to fragment genomic DNA in situ by tissue, and barcode connector with spatial information is added for subsequent second-generation sequencing. H CUT and Tag guides Protein A/G-Tn5 transposase to cut the target chromatin region through protein-specific antibodies such as transcription factors. At the same time, sequencing joints are added to both ends of the sequence to form a library for high-throughput sequencing by PCR amplification

Both ISH and ISS provide similar transcriptomic information, with the primary difference being that ISS-based methods directly read nucleotide sequences within tissues to identify a larger number of RNA‐targeting probes, while ISH-based methods image the sequences of barcoded FISH probes [ 9 ]. As a targeted spatial transcriptomics technology, ISS facilitates highly multiplexed in situ gene expression profiling through padlock probes, rolling circle amplification (RCA), and sequencing-by-ligation [ 16 , 17 ] chemistry combined with next-generation sequencing chemistry [ 18 ]. In ISS, reverse transcribed cDNA is hybridized with padlock probes containing gene-specific barcode sequences, which are ligated at the specific hybridization site and amplified by rolling circle amplification (RCA) with a circularized padlock primer probe [ 9 ]. Chatarina et al. developed a method that combines padlock probes with in situ target-primed rolling-circle amplification to detect and genotype individual transcripts, offering deeper insights into mRNA expression heterogeneity within single-cell populations [ 19 ]. Sequential imaging using sequencing-by-ligation allows for the identification of repeatedly amplified barcode sequences in situ, while fluorescent in situ sequencing (FISSEQ) (Fig.  2 C) employs an oligonucleotide ligation and detection substrate (SOLiD) for genome and transcriptome sequencing of DNA amplicons [ 9 , 17 ]. FISSEQ experiences fewer issues with optical crowding compared to ISH-based methods because it is less efficient at converting transcripts into cDNA in situ. However, methods that use padlock probes hybridized with target RNA species require enzyme ligations and have lower detection rates compared to multiplexed FISH methods [ 12 ]. Next-generation FISSEQ [ 20 ] was developed to complement spatially structured sequencing libraries and includes an imaging method capable of resolving amplicons, which is essential for conducting ISS of cellular RNA for gene expression profiling [ 17 ]. RNA is reverse transcribed in fixed cells with tagged random hexamers to generate cDNA amplicons within the cell, which can be repeatedly hybridized with minimal changes in signal-to-noise ratios or position [ 21 ]. RNA sequencing libraries can be visualized in different cell types, tissue sections, and whole-mount embryos, enabling 3D visualization spanning multiple resolution scales [ 17 ]. Spatially resolved transcript amplicon readout mapping (STARmap) [ 22 , 23 ] (Fig.  2 D) employs dynamic annealing and ligation (SEDAL) to reduce sequencing errors. This technology integrates hydrogel tissue chemistry, targeted signal amplification, and ISS [ 22 ], enabling high multiplexing and analysis of thicker tissue slices, although it may detect fewer transcripts in such slices [ 24 ]. BaristaSeq, an optimized padlock probe-based technique compatible with Illumina sequencing, significantly enhances amplification efficiency and sequencing accuracy, achieving at least 97% accuracy and a five-fold increase in amplification efficiency [ 25 ].

Both ISS and ISH-based methods require image processing to generate gene expression matrices. These images are segmented to create cell-level matrices, which can be done manually for small areas or systematically using computational approaches [ 3 , 26 ]. RNA hybridization-based spatial transcriptomics provides exceptional detection sensitivity [ 27 ]; however, the misassignment of mRNAs during cell segmentation is a significant source of error. To address this, the JSTA computational framework utilizes prior knowledge of cell type-specific gene expression to perform joint cell segmentation and cell type annotation, increasing the accuracy of RNA assignment by over 45% [ 28 ]. Spot-based spatial cell-type analysis by multidimensional mRNA density estimation (SSAM) is a robust cell segmentation-free computational framework that identifies cell types and tissue domains in both 2D and 3D [ 29 ].

(2) Oligonucleotide-based spatial barcoding followed by the next-generation sequencing (NGS) [ 20 ]. NGS represents a significant improvement over previous sequencing technologies, offering cost-effective, rapid sequencing with higher throughput, thereby greatly extending our genomic knowledge [ 30 ] and addressing the time and resource-intensive challenges faced by the Human Genome Project [ 31 ]. NGS technologies introduce three main improvements over first-generation sequencing. First, they rely on the preparation of NGS libraries in a cell-free system, eliminating the need for bacterial cloning of DNA fragments [ 32 ]. Second, numerous sequencing reactions are produced in parallel, enhancing efficiency [ 33 ]. Third, sequencing outputs are detected directly, with base interrogation performed cyclically and in parallel [ 34 ]. Several prominent NGS platforms have emerged, including 454 (pyrosequencing) [ 35 ], Illumina/Solexa, and Sequencing by Oligo Ligation Detection (SOLiD) [ 36 ]. The 454 approach involves the clonal amplification of DNA fragments on beads within emulsion droplets, which are then loaded into wells for sequencing using the pyrosequencing protocol [ 37 ]. This approach enables the sequencing of long reads, making it suitable for various applications, although its inherent problem in detecting homopolymers and nucleotide stretches can impact data quality as sequence volume increases [ 38 ]. Illumina/Solexa employs an array-based DNA sequencing-by-synthesis technology with reversible terminator chemistry [ 39 ]. Primers, DNA polymerase, and four differently labeled reversible terminator nucleotides are used, with each nucleotide identified by color, followed by terminator and fluorophore removal, and the cycle repeating [ 34 ]. This platform currently offers the highest throughput and lowest per-base cost, making it the leading NGS platform. In contrast, the SOLiD platform prepares sequencing libraries by emulsion polymerase chain reaction (PCR) and sequences through successive cycles of ligation [ 39 ], exhibiting the lowest error rate among the three platforms. However, NGS methods have several drawbacks, notably short reads that fail to cover full-length transcripts in eukaryotic genomes and challenges in detecting larger structural variations. Additionally, the reliance on PCR amplification can lead to difficulties in regions with extreme GC content [ 40 ]. The advent of single-molecule, third-generation sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), has resolved these issues. PacBio and ONT offer read lengths exceeding 15 kb and 30 kb, respectively, surpassing the length necessary to capture most RNA molecules in eukaryotes. Furthermore, ONT long-read sequencing does not require PCR amplification, thereby reducing potential bias [ 41 , 42 ].

(3) Laser capture microdissection (LCM) . A key challenge in transcriptomics is precise segmentation of tissues and accurate assignment of individual cells to specific locations, often resulting in the loss of spatial information [ 12 ]. LCM (Fig.  2 E), a powerful, microscope-guided cutting system that uses ultraviolet (UV) light as a contact- and contamination-free knife [ 43 ], enables accurate isolation of specific tissues or cells of interest from complex tissue structures. The combination of smart-3SEQ and LCM overcomes various experimental design challenges posed by conventional single-cell RNA-sequencing (scRNA-seq). For instance, formalin-fixed, paraffin-embedded archival clinical tissues, which are unsuitable for conventional RNA-seq due to their inability to be physically dissociated, and fresh or frozen non-archival tissue samples that lack sufficient material for clinical studies can be effectively analyzed using the LCM smart-3SEQ technique [ 11 ].

Spatial genomics

The proper functioning of tissues relies on the precise spatial organization of cell types, which is influenced by both intrinsic genetic factors and the external cellular environment. In cancer, tumor cells exhibit multiple DNA mutations and large chromosomal rearrangements, resulting in intratumor genetic heterogeneity [ 44 ]. Additionally, cells within the tumor microenvironment (TME) interact with each other, forming spatial neighborhoods with distinct biochemical and biomechanical properties. Quantifying these genetic aberrations and environmental cues within tumors is critical for understanding cancer progression and improving treatment [ 45 ]. In situ genome sequencing (IGS) (Fig.  2 F) and slide-DNA-seq (Fig.  2 G) are two exciting methods that promise to fuel the spatial genomics revolution [ 46 ]. IGS expands non-targeted genomic samples in a natural spatial environment, creating an in situ sequencing library in a fixed sample using in vitro transposon technology to fragment DNA. Hairpin DNA splices are then connected to DNA fragments to form circular DNA, which is amplified via rolling circle replication mediated by Phi29 DNA polymerase. Sequencing is performed at both in situ and ectopic sites on the circular DNA fragments [ 47 ]. IGS spatially locates paired-end sequences of the whole genome in an endogenous environment, combining sequencing and imaging to construct a genome map [ 47 ]. This technique specializes in high-resolution imaging of chromosome structure, allowing detailed analysis of tissue sections. Slide-DNA-seq enables spatially resolved sequencing of DNA from intact tissues. The process begins with generating a spatial index array composed of 3 mm beads, each containing unique DNA barcodes corresponding to specific spatial locations. This array is then read through chemical sequencing [ 48 ]. Next, a single 10 μm thick fresh-frozen tissue slice is transferred onto the sequencing bead array. Spatial barcoding is performed through photolysis, and the proximal genome fragments are attached and amplified via PCR to create a DNA sequencing library [ 49 ]. Following library construction, high-throughput paired-end sequencing is carried out, associating each genome fragment with its spatial location on the bead array using DNA barcoding [ 45 ]. Slide-DNA-seq enables detection of clonal heterogeneity, characterization of copy number variations in each clone, and analysis of their spatial distribution within tissue. This technique is particularly useful for large-scale mapping of tumor evolution, providing essential spatial context to the study of clonal heterogeneity [ 45 , 50 ]. Current methods for characterizing chromatin states or DNA within tissues on a large spatial scale are still in their infancy. The integration of spatial multi-omics technologies aims to achieve spatially resolved whole-exome or whole-genome sequencing. Ultimately, integrating various spatially resolved omics technologies will mark the beginning of the era of molecular anatomy, offering unprecedented insights into tissue organization and function [ 46 ].

Spatial proteomics

Proteomics involves the large-scale study of proteins, encompassing their expression levels, post-translational modifications, and protein–protein interactions, thereby providing a comprehensive understanding of processes such as disease occurrence and cell metabolism at the protein level. Proteins, whether in their native or modified forms, are functional units within the body, making the direct study of proteomics more valuable than relying on transcripts. Targeted localization of proteins within eukaryotic cells can redirect existing proteins to various transport pathways, including nuclear, mitochondrial, ciliary, peroxisomal, endomembrane, and vesicular transport [ 51 ], enabling rapid changes in local protein functions. Conversely, protein mislocalization is frequently associated with cellular dysfunction and diseases such as neurodegeneration, cancer [ 52 ], cystic fibrosis [ 53 ], and metabolic disorders. Therefore, researching protein localization at the subcellular level and capturing subcellular dynamics are crucial for a complete understanding of cell biology. Two primary approaches are used to acquire large-scale spatial proteomic data, including mass spectrometry (MS) and imaging-based methods.

(1) Mass spectrometry-based methods : These approaches offer accurate, proteome-wide identification and quantification of proteins. In subcellular proteomics, specific subcellular compartments are often isolated through biochemical fractionation or proximity labeling before MS analysis [ 54 ]. Key processes involve the enrichment and quantification of proteins through biochemical fractionation across different stages using MS [ 55 ]. Organelles are separated based on properties such as size, density, membrane solubility, or charge, with differential and density centrifugation being common strategies. These methods typically achieve high sensitivity and proteome coverage, although contamination from non-target proteins can occur. Ensuring adequate enrichment of the target organelle is crucial for accurate analysis. Once purified, the distribution profiles of proteins specific to different organelles can reveal the subcellular localization or complex binding of uncharacterized proteins [ 54 ]. MS analysis, combined with multivariate statistics and machine learning (ML), is widely used to handle the complex data generated in spatial proteomics [ 56 ]. These techniques compare the abundance distribution of proteins with known organelle markers to infer protein locations and trafficking pathways [ 57 ]. They can identify trends in organelle protein distribution, even in the presence of structural alterations. Proteins, which can have different morphologies and modified states, function as essential units within cells. The relationship between mRNA and corresponding protein expression is highly regulated and non-linear, making RNA expression an unreliable predictor of protein levels. Unlike the more random expression of transcripts, proteins exhibit a much lower coefficient of variation than their homologous mRNA counterparts. Therefore, directly studying proteins at the single-cell level is far more informative than using transcripts as proxies [ 58 ]. Deep visual proteomics (DVP) combines artificial intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity MS. This technique associates protein abundance with complex cellular or subcellular phenotypes while preserving spatial context [ 59 ]. To achieve this, an ultra-sensitive liquid chromatography-mass spectrometry (LC/MS) workflow has been developed, enhancing sensitivity by up to two orders of magnitude to enable true single-cell state proteomic analysis. The data generated by DVP provide molecular insights into proteomic variation at the phenotypic level while retaining complete spatial information.

(2) Imaging-based methods : These approaches allow for the visualization of proteins in situ without requiring cell lysis or the physical separation of compartments or organelles. Unlike MS methods, which are faster and suitable for large‐scale quantitative analysis, imaging-based approaches visualize the interactions between proteins and affinity reagents. Modern microscopes can simultaneously analyze up to 50 proteins, but each protein of interest requires specific and validated antibodies, limiting high-throughput detection. To minimize the loss of soluble proteins during cellular permeability, it is best to use non-specific crosslinking in proteome-wide studies. It is increasingly evident that protein expression varies even among genetically identical cells. Imaging-based methods can capture this variation by targeting the spatial distribution of proteins at single-cell resolution [ 60 ]. However, the number of published global spatial proteomic studies remains small due to the high cost and time-consuming production of affinity reagents for entire proteomes.

MS and imaging methods each have unique advantages and disadvantages and can complement each other. MS offers high sensitivity, high resolution, and powerful quantitative analysis but involves complex and costly sample preparation. In contrast, imaging methods provide high spatial resolution and dynamic observation capabilities, enabling visualization of protein distribution but have limited quantitative abilities and cover only a small number of proteins. Combining these two techniques allows for comprehensive global protein analysis, enabling the observation of the spatial distribution and dynamic changes of key proteins for a more complete understanding of protein spatial organization.

Spatial epigenomics

Spatial epigenomics examines modifications to the DNA sequence and chromatin structure that regulate gene activity without altering the genetic code itself [ 61 ]. Nucleosomes, the fundamental units of chromosomes, are organized into higher-order chromatin structures. Epigenetic modifications, such as histone acetylation, methylation, phosphorylation, ubiquitination, and DNA methylation, play crucial roles in regulating chromatin structure and DNA accessibility [ 62 , 63 ]. These modifications impact key cellular processes, including gene transcription, DNA replication, recombination, and repair [ 61 ]. Unlike other omics fields, epigenomics relies heavily on bioinformatics to uncover the mechanisms by which the epigenome operates at the molecular level. Developing powerful, repeatable, and process-based techniques is essential for generating data that can be integrated into existing omics databases. The ultimate goal is to create a comprehensive picture of the epigenome by combining information on DNA methylation, chromatin dynamics, accessibility, and gene expression [ 64 ]. Epigenomic MERFISH combined with the recently developed Cleavage Under Targets and Tagmentation (CUT&Tag) approach (Fig.  2 H) enables the mapping of more than 100 epigenomic loci in tissues [ 65 ]. These maps can be used to study patterns of active and silent promoters and potential enhancers, providing deeper insights into the spatial organization and regulation of the epigenome [ 65 ].

Spatial metabolomics

Metabolites play a crucial role in various cellular activities, such as cell signaling, energy transfer, and intercellular communication [ 66 ]. Metabolomics is an emerging discipline that involves the qualitative and quantitative analysis of all low-molecular-weight metabolites within an organism or cell during specific physiological states [ 67 ]. Analyzing metabolites presents challenges due to their dynamic nature and susceptibility to environmental influences during cellular processes [ 68 ]. Spatial metabolomics involves the initial detection and quantification of metabolites present in biological material [ 69 ]. Depending on experimental objectives, researchers can employ either targeted approaches, focusing on quantifying specific analytes, or untargeted approaches, focusing on biomarker discovery and global metabolite profiling [ 70 ].

(1) Targeted metabolomics [ 71 ]: This approach analyzes specific subsets of compounds to address particular biochemical questions or hypotheses. The two primary methods include Fourier transfer mass spectrometry (FT-MS) [ 72 ] and nuclear magnetic resonance (NMR) [ 73 ], both of which offer significant advantages in data acquisition due to their specificity and quantitative reproducibility. FT-MS generates mass data for infused samples, allowing for the identification and matching of metabolites with entries in metabolomics databases. The major drawback of this method is that it does not establish a one-to-one correspondence relationship between entities, which means that a single data point can potentially match with multiple metabolites. NMR produces signals based on the chemical environment of protons present in each metabolite, enabling tentative identification [ 74 ]. The development of triple quadrupole (QqQ) MS provides a robust and sensitive method for high-throughput measurement of a substantial number of biologically significant metabolites. This technique is particularly effective for quantifying low-concentration metabolites that are difficult to detect using NMR [ 75 ].

(2) Untargeted metabolomics : Untargeted metabolomics aims to globally analyze biological compounds, permitting the simultaneous detection of as many metabolites as possible and the exploration of cellular biochemical pathways. LC/MS is the most commonly used platform for untargeted metabolomics [ 76 , 77 ], producing numerous signals during the detection of biological samples. The structural diversity of metabolites is vast, and the acquired data often include both known and unknown metabolites. When searching metabolomics databases for the mass–charge ratio of each detected feature, only a small percentage match the database entries, making the identification of unknown metabolites challenging [ 78 ]. The number of detected unknown metabolites is often overestimated due to several factors. A high concentration of 13 C can cause a mass shift, leading to the detection of multiple features for a single metabolite through naturally occurring isotopes. Additionally, a single metabolite can be ionized into various adducts, including isomers, increasing the demand for selective analytical techniques. Furthermore, metabolites can fragment or form non-covalent interactions with other metabolites upon entering the mass spectrometer. These factors collectively increase the complexity and diversity of detected metabolites [ 79 ].

LC/MS data analysis addresses the complexity of metabolite detection through two main approaches: (1) grouping metabolites with similar features and (2) annotating the type of ion species. These steps facilitate the identification of excimer ions, which are essential for further metabolite identification, such as determining elemental composition or conducting tandem MS based on accurate mass and isotope patterns. CAMERA (an integrated strategy for compound spectral extraction and annotation of LC/MS datasets) can effectively identify most features corresponding to isotopes, adducts, and fragments [ 80 ]. Isotopic labeling methods can also be used to identify and analyze isotope ratio outliers [ 81 ]. Despite these advancements, many metabolites remain uncharacterized. Variations in metabolites within the cellular environment are closely linked to health and disease development. Metabolomics enhances disease analysis at the genomic and protein levels by providing semi-quantitative and quantitative measurements of metabolite levels, which serve as chemical mediators defining specific phenotypes [ 70 ]. The rapid expansion of omics technologies has provided holistic molecular information, enabling the comprehensive study of biological systems. Small molecules and metabolites are essential for numerous cellular functions [ 82 ], offering unique insights into the phenotypic characteristics associated with genome sequences [ 83 ].

Integration of spatial multi-omics

While single-cell multi-omics yields valuable insights into gene regulation across various omics layers [ 84 , 85 ], it lacks the spatial information necessary for understanding cellular functions within tissues. Recently, spatial transcriptomics, proteomics, genomics, epigenomics and metabolomics have emerged, with extensive application in various fields [ 86 , 87 ]. These techniques typically capture only one layer of omics information, and computational methods for integrating data from different omics layers cannot fully overcome the lack of mechanistic links between them. Spatial multi-omics enables the simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, with the same tissue section (Table 2 ).

Integration of spatial transcriptomics and (epi)genomics

Spatial ATAC&RNA-seq and spatial CUT&Tag RNA-seq have revolutionized genome-wide co-mapping of the epigenome and transcriptome by simultaneously profiling chromatin accessibility and mRNA expression, or histone modifications and mRNA expression, respectively. These technologies integrate the chemistry of spatial ATAC-seq or CUT&Tag with spatial transcriptomics on the same tissue section at the cellular level via deterministic co-barcoding [ 88 ], combining microfluidic deterministic barcoding in tissue (DBiT) strategies for spatial ATAC-seq [ 89 ] and CUT&Tag [ 90 ] with DBiT-seq poly(A) transcript profiling [ 91 ]. Spatial-ATAC-seq enables high-spatial-resolution genome-wide mapping of chromatin accessibility in tissue at the cellular level by applying a spatial barcoding scheme to DNA oligomers inserted into accessible genomic loci by Tn5 transposition [ 89 ]. This technology advances our understanding of cell identity, cell state, and cell fate decisions related to epigenetic bases in development and disease. Spatial-CUT&Tag analyzes spatial histone modification profiling at the pixel level on frozen tissue sections without requiring dissociation. This method addresses spatially distinct and cell type-specific chromatin modifications during mouse embryonic organogenesis and postnatal brain development, adding a new dimension to spatial biology by mapping epigenetic regulation related to development and disease [ 90 ]. DBiT-seq creates a 2D grid of spatially barcoded tissue pixels, each defined by a unique combination of barcodes A and B [ 88 ]. After reverse crosslinking, barcoded complementary DNA and genomic DNA fragments are released, and NGS constructs separate libraries for gDNA and cDNA. Sequencing reads are then combined with microscopy images of the tissue section based on spatial barcodes, allowing multi-omics sequence information to be spatially mapped [ 88 ]. These techniques have been applied to co-map embryonic and juvenile mouse brains, as well as the adult human brain. Spatially resolved, genome-wide co-sequencing of the epigenome and transcriptome at the cellular level provides an informative tool for a wide range of biological and biomedical research. Transcriptomics focuses on gene expression from the perspective of mRNA, presenting a global perspective on molecular dynamic changes induced by environmental factors or pathogenic agents [ 92 ]. Benefiting from mature in situ RNA hybridization strategies, targeted capture of DNA sequences or chromosomal loci facilitates spatial genomics detection. DNA-seq FISH + can be applied for studying the spatial structure of the genome based on multi-round probe hybridization imaging. Takei et al. [ 93 ] reported the imaging of 3660 chromosomal sites in a single mouse embryonic stem cell (ES) using DNA-seq FISH + and the imaging of 17 chromatin markers and subnuclear structures by sequential immunofluorescence and expression profiles of 70 RNAs. Genomic regions and chromosomes associated with nuclear bodies and chromatin marks in different cells were revealed by genomic regions. Some of these regions appear to be related to cell types, whereas others (mostly spot-related regions) are more conserved among different cell types [ 46 ].

Integration of spatial proteomics and transcriptomics

Single-cell multi-omics has been highly successful in capturing diverse biological processes at the level of individual cells and nuclei but lacks spatial information [ 94 ]. Gene expression is regulated at multiple levels, from transcription to protein degradation, with RNA and protein levels conveying distinct information about gene function and cell state. These processes occur in various contexts, such as tumors and single-cell suspensions [ 95 ]. Recent progress in spatial in situ profiling has enabled the simultaneous profiling of location and expression. Spatial transcriptomics provides a global spatial tissue profile and has been applied to the study of diverse diseases. Spatial proteomics acquires large-scale spatial proteomic data through MS- and imaging-based experimental approaches. However, few platforms have successfully integrated spatial proteomics and transcriptomics data. Vickovic et al. [ 96 ] developed Spatial Multi-Omics (SM-Omics), an end-to-end framework that leverages a liquid handling platform for high-throughput transcriptome and antibody-based spatial tissue profiling. Using DNA-barcoded antibodies, this automated system enables the simultaneous profiling of the epitopes and transcriptomes within single cells, offering detailed molecular characterization of tissues in situ by quantifying both spatial transcriptomics and multiplex protein detection [ 96 ]. Compared to Visium by 10X Genomics, SM-Omics provides an automated workflow that extends combined spatial transcriptomics and antibody-based protein measurements into a scalable all-sequencing-based technology.

NanoString GeoMx Digital Spatial Profiler (DSP) facilitates high-plex profiling at both the protein and RNA level, permitting spatial and temporal assessment of tumors in frozen or formalin-fixed, paraffin-embedded limited tissue samples [ 97 ]. This platform quantifies protein or RNA abundance by counting unique indexing oligos assigned to each target of interest, using oligonucleotides to study a higher number of biomarkers. Additionally, DSP is a non-destructive technique, allowing the same slides to be used for subsequent studies after the assay is completed [ 97 ].

Spatial co-indexing of transcriptomes and epitopes (Spatial-CITE-seq) offers high-plex protein and whole-transcriptome co-mapping. This approach involves the staining of a tissue slide with a cocktail of approximately 200–300 antibody-derived tags (ADTs), followed by deterministic in-tissue barcoding of both DNA tags and mRNAs. Each tag contains a unique spatial address code AiBj (i = 1 − 50, j = 1 − 50), co-indexing all protein epitopes and the transcriptome. Barcoded cDNAs are subsequently retrieved, refined, and amplified via PCR to create two NGS libraries for paired-end sequencing of ADTs and mRNAs. This process enables computational reconstruction of spatial protein or gene-expression maps [ 98 ].

Integration of spatial transcriptomics and metabolomics

Gene expression and metabolite distribution in tissues are influenced by a variety of factors, including cell type, microenvironment, signaling pathways, and gene regulation. To elucidate the interplay among these factors, it is essential to employ methods that can simultaneously measure molecular evidence of different patterns in tissues while preserving spatial distribution information. Researchers have developed a spatial multimodal analysis (SMA) protocol that combines spatially resolved transcriptomics and mass spectrometry imaging (MSI) in a single tissue slice, while maintaining the specificity and sensitivity of both analytical methods [ 88 ]. This integrated approach reveals associations and heterogeneities between transcriptomes and metabolomes across different tissue regions. Combining spatial transcriptomic and metabolomic data, Vicari et al. identified a reduced proportion of midbrain dopaminergic neurons (MBDOP2) in the lesioned substantia nigra pars compacta and ventral tegmental area, and specified the localization of multiple neurotransmitters and metabolites, including taurine, 3-methoxytyramine, 3,4-dihydroxy-phenylacetaldehyde (DOPAL), 3,4-dihydroxy-phenylacetic acid, norepinephrine, serotonin, histidine, tocopherol, and gamma-aminobutyric acid [ 88 ]. Oral submucous fibrosis (OSF) is a well‐established precancerous lesion, but the molecular mechanisms underlying its malignant transformation into oral squamous cell carcinoma (OSCC) remain unclear [ 99 ]. Yuan et al. integrated spatial transcriptomics and metabolomics to obtain spatial location information on cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes of OSF-derived OSCC tissues. Moreover, they revealed the malignant progression from in situ carcinoma (ISC) to partial epithelial-mesenchymal transformation (pEMT), and identified significant metabolic reprogramming, including abnormal polyamine metabolism, which may play a key role in promoting tumorigenesis and immune escape [ 100 ]. Zheng et al. [ 101 ] combined spatial transcriptomic and metabolic analyses to reveal metabolic heterogeneity and complex transcriptome regulation in injured human brain tissue, facilitating the design of reagents for functional analysis of specific genes. The simultaneous application of these advanced technologies reveals the spatial composition of functional maps within tissues, heterogeneous distribution of cell populations, and differential gene expression in different locations. This comprehensive spatial expression mapping of genes holds significant research value and potential for advancing our understanding of complex biological systems.

Integration of spatial transcriptomics, genomics, and proteomics

The integration of spatial multi-omics aims to expand our understanding of mechanistic relationships across different omics layers and uncover molecular roles essential for cellular function by jointly profiling the transcriptome, genome, epigenome, proteome, and metabolome. Spatially resolved joint analysis of multi-omics can facilitate the identification of novel cell subtypes and measurement of intracellular and intercellular molecular interactions [ 102 ]. Therefore, the need for advanced spatial multi-omics methods has become increasingly important. Multi-omics in situ pairwise sequencing (MiP-seq) is a high-throughput targeted in situ sequencing technique that simultaneously detects multiplexed DNA, RNA, proteins, and biomolecules at subcellular resolution, providing comprehensive data for studying cellular functions and disease mechanisms [ 103 ]. The in situ detection of proteins and biomolecules is achieved using padlocking probes that target antibody-conjugated nucleic acids, while the detection of DNA and RNA is accomplished through direct padlock probes targeting nucleic acids [ 103 ]. Compared to current in situ sequencing methods, MiP-seq utilizes a pairwise-sequencing strategy and dual barcoded padlock probes, markedly increasing decoding capacity and requiring fewer sequencing rounds (10 N vs. 4 N ). Consequently, MiP-seq can reduce sequencing time by approximately 50%, lower sequencing and imaging costs, and minimize laser damage, thereby improving signal decoding accuracy, a key issue in in situ sequencing [ 104 ]. MiP-seq has been applied to mouse brain tissue, enabling the in situ detection of Rbfox3 and Nr4a1 gene loci, which are located on different chromosomes and spatially localized within the nucleus. MiP-seq has also been used to study PK-15 cells co-infected with porcine circovirus 2 (PCV2) and classical swine fever virus (CSFV), simultaneously detecting mRNA from eight cytokine or chemokine genes and two virus-specific proteins (CSFV E2 protein and PCV2 Cap protein) by binding antibodies to nucleic acids [ 103 ]. Thus, MiP-seq demonstrates versatility and high sensitivity in multi-omics in situ analysis, detecting specific DNA sequences, RNA transcripts, and proteins at single-cell resolution, and is a powerful tool for studying cell function, disease mechanisms, and cell–cell interactions in complex biological systems.

Applications of spatial multi-omics

Deciphering spatial-specific atlas production of molecular and cellular profiles.

A comprehensive spatial-specific atlas of molecular and cellular profiles in both healthy and diseased states is essential for developing new therapeutic targets and disease interventions (Fig.  3 A). Spatial transcriptomics combined with single-cell sequencing has been widely used to decipher molecular profiles. Fang et al. constructed a spatial atlas of the human middle and superior temporal gyrus using MERFISH, revealing differences in the cellular composition of these cortical regions between humans and mice [ 105 ]. Single-nucleus RNA-seq (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) [ 106 ], and spatial transcriptomics have been applied to generate a spatially resolved multi-omics single-cell atlas of the entire human maternal–fetal interface, including the myometrium, enabling resolution of the full trajectory of trophoblast differentiation [ 107 ]. Kuppe et al. used snRNA-seq and spatial transcriptomics to create an integrative high-resolution map of cardiac remodeling, enhancing the spatial resolution of cell-type composition and providing spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair, and remodeling [ 106 ]. Advanced spatial epigenome-transcriptome co-sequencing has revealed how epigenetic mechanisms control transcriptional phenotypes and cell dynamics at both spatial and genome-wide levels, providing new insights into spatial epigenetic initiation, differentiation, and gene regulation within tissue structures. Spatial ATAC-RNA-seq and spatial CUT&Tag-RNA-seq were first introduced in analyzing mouse embryos, successfully distinguishing each organ with epigenetic and transcriptome data [ 88 ]. In some mouse brain tissue regions, the epigenetic signature of certain genes persisted with development, but the gene expression was different. In addition, the results of the joint analysis also found that epigenetic regulation and gene expression in different regions of the brain of young mice have unexpected correlations, and that different epigenetic features can cooperate with each other to regulate gene expression. The integration of spatial multi-omics not only opens a new field of spatial omics but also provides novel research avenues for biological and biomedical research.

Applications of spatial-based technologies. Spatial multi-omics technology is employed to investigate various cell biology. This diagram provides an overview of the application of spatial multi-omics. A Spatial-based molecular and cellular atlas. B Spatial-based heterogeneity in human diseases. C Spatial-related crosstalk in tumor immunology. D Spatial trajectory and lineage tracking in human diseases. E Potential targets for therapeutic applications. F Reproduction and development research

Spatial multi-omics decodes spatial-based heterogeneity in human diseases

The complex interactions among tumor cells, surrounding tissues, infiltrating innate immune cells, and adaptive immune cells create a unique environment characterized by inter-related, coexisting, and competitive dynamics [ 108 ]. The characteristics of this tumor immune microenvironment vary significantly due to both intrinsic (e.g., tumor type) and extrinsic factors (e.g., environment). Tumor heterogeneity plays a crucial role in enabling tumor cells to adapt to changes in the microenvironment, thereby promoting tumor resistance and progression (Fig.  3 B).

Tumor heterogeneity includes both intratumor and intertumor heterogeneity [ 109 ]. Metastatic prostate cancer exhibits a wide spectrum of diverse phenotypes, but the extent of these heterogeneities has not yet been established [ 110 ]. Brady et al. integrated spatial transcriptomics and proteomics to analyze multiple discrete areas of metastases, discovering heterogeneity among tumors at different metastatic sites and within the same site. They also identified significant intra-patient heterogeneity in regions with varying androgen receptor (AR) and neuroendocrine activity. Most metastases lacked significant inflammatory infiltrates and PD1, PD-L1, and CTLA4 expression, while the B7-H3/CD276 immune checkpoint protein was highly expressed, particularly in metastatic prostate cancers with high AR activity [ 111 ]. These findings correlate with the clinical observation that metastatic prostate cancers often fail to respond to immune checkpoint blockade therapies such as anti-CTLA4, PD1, and PD-L1 antibodies, suggesting that B7-H3/CD276 could be a potential therapeutic target. Non-small cell lung cancer (NSCLC) is characterized by substantial heterogeneity among individual tumors and within regions of a single tumor [ 112 ]. Intratumor heterogeneity has been shown to contribute to treatment failure and drug resistance through the expansion of pre-existing resistant subclones [ 113 , 114 ]. Previous studies using multi-region profiling to decode the spatial patterns of heterogeneity were limited by the small number of regions analyzed per tumor [ 115 ]. Wu et al. employed multi-region matrix-assisted laser desorption ionization-time of flight (MALDI-TOF), cyclic immunofluorescence (CyCIF), and multi-region single-cell copy number sequencing to conduct spatial multi-omics analysis of tumors from 147 lung adenocarcinoma patients. They developed a novel analysis approach to quantify intratumor spatial heterogeneity: clustered geographic diversification (GD), where molecularly similar cells cluster together, and random GD, where molecularly similar cells are randomly distributed. Patients with random GD exhibited higher recurrence rates and risk of death, characterized by fewer tumor-interacting endothelial cells, higher infiltrating immune cells, and similar GD patterns observed in both proteomic and genomic data [ 116 ], providing insights into spatial heterogeneity and innovative ideas for cancer research. A non-targeted MALDI-MSI analysis [ 117 ] followed by spatial segmentation using different algorithms allowed to highlight molecular heterogeneity among glioblastomas. Three sub-regions were identified (A, B and C regions). Duhamel et al. performed a spatially resolved proteomic analysis to decode the biological pathways involved in these three regions: region A is enriched in genes related to neurotransmission and synaptogenesis; proteins overexpressed in region B are associated with immune infiltration; region C identified proteins involved in RNA processing and metabolism. Finally, they identified PPP1R12A and RPS14 are favorable prognostic markers while ALCAM, ANXA11, and AltProt IP_652563 are unfavorable prognostic markers [ 118 ]. These results highlight the potential of spatial proteomics and spatial metabolomics to decipher the molecular heterogeneity of glioblastoma and identify markers associated with survival.

Understanding how reprogrammed metabolic networks impact tumor growth is crucial for identifying metabolic vulnerabilities that improve cancer treatment. Sun et al. [ 119 ] combined mass spectrometry imaging-based spatial metabolomics and lipid-omics with microarray-based spatial transcriptomics [ 120 ] to visualize intratumor metabolic heterogeneity and cell metabolic interactions within the same gastric cancer sample. They imaged tumor-associated metabolic reprogramming at metabolic-transcriptional levels, linking marker metabolites, lipids, and genes within metabolic pathways and colocalizing them in heterogeneous cancer tissues. The integrated data revealed unique transcriptional features and significant immune-metabolic changes at the tumor invasion frontier. Furthermore, glutamine was overutilized in tumor tissue, genes related to lipids, fatty acid synthesis (FA), and fatty acid elongation were enriched in the tumor tissue region, and long chain polyunsaturated fatty acids were significantly up-regulated in borderline lymphoid tissue, even exceeding levels in tumor tissues [ 121 ]. These findings enhance our understanding of tumor molecular mechanisms and potential targets for cancer therapy. Spatial multi-omics technology accurately depicts gene expression in different tumor tissue locations, addressing the lack of spatial context in single-cell sequencing. Thus, the advancement of spatial omics provides essential support for exploring tumor immune microenvironment dynamics and identifying corresponding therapeutic targets.

Novel insights from spatial multi-omics analyze spatial-related crosstalk in tumor immunology

Spatial multi-omics has provided new perspectives on the complex interactions within the tumor microenvironment. Tumor tissue comprises various cell types, including epithelial, endothelial, fibroblast, vascular smooth muscle, resident immune, and infiltrating immune cells, all of which interact within a 3D environment to support cancer cell growth [ 122 ] (Fig.  3 C). By integrating mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics, researchers have identified a distinct interface at the junction of tumors and neighboring tissues, termed cluster9, within which peritumoral lymphoid tissue (PLT) and distal lymphoid tissue (DLT) are defined [ 121 ]. The PLT exhibits significantly increased uptake and metabolism of glutamine, as well as certain fatty acids, essential for tumor energy metabolism and signaling. Genes associated with fatty acid synthesis, such as FASN, SCD, and ELOVL, as well as ALOX5AP, which promotes arachidonic acid metabolism into leukotriene inflammatory mediators, are also up-regulated in PLT. These results suggest that PLT has a stronger inflammatory response than DLT and inhibits tumor cell proliferation [ 121 ]. Identification of this crosstalk between PLT and tumor cells has enhanced our understanding of tumor molecular mechanisms. ScRNA-seq studies on glioblastomas have highlighted the dynamic plasticity across cellular states [ 123 ], including mesenchymal-like (MES-like), neural progenitor cell-like (NPC-like), astrocyte-like (AC-like), and oligodendrocytic precursor cell-like (OPC-like) states, which are markers of malignant brain tumors [ 124 ]. However, single-cell analysis provides only indirect inferences about cell interactions, often neglecting the role of the local microenvironment in tumorigenesis. Ravi et al. [ 125 ] utilized spatial transcriptomics, metabolomics, and proteomics to quantify the relationship between tumor cells and myeloid and lymphoid cells, discovering increased interactions in inflammation-related gene-rich areas and confirming enhanced interactions between tumor cells and virus-free compartments within transcriptionally defined reactive immune regions. Annika et al. [ 126 ] combined spatial multi-omics and scRNA-seq data from epithelial and stromal compartments to examine immune cell composition during intestinal damage and regeneration, finding that activated B cells decreased and disrupted the essential crosstalk between stromal and epithelial cells during mucosal healing. Spatiotemporal multi-omics allows for consideration of the microenvironment in cell–cell crosstalk studies, enhancing the accuracy of research findings.

Spatial trajectory and lineage tracking in human diseases

Lineage tracking technology is crucial for studying the developmental trajectory and differentiation process of cells (Fig.  3 D). This technology can help determine how individual cells differentiate from a founder cell and how they evolve during development and disease [ 127 ]. Traditionally, lineage tracing involves labeling cells with heritable marks and tracking the trajectory of their offspring. The diversity of cell types produced from a founder cell reflects its differentiation potential. To predict the potential and evolutionary trajectory of founder cells, a wide array of markers is needed for accurate cell type classification. However, the limited availability of markers can mask the variability within cell subsets expressing the selected marker genes [ 128 ], potentially biasing the interpretation of organ complexity. Spatial transcriptomics not only enables comprehensive transcriptomic analysis of thousands of cells but also offers considerable insights into the spatiotemporal relationships among cells. This approach enhances cell-type identification, deepening our understanding of organizational complexity [ 129 ]. By constructing transcriptional atlases of adult tissues and developing embryos, spatial transcriptomics reveals the molecular mechanisms underlying differentiation from stem cells to mature cells. This detailed record elucidates the sequence of events and molecular mechanisms by which cells attain their final identity in embryogenesis or tissue regeneration. It also provides clues to the origins of developmental pathologies and cancer, allowing intervention in pathogenic pathways and replication of cell differentiation processes in vitro [ 130 ]. Densely sampling cells at various stages can describe state manifolds, which visualize the continuum of cell state changes in a multidimensional space and the trajectory of cell differentiation. To understand the instantaneous state of the cell, it is necessary to consider its molecular composition, inter-relationships, tissue position, and physical and regulatory interactions with surrounding cells. This comprehensive approach provides deeper insights into the state and function of cells [ 130 ]. Given the complexity of cells within different species, lineage tracing has expanded to include additional approaches, such as tracer dyes, cell transplantation, and in vivo genetic recombination. Advances in confocal and light-sheet microscopy have enabled the direct tracking of individual cell division patterns in complex vertebrates. However, these methods are limited to only a few measurements of cell state. Recent spatial transcriptomics approaches overcome spectral limitations by allowing genome-scale measurements in fixed in situ samples. High-throughput sequencing employs DNA sequence barcodes to encode clonal information, which can later be read and integrated with other sequence-based omics data. Zhang et al. applied single-cell and spatial transcriptomics to demonstrate extensive diversification of cells from a few multipotent progenitors to numerous differentiated cell states, including several novel cell populations. Furthermore, they identified lineage-specific clusters radiating from the center of six mesenchymal states and active transcription factor network modules associated with the progression of each lineage. They also observed that chondrocyte lineages increased over time, shifting from progenitor cells to more mature clusters [ 131 ]. Bao et al. [ 132 ] revealed that microglia and perivascular macrophages exhibit parallel differentiation processes, although the developmental origins of other tissue-resident macrophages require further exploration using single-cell and spatial transcriptomics. Spatial multi-omics have been applied in several fields, such as tumor progression, immune-associated diseases and metabolism-related disorders. Renal fibrosis, a critical pathological feature in chronic kidney disease progression, has significant global health implications. Spatial multi-omics techniques, such as Cut&Tag with DBiT-Seq [ 133 ], have been crucial in elucidating the complex epigenetic reprogramming during the transition from acute kidney injury to chronic kidney disease, underscoring the importance of multi-omics in understanding and addressing renal fibrosis pathogenesis [ 134 ]. The integration of imaging and sequencing-based omics has led to significant progress in spatial technologies, enabling spatially resolved single-cell detection [ 135 ]. These technologies preserve spatial resolution and large fields of view, allowing for detailed analysis of the microenvironment, spatial neighborhoods, and niche networks in kidney injury. Compatibility with formalin-fixed, paraffin-embedded tissue also facilitates the establishment of kidney injury cohorts, filling a critical gap in prognostic research [ 136 ].

Investigation of new therapies via spatial multi-omics

Targeting nucleotide metabolism is a well-established metabolic therapy in clinical oncology and practice [ 137 ]. However, efforts to target non-nucleotide metabolism in clinical trials have faced challenges due to drug toxicity, inconsistent dietary interventions, lack of biomarkers, and imprecise combination treatments, collectively leading to suboptimal trial outcomes. Additionally, cells within the TME can significantly influence treatment efficacy and undergo substantial changes during tumor progression and treatment response [ 138 ]. Therefore, developing biomarker-guided personalized precision metabolic therapies and targeted metabolic reprogramming is critical to improve the sensitivity of cancer therapy. Rational combinations of chemotherapy, radiation therapy, and other targeted therapies should also be considered. Integrating spatial multi-omics could enhance our understanding of tumor metabolic regulation, offering new therapeutic targets and identifying diagnostic and prognostic markers for various diseases.

Through multi-omics analysis of patients with triple-negative breast cancer (TNBC), researchers discovered that Clostridiales and the associated metabolite trimethylamine N-oxide (TMAO) induce pyroptosis in tumor cells by activating the endoplasmic reticulum stress kinase PERK, which amplifies CD8 + T cell-mediated antitumor immunity in vivo. These findings suggest that microbial metabolites, such as TMAO or its precursor choline, could serve as a new therapeutic strategy to enhance the efficacy of TNBC treatment [ 139 ], offering insights into the crosstalk between microbiota and metabolite immunology. Metastasis remains the leading cause of death in patients with breast cancer; however, the dynamic changes in dissemination evolution remain poorly understood. High-resolution technologies, such as spatial transcriptomics and metabolomics, have been used to map the metabolic landscape. Combined spatial transcriptomics and scRNA-seq have revealed metabolic changes in tumor cells during their transition from the primary site to the leading edge and metastatic lymph nodes, highlighting the potential of incorporating metabolic therapies in treating breast cancer with lymph node metastasis [ 140 ]. Eclipta prostrata L. [ 141 ] has long been used in traditional medicine for its liver-protective properties. Wedelolactone (WEL) and demethylwedelolactone (DWEL) are the primary coumarins found in E. prostrata L. Using a mature thioacetamide (TAA)-induced zebrafish model, Chen et al. integrated spatial metabolomics and transcriptomics and discovered that both WEL and DWEL can improve metabolic disorders induced by nonalcoholic fatty liver disease (NAFLD), primarily through the regulatory effects of WEL on steroid biosynthesis and fatty acid metabolism. Their study successfully mapped the biological distribution and metabolic characteristics of these compounds in zebrafish, revealing the unique mechanisms of WEL and DWEL in improving NAFLD and proposing a multi-omics platform to develop highly effective compounds that improve therapeutic outcomes [ 142 ]. Previous studies have highlighted the role of ferroptosis in a variety of neurological diseases [ 143 ], although its precise role in multiple sclerosis (MS) remained uncertain. Wu et al. integrated data from snRNA‐seq, spatial transcriptomics, and spatial proteomics to define a computational metric of ferroptosis levels and identify the ferroptosis landscape in neuroimmunity and neurodegeneration in MS patients [ 144 ]. Results showed that active lesion edges exhibited the highest ferroptosis scores, associated with phagocyte system activation, while remyelination lesions had the lowest scores. Elevated ferroptosis scores were also observed in cortical neurons, linked to multiple neurodegenerative disease-related pathways [ 144 ], while significant co-localization was detected between ferroptosis scores, neurodegeneration, and microglia. They also established a diagnostic model for MS based on 24 ferroptosis-related genes in peripheral blood. These findings suggest that ferroptosis may play a dual role in MS, associated with both neuroimmunological and neurodegenerative processes, making it a promising therapeutic target and diagnostic marker for MS. Vedolizumab (VDZ) is known to inhibit lymphocyte trafficking to the intestine and is effective in treating ulcerative colitis (UC). However, its broader effects on other cell subsets are less understood. Using comprehensive spatial transcriptomic and proteomic phenotyping, Mennillo et al. identified mononuclear phagocytes as an important cell type impacted by anti-integrin therapy in UC and revealed changes in the spatial distribution of cell subpopulations in tissues before and after VDZ treatment [ 145 ]. Notably, they highlighted the cellular and genetic factors of UC and VDZ therapy, potentially aiding in the development of more precise treatment strategies and the prediction of treatment responses (Fig.  3 E).

Multi-omics in reproduction and development research

Mammalian fertilization begins with the fusion of an oocyte and a sperm cell [ 146 ], with the reproductive system creating an environment for embryonic development (Fig.  3 F). In-depth exploration of the reproductive system requires an understanding of the function of each cell type and their interactions. Spatial multi-omics techniques have been used to examine interactions between adjacent cells and gametes or embryos within the natural tissue environment, preserving the spatial context of the analyzed cells. These technologies have the potential to transform our understanding of mammalian reproduction [ 147 ]. Winkler et al. used scRNA-seq and spatial transcriptomics to profile the remodeling of the female reproductive tract during the estrous cycle, decidualization, and aging and discovered that fibroblasts play a central and organ-specific role in female reproductive tract remodeling by coordinating extracellular matrix (ECM) recombination and inflammation. They also revealed the unexpected costs of repeated remodeling required during reproduction and illustrated how estrus, pregnancy, and aging collectively shape the female reproductive tract [ 148 ]. Yang et al. conducted scRNA-seq, scATAC-seq, and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13–18, generating a large-scale multi-omics atlas of the developing human fetal cerebellum. They found that PARM1 exhibits inconsistent distribution in human and mouse granulosa cells, and identified gene regulatory networks that control the diversity of Purkinje cells and unipolar brush cells [ 149 ]. These key regulatory factors can be harnessed in vitro to generate small brain cells for future clinical applications and enhance our understanding of the link between molecular variation and cell types in neurodevelopmental disorders. Li et al. employed scRNA-seq, spatial transcriptomics, and hybridization-based in situ sequencing to analyze 16 human embryonic and fetal spinal cord samples from post-conceptional weeks 5–12, providing a comprehensive atlas of developmental cells and identifying novel molecular targets and genetic regulation of childhood spinal cancer stem cells [ 150 ] (Table  3 ).

Perspectives

The rapidly evolving field of spatial omics technologies aims to achieve higher resolution, deeper coverage, greater multiplexity, and enhanced versatility in analyzing diverse samples, including formalin-fixed, paraffin-embedded, fresh-frozen, and living tissues. These advancements enable 3D reconstruction of larger tissue regions and comprehensive analysis of spatiotemporal multi-omics, enhancing our understanding of the complex molecular mechanisms underlying cellular interactions within tissues. Effective acquisition, manipulation, analysis, and visualization of spatial omics data are critical components for their successful application. Integrating datasets from different omics modalities is essential to unlock their synergistic potential, although this is challenging due to differing spatial features of the data. Consequently, there is an urgent need for specialized hardware and software to visualize these complex datasets effectively. Key steps include normalizing data matrices, removing low-quality data, improving signal-to-noise ratios, smoothing data to increase sensitivity, and eliminating unwanted technical and biological variations. Developing an independent benchmark of spatial omics integration algorithms should greatly assist researchers in selecting appropriate integration strategies and designing experiments. Without suitable analytical tools, even costly experiments can yield unusable data. To mitigate bias, the scientific community must provide open datasets for comparative analysis of tissues and develop novel methods for accurate detection or capture efficiency. The path to widespread adoption of these technologies remains long. A thorough understanding of the cellular and molecular mechanisms within specific normal or pathogenic microenvironments is crucial for advancing personalized precision medicine. This approach is anticipated to become the primary treatment option in the near future. Expected advancements include increased throughput, reduced costs, integration of more detection modes, and enhanced sensitivity and specificity. Ultimately, multi-omics techniques with spatial single-cell resolution will revolutionize our understanding of cell biology.

Conclusions

The integration of multi-omics with spatial analysis is a rapidly evolving field that holds great promise for a wide range of applications. Spatial multi-omics enables a deeper understanding of complex biological systems, providing novel insights into disease mechanisms, drug target identification, and biomarker discovery. However, integrating multi-omics data presents technical challenges, necessitating advanced computational and statistical methods. Moreover, the interpretation of spatial multi-omics data is further complicated by spatially varying environmental factors and technical noise. Thus, the development of sophisticated computational tools and analytical methods capable of managing large-scale spatial multi-omics datasets is essential for fully leveraging the potential of this approach.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Liu, X., Peng, T., Xu, M. et al. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol 17 , 72 (2024). https://doi.org/10.1186/s13045-024-01596-9

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Fungal diversity and key functional gene abundance in Iowa bioretention cells: implications for stormwater remediation potential †

First published on 20th August 2024

Stormwater bioretention cells are green stormwater infrastructure systems that can help mitigate flooding and remove contaminants. Plants and bacteria improve nutrient removal and degrade organic contaminants; however, the roles of fungi in bioretention cells are less known. Although mycorrhizal fungi aid in plant growth/improve nutrient uptake, there is a notable lack of research investigating fungal diversity in bioretention cells. Other types of fungi could benefit bioretention cells ( e.g. , white rot fungi degrade recalcitrant contaminants). This study addresses the knowledge gap of fungal function and diversity within stormwater bioretention cells. We collected multiple soil samples from 27 different bioretention cells in temperate-climate eastern Iowa USA, characterized soil physicochemical parameters, sequenced the internal transcribed spacer (ITS) amplicon to identify fungal taxa from extracted DNA, and measured functional gene abundances for two fungal laccases ( Cu1 , Cu1A ) and a fungal nitrite reductase gene ( nirKf ). Fungal biodegradation functional genes were present in bioretention soils (mean copies per g: 7.4 × 10 5 nirKf , 3.2 × 10 6 Cu1 , 4.0 × 10 8 Cu1A ), with abundance of fungal laccase and fungal nitrite reductase genes significantly positively correlated with soil pH and organic matter (Pearson's R : >0.39; rho < 0.05). PERMANOVA analysis determined soil characteristics were not significant explanatory variables for community composition (beta diversity). In contrast, planting specifications significantly impacted fungal diversity; the presence/absence of a few planting types and predominant vegetation type in the cell explained 89% of variation in fungal diversity. These findings further emphasize the importance of plants and media as key design parameters for bioretention cells, with implications for fungal bioremediation of captured stormwater contaminants.

1. Introduction

To mitigate limitations from abiotic removal mechanisms, maximized contaminant sorption/filtration can be coupled with biotic uptake and transformation processes in microbes and plants. Plant selection is important for functioning bioretention cells; plants with deeper rooting zones can prevent clogging and improve infiltration. 16 Phosphorus and nitrogen removal can also improve in planted systems, though efficacy may depend on plant type. 17 Increasing evidence demonstrates plants can impact trace organic contaminant removal in bioretention. For example, plants can remove and transform benzotriazole hydroponically, 18 and benzotriazole phytotransformation products were detected in bench-scale bioretention mesocosms 19 as well as a field-scale tracer study. 20 In addition to plants, organisms such as bacteria and fungi also affect contaminant fate within bioretention cells. Bacteria have mostly been studied in context of nutrients within bioretention cells but have also demonstrated contributions to organic contaminant removal. One recent study suggests biochar-amended bioretention cells, with the contributions of active microbial biofilms, could extend the lifetime of the system by 1.8–2.3 times and enhance removal of trace contaminants like atrazine and neonicotinoids. 21

Although there is growing evidence on the roles of plants and bacteria within bioretention cells, fungi have received very limited consideration. Within these limited studies, mycorrhizal fungi have garnered the most attention, as they associate with and often provide benefits to plants such as nutrients and protection against pathogens. 22,23 Mycorrhizal fungi have been observed in bioretention cells, 24 and initial studies indicate functional improvements such as increased plant growth and stress resistance, improved nutrient uptake and heavy metal removal. 25–27 Beyond mycorrhizal fungi, fungal enzymes such as lignin peroxidases or laccases could enhance biotransformation of contaminants; indeed, many of these enzymes are not substrate-specific and have widely demonstrated the break-down of recalcitrant contaminants such as polychlorinated biphenyls, 28,29 carbamazepine, 30,31 and azo dyes. 32 Despite the potential of fungi to enhance bioretention function, we found very few studies reporting on fungal diversity within green stormwater infrastructure bioswales. These initial findings indicated fungal communities in bioswales (bioswales serve similar functions to bioretention cells) were distinct from park/tree pit soils, likely influenced by differences in micro-environment/habitat, 33 and that community differences were correlated with plant species differences. 34

Though emerging studies on microbial communities in green stormwater infrastructure provide important initial insights on fungal diversity, more work is needed to understand the role of fungi in green stormwater infrastructure, particularly bioretention cells. The single study probing the connection between plant species/physiology and microbial community differences focused on plant transpiration rates as the relevant physiological trait. Other traits, such as rooting depth, or non-plant variables such as runoff type ( e.g. , parking lot vs. road), remain unexplored. Abundance of relevant bacterial biotransformation genes such as denitrification genes, 35,36 bphA , 37 and naphthalene degradation genes 38,39 have been quantified within bioretention cells or mesocosms, but no studies have specifically investigated relevant fungal biotransformation genes such as laccases and peroxidases in bioretention cells. Thus, there is a paucity of information on fungal communities and function within bioretention cells and how fungi (native or bioaugmented) could enhance removal of dissolved organic contaminants. To address these knowledge gaps, we measured fungal diversity and key fungal functional gene abundance in 27 different bioretention cells in eastern Iowa (in the temperate Midwestern United States). The objective of this study was to determine driving factors of fungal diversity and functional gene abundance in representative Iowa bioretention cells. We hypothesized that bioretention plant rooting depth and soil characteristics would significantly influence fungal diversity and functional gene abundance.

2.1 Bioretention cell soil sampling

2.2 soil dna extraction and sequencing, 2.3 bioinformatics pipeline, 2.4 statistical analysis for sequencing data.

To test relationships between site characteristic variables and the beta diversity distance matrix, permutational multivariate analysis of variance (PERMANOVA) was used. PERMANOVA is a non-parametric method of hypothesis testing for multivariate variation that can be applied to distance matrices such as the rCLR-transformed counts table with the Aitchison distance. 52 The PERMANOVA test assumes sample “exchangeability” (data can be rearranged without changing the overall distribution), and random permutations of the data are compared to determine if the centroid of the random permutations are equivalent. The null hypothesis of the test states that there are no differences in the positioning of the centroids when comparing the random permutations of the data to the distance matrix. 46 PERMANOVA is considered a powerful statistical tool for microbiome and ecology data, as data do not need to be normally distributed. However, PERMANOVA can be affected by uneven/unbalanced sampling for groups (which applied for many of our site characteristics). To account for this, we conducted tests for homogeneity of multivariate dispersions (PERMDISP). 52 Soil characteristics, planting variables, and physical location were analyzed using PERMANOVA (adonis2 function in the R ‘vegan’ package). 53 Soil characteristics (continuous variables) and planting/site categorical variables were analyzed in separate PERMANOVA analyses.

2.5 Fungal functional gene analysis

3. results and discussion, 3.1 fungal diversity in bioretention cells.

Results from PERMANOVA analysis ( Table 1 ) provide insight into the variables that influence differences in community composition (beta diversity). Soil characteristics were not significant explanatory variables for beta diversity. Rather, a few key planting variables explained 89% of the variability in the distance matrix: the presence of native grasses ( R 2 = 0.43), the presence of forbs ( R 2 = 0.12), and the type of plant determined to be predominant in the cell ( R 2 = 0.24) based on sampling field notes and site images. Presence/absence of different plants were coded as Boolean variables. For example, if native grasses such as switchgrass ( Panicum virgatum ) were recorded as present within the cell and confirmed in pictures, presence of native grasses was coded as TRUE (otherwise coded as FALSE.) Presence/absence variables do not necessarily indicate the plant is dominant, but simply that it was growing within the cell. In contrast, the type of plant determined to be “dominant” in a cell was based on the abundance of the plant within a cell; for example, if the cell consisted of mostly Black-eyed Susan ( Rudbeckia fulgida ) and other forbs, the dominant planting type was classified as “forb”. Some cells had multiple planting types that could be considered dominant (Fig. S.2 † ) and were classified as “mixed”. PERMDISP analyses of each of the statistically significant PERMANOVA variables indicate homogeneous dispersions within groups for the presence of forbs variable and the dominant planting variable, but not for the native grasses nor legumes variables (Table S.11 and Fig. S.15–S.18 † for PERMDISP results). For the native grasses/legumes variables, the significant results from the PERMDISP indicate that effects from within group dispersion (variances) could be confounding the results from the PERMANOVA; in essence, although the PERMANOVA is not ‘invalid’ per se , caution must be taken when interpreting results. The statistically significant PERMDISP implicates that within-group variance is also driving some of the differences seen in beta diversity and not solely the effect from presence of native grasses. These within-group variances are further explored using other visualizations (Fig. S.19 and S.20 † ). For the other two variables, the results of the PERMDISP further confirm the appropriateness of the PERMANOVA test, and within-group variances do not need to be considered when evaluating effects on beta diversity from the variables.

In the visualization of the PCA for the distance matrix, we colored points by the variables explaining the most variation: type of dominant planting, presence of native grasses, and presence of forbs ( Fig. 1 ). Legumes, while a statistically significant variable from the PERMANOVA model, were excluded from the PCA analysis due to the low R 2 (0.04). The PCA ordination explained 79% of the variance in PC1, and 21% of the variance in PC2. The high values of variance explained is not uncommon for this type of ordination in microbiome datasets. 43,51 When examining the PCA for the native grasses, we can consider the results from both the PERMANOVA and PERMDISP tests ( i.e. , visually check for differences in within-group variation). The cells lacking native grasses visually appear to have heterogenous dispersion (spread unevenly within the group); given the significance of other planting variables, we posit that other planting types within the cells with no native grasses could be influencing results, in addition to the effect of not having native grasses. Within the six sites with no native grasses present, there were three within the group that are turf grass cells, two that were dominated by legumes, and one that had mostly weeds but legumes present. These planting types are physiologically different, with turf grass having short rooting depths and less transpiration than legumes. These differences could explain the significant result of the PERMDISP for the presence of native grasses.

The cluster dendrogram ( Fig. 2 ) based on the beta diversity rCLR-transformed Aitchison distance matrix is another visualization method for relatedness between sampling locations. The two North Liberty cells appear to be distinct, exhibiting a high branch height and clustering separately from all other samples. These two cells were the only cells with legumes as the dominant planting. Given the small sample size for this dominant planting group, it is challenging to attribute the difference to just one variable and should be considered as an area for future research. In contrast to the legume-dominant cells, the cells sampled from Cedar Rapids do not all cluster together, despite having some similarities in plantings within those sampling locations. Another interesting separation on the dendrogram is the “cvcell14top” vs. “cvcell14bottom” sample. At this cell, we sampled at two different depths in the soil cores – one at the top 10 cm and one at the bottom 10 cm of the corer. Though the sample size for depth is limited, this preliminary indication of difference with depth suggests that soil depth could be another variable of interest.

Altogether, the strong contributions from the planting categorical variables strengthens the evidence from Gill et al. 33 who reported that differences in micro-environment drove community differences. In a separate study on influence of plant transpiration rates on fungal/bacterial diversity in bioswales, Brodsky et al. 34 found that plants with higher transpiration rates were associated with higher fungal and bacterial diversity. Though planting categorical variables appear to explain much of the variation in the data for our study, we cannot rule out the possibility that further variability could be explained by site-specific characteristics not considered here, such as redox conditions, presence of a saturated zone, size of the cell/runoff volume, specific pollutant loading, cell age, cell maintenance strategies/frequency, etc. We also note that certain fungi are likely excluded from this study based on the sampling design. Examples of excluded fungi are endomycorrhizal fungi and plant pathogens that would inhabit within the vegetation of the bioretention cells, because only soil samples were analyzed. Additionally, fungal community variation with depth was not studied, as soil samples were collected near the subsurface of the cell, excepting cell 14. This limitation could exclude fungi that can survive under low oxygen conditions. Thus, while subsurface soil diversity was studied, more studies may be needed to further understand fungal diversity in bioretention cells.

Plots of mean relative abundance data ( Fig. 3 , S.19 and 20 † ) provide another way to compare the fungal communities between our study and Gill et al. 's and insight into taxonomic information. One key difference is the abundance of Ascomycota (a diverse phylum with the largest number of fungal species); in Gill et al. 's study, Ascomycota represent over 50% of the fungal sequences in all sample types. 33 A different study on microbiomes of urban greenspaces (non-engineered soils) had 40–50% abundance for Ascomycota . 57 Surprisingly, Ascomycota abundance did not fall within the 40–50% range for all samples. Only forb- and turf-dominant cells exhibit close to 50% abundance of Ascomycota , with abundance at or below 30% in legume-, trees/shrubs-, and weed-dominant cells. Another unexpected result is the high percentage of Basidiomycota relative to Ascomycota within our samples, especially in the weed-dominant cells. Basidiomycota include a multitude of fungal taxa that fall on the “white/brown rot continuum”. 58 These fungal taxa could aid in contaminant removal within bioretention cells, as demonstrated in our previous work. 59

The distribution of other phyla is also notable. Interestingly, the early-diverging phylum, Aphelidiomycota , was detected with a relative abundance around 2% in forb-, legume-, and native grass-dominant samples. Aphelidiomycota , once considered protists and/or a sister group to fungi, parasitize algae and diatoms and may have a role in leaf litter decomposition. 60,61 The majority of fungi with an arbuscular mycorrhizal lifestyle are found in the Glomeromycota phylum. Within our samples, Glomeromycota were scarce in most samples, except in cells with trees/shrubs (5% abundance), and were only detected at or above 1% in samples with dominant plantings of trees/shrubs, turf, and weeds. Surprisingly, the two cells with dominant legumes (the North Liberty cells that also grouped separately in the dendrogram) had negligible Glomeromycota relative abundance, despite evidence that legumes host higher arbuscular mycorrhizal diversity relative to some native grass species. 62 Indeed, the opposite was true within our results. Thus, there is a possible need for mycorrhizal inoculation within bioretention cells to enhance benefits from mycorrhizal fungi, as well as a need to further investigate the presence of mycorrhizal fungi in legume-dominant bioretention cells.

3.2 Functional gene quantification

Soil characteristics correlated with functional gene abundance in surprising ways ( Fig. 5 ). Despite the involvement of fungal nirK in the nitrogen cycle, neither soil nitrate nor soil ammonium was significantly or strongly correlated with nirKf gene abundance. Somewhat similarly, none of the functional gene abundances were correlated with soil copper content, despite both laccase and nirKf being copper-containing enzymes. All functional genes were significantly positively correlated ( p < 0.05) with soil pH and percent organic matter. Additionally, we found that percent organic matter (OM) and pH result in statistically significant differences in gene abundance for each functional gene (Fig. S.24; † note that OM/pH graphs would be equivalent and only one is depicted, as the samples group in the same manner for both variables). When we compared functional gene abundance for samples with percent OM less than the median to samples with percent OM greater than the median, we found statistically significant differences between the means, as indicated by the 95% confidence intervals excluding zero for all functional genes. We posit that the correlation between functional genes and percent organic matter as well as the differences in gene abundances with percent OM are likely because higher organic matter could result in a higher overall fungal richness. 66 Indeed, based on the correlation matrix, it appears that percent OM and pH are driving soil characteristics for fungal species carrying these functional genes. These findings in stormwater bioretention cells are consistent with related literature from the fields of mycology/soil science. For example, soil fungal communities in the natural environment are known to vary with depth and organic matter content, 67,68 with greater richness and diversity in the organic horizon 69 that decreases with depth. 70,71 Additionally, soil pH has been shown to affect fungal abundance and composition, e.g. , resulting in changes in nutrient cycling through stimulated heterotrophic nitrification. 72 Nevertheless, we caveat the interpretation of pH with the results in that pH values were generally circumneutral (mean of 7.4 ± 0.06); thus large deviations in soil pH are not expected, whereas percent organic matter can vary depending on the proportion of compost added to the geomedia. In the context of Spearman's correlations between functional gene abundance and site characteristic categorical variables were generally not statistically significant (Fig. S.25 † ); Cu1 was weakly negatively correlated with the presence of forbs ( p = 0.044, rho = −0.383) and nirKf was weakly positively correlated with smaller-scale local geography ( p = 0.042, rho = 0.388). Local geography was categorized based on within-city areas ( e.g. , “Iowa River Landing”) whereas geography was categorized based on city. It is possible that the fungi represented by the selected functional genes are widespread within bioretention cells, which could explain the weak correlation with planting variables and significant correlation with soil organic matter and pH.

Our work demonstrated the utility of measuring fungal functional genes in bioretention cells. Pairing functional gene abundance measurements with fungal sequencing allows for a quantitative metric of key processes along with knowledge of the fungal community. One challenge of using DNA extracts from soil is that there is a possibility for detection of “relic” DNA, or DNA from organisms no longer alive. 73 This could be potentially problematic if a study's goal is to capture a fully representative picture of bioretention function. An alternative could be RNA extraction, although this process has substantial methodological challenges. Nonetheless, qPCR is a useful method to understand how bioretention design parameters ( e.g. , amount of compost addition) enhances or reduces desirable biotic functions such as contaminant transformation. Future studies could investigate impacts from bioretention saturated zones, further optimize qPCR primer specificity, and target a wider range of fungal functional genes. Altogether, our study increases understanding of factors influencing fungal diversity and functional gene abundance in bioretention cells and revealed fruitful areas of further research.

In a broader context, there is only limited existing work investigating the role of microbial (and even less on fungal) diversity or functional genes and their role in green stormwater infrastructure (GSI). Several papers studying GSI practices in New York City have characterized microbial community diversity in vegetated swales, right-of-way bioswales, tree pits, and other practices. In one investigation, 37 bacterial communities in engineered soils were compositionally distinct from non-engineered soils/not-engineered site. Specifically, bioswales exhibited distinct, phylogenetically diverse communities, including taxa associated with nutrient cycling and contaminants biodegradation. Bioswale soils also had a significantly greater diversity of genes involved in several functional pathways, including carbon and nitrogen cycling, as well as for degradation of a diverse suite of contaminants ( e.g. , noxZ and amoA for N cycling; bphA and monooxygenases for contaminant biodegradation). GSI design had significant impacts on measured microbial variables. 74 Nevertheless, a subsequent NYC study indicated bacterial communities in GSI had levels of diversity similar to nonurban soils. 75 A study of microbial (bacteria, archaea) and fungal diversity of urban greenspaces in France 76 reported fungal richness increased in urban agriculture soils while bacterial richness was lower in public leisure areas. Similar to our findings, trace metals nor PAH contents in their study explained variations in microbial communities; organic carbon and C/N best predicted overall biomass. A study in the southwest US studying divergence of microbial communities in GSI soils concluded that diversifying vegetation can lead to positive feedback cycles between plants and microbes. 77 These positive feedback cycles can result in more resilient systems and higher carbon storage, strengthening evidence for the importance of vegetation–microbe relationships in GSI. Indeed, a recent study on greenroofs 78 in the Midwestern US reported greenroof fungal community composition was distinct between their nearby ground areas and between cities, positively correlated with plant cover. The overall paucity of work characterizing fungal (and even bacterial) communities in GSI practices represents a critical need—especially in the context of potential to bioremediation captured stormwater contaminants 37–39,59 —and warrants further investigation.

4. Conclusions

Our work also demonstrates potential for in situ fungal biotransformation within GSI practices. We noted the unexpectedly high representation of Basidiomycota within our samples, a fungal taxa that fall on the “white/brown rot continuum”. 58 These fungal taxa could aid in contaminant removal within bioretention cells, as demonstrated in our previous work 59 that white-rot fungi biodegrade toxic tire-wear compounds. White-rot fungi are able to biodegrade a wide suite of other recalcitrant organic contaminants that may be present in stormwater ( e.g. , some pesticides 80–82 ) and may otherwise accumulate in bioretention cells. Indeed, a recent study revealed that more recalcitrant, higher molecular weight PAHs were accumulating at the mid-depth below the surface of a bioretention cell where biodegradation may be limited, despite presence of PAH-ring cleaving dioxygenase bacterial genes at the surface. 15 This suggests potential utility for in situ fungal bioremediation in GSI, and indicates value in investigating fungal bioaugmentation/inoculation opportunities of native/beneficial bioremediating fungi. 83 Another area for further exploration would be the impact of bioretention geomedia amendments to fungal diversity in bioretention cells, as the addition of biochar can impact beta diversity of fungi due to alterations in soil characteristics. 84 With growing interest in synergizing coupled contaminant sorption to geomedia with subsequent biological degradation in stormwater systems, 21,83 probing the relationships between fungi and bioretention geomedia will be critical. Our novel reporting of fungal denitrification genes in bioretention systems also points towards a possible role of fungi for enhanced nutrient cycling in GSI; there is growing interest in using passive treatment systems for non-point nitrogen pollution control 85–88 ( e.g. , bioretention, woodchip bioreactors). Overall, our work points to great value in further understanding the fungal communities of GSI and potential for improved contaminant degradation.

Data availability

Conflicts of interest, acknowledgements.

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(page 110) p. 110 Conclusion: the varied concepts of the gene

• Published: September 2014
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There are many concepts of the gene. They range from defined sequences of DNA encoding proteins, to variant genes distinguishing individuals (markers), to unknown genes controlling quantitative traits, to hypothetical entities controlling behaviour as well as other complex characteristics. The science of genes is at its most precise and reliable when dealing with known protein coding genes. But all of the different concepts of the gene have been and continue to be important in numerous areas of human thought.

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How to Write a Personal Narrative: A Step-by-Step Guide

“As I sat down to write this article, memories flooded back, each one a brushstroke in the painting of my past…”

That could be the beginning of your personal narrative. Writing it lets you turn your memories and experiences into stories that click with others. This type of writing goes beyond school assignments or essays for college applications; it’s a chance to get really good at sharing your life's events in ways that matter. 

In this article, we're going to explore what personal narratives are all about and guide you through a simple seven-step process to create your own. You’ll learn how to pull out moments that make your story stand out and how to tweak your writing until it’s just right. We’ve got practical examples for you to follow along, making sure you have everything you need to tell your story. 

What is a Personal Narrative?

A personal narrative is a way to tell your own story. It's a style of writing that puts your experiences front and center, inviting readers into your world. Teachers often assign personal narratives to encourage free, expressive writing. 

The personal narrative definition is wider than academic settings, though. . These narratives can also show potential employers who you are beyond your resume. At its core, writing a personal narrative is a form of storytelling, using a first-person perspective to bring real-life tales to life. Whether it's for a grade, a job, or just for fun, it's about getting your story out there.

Your Story, Perfected

Let our experts refine your personal narrative, making sure every detail shines and your story is both clear and impactful.

How to Write a Personal Narrative: Steps

In this section, we'll break down the process into manageable steps, starting with how to zero in on the right topic that speaks about who you are.

Step 1. Choosing a Personal Narrative Topic

The first step in crafting your personal narrative is picking the perfect topic. It should be something meaningful to you, something that has not just happened, but also shaped who you are or has a significant story behind it. Here’s how to frame your personal narrative ideas:

• Story Arc : Your narrative is like a mini-movie. Start with setting the scene, build up to the main event, and wrap up with a reflection. For example, if you’re writing about your first solo travel experience, begin with your initial feelings, describe the challenges you faced, and end with what you learned about yourself.
• Thematic Focus : Instead of moving through time, center your narrative around a central theme. Maybe it’s about resilience, and you could link different times you had to be resilient, ending with a major life challenge.
• A Day to Remember : Sometimes a single day can tell a lot about you. Pick a day that was particularly memorable and unpack it from start to finish. Maybe it was a seemingly ordinary day that brought unexpected lessons or joys.

Step 2. Working on Your Personal Narrative Outline

When putting together your personal narrative, starting with a solid outline can help keep your story on track. Here's how you can lay it all out:

• Introduction: Kick things off with a hook that grabs attention, like an intriguing question or a vivid snapshot of a key moment. Set the scene and introduce the main theme.
• Setting and Characters : Give a good sense of where your story is unfolding and who's involved. Paint a clear picture of the backdrop and the key people.
• Plot Development : Lay out the events in the order they happened, or group them around major themes. Build up to your main event, adding conflicts or challenges as you go.
• Climax : This is the high point of your story, where everything comes to a head. Make it a moment that has the most impact.
• Resolution : Wrap up the main storyline, showing how things settled down after the climax.
• Reflection : Spend some time reflecting on what happened. Share what you learned or how you changed because of the experience.

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Step 3. Writing the First Draft of Your Personal Narrative

Now let’s move on to the fun part! Don't worry about getting everything perfect right away — the first draft’s goal is to let your story flow naturally:

• Start with Your Hook: Revisit the introduction you outlined and flesh it out. Begin with the attention-grabbing sentence that will make readers want to continue.
• Let the Story Unfold: Follow your outline, but allow yourself some flexibility. As you write, new memories or details may come to mind. Embrace them! Think about what you saw, heard, and felt during these moments. Were you sitting in a sunlit room, listening to the hum of a busy street outside? Maybe you felt the chill of an autumn breeze? 
• Stay True to Your Voice: This is your story, so let your unique voice shine through. Whether you're humorous, reflective, or serious, maintain a consistent tone that feels authentically you. Keep the tone conversational and straightforward, as if you’re telling this story to a friend. 

Once the first personal narrative draft is done, set it aside for a bit before revisiting it with fresh eyes.

Step 4. Revising Your Personal Narrative

Once the first personal narrative draft is done, set it aside for a bit before revisiting it with fresh eyes:

• Tighten Up the Story : As you go through your draft, focus on making everything clear and to the point. If you’ve talked about how nervous you were before a big event more than once, try to combine those thoughts into one powerful sentence that really captures how you felt.
• Keep Your Tone Consistent : Make sure your voice stays the same throughout the story. If you start off with a casual, conversational tone, like saying, “I couldn’t shake the nerves before my big test,” stick with that style instead of suddenly becoming formal later on.
• Adjust the Pacing : Pay attention to how smoothly your story flows from one part to the next. When you’re describing a key moment, like meeting someone important or going through a major experience, give it the detail and time it deserves. Let those moments develop naturally without rushing.
• Enhance Your Descriptions : Make your imagery more vivid to help the reader visualize your story. For example, instead of just saying, “The room was noisy,” you could say, “The room buzzed with excited chatter.” These small tweaks can make your story feel more alive and engaging.

Step 5. Adding Personal Touches

As you polish your personal narrative, focus on making it uniquely yours. You can include personal reflections on your experiences. For example, if you’re writing about a challenging project, discuss not just the struggle but how it impacted you personally and professionally.

Besides, add unique details that only you can share. Instead of generic descriptions, use specific anecdotes or sensory details, like how the scent of freshly baked cookies from your grandmother's kitchen made you feel nostalgic.

Last but not least, incorporate dialogues or direct quotes from people involved in your story to add authenticity and depth. For instance, if your mentor gave you advice, include their exact words to capture the moment’s impact. This approach will help you understand how to write a personal narrative that is both engaging and deeply personal.

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Step 6: Editing for Clarity and Style

When you’re editing your personal narratives, the goal is to make sure everything flows smoothly and makes sense. Here’s how to get it just right:

• Clarify Your Message: Check for any parts of your story that might be a bit confusing. If you talked about being excited about a project and then suddenly shifted to its challenges, make sure to connect these thoughts clearly. For instance, you might rephrase it as “I was excited about the project, but I soon faced some unexpected challenges, like tight deadlines.”
• Simplify Complex Sentences: Break down long or complicated sentences. Instead of saying, “My enthusiasm for the project, which was incredibly high despite the difficulties I faced, was the driving force behind my perseverance,” you could simplify it to, “Even though the project was tough, my excitement kept me going.”
• Smooth Transitions: Check how your paragraphs and sections flow together. If you jump from describing a problem to the solution without a clear link, add a transition. For example, “After struggling with the project’s challenges, I realized that asking my mentor for help was the key to overcoming the obstacles.”

Oh, and read your narrative out loud. This can help you spot any awkward phrases or spots where the story might be a bit choppy. It’s a great way to catch any issues and make those final tweaks to get everything just right.

Personal Narrative Prompts

Here are ten personal narrative prompts to get you thinking about different moments in your life:

Need more tips on how to get started? Check out this guide on how to start a narrative essay to kick off your writing with a strong opening.

Personal Narrative Examples

Here are a few personal narrative beginnings to spark your creativity. These snippets are designed to get you started and inspire your own storytelling.

Wrapping Up

As you finish up your story, think about how those moments shaped who you are today. It's not just about what happened, but how it changed you. When learning how to write a personal narrative, it’s important to focus on the moments that truly matter to you and tell them in your own voice. This way, your narrative can really connect with others. 

Remember, the best stories come straight from the heart, so trust yourself and let your experiences shine through!

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Daniel Parker

is a seasoned educational writer focusing on scholarship guidance, research papers, and various forms of academic essays including reflective and narrative essays. His expertise also extends to detailed case studies. A scholar with a background in English Literature and Education, Daniel’s work on EssayPro blog aims to support students in achieving academic excellence and securing scholarships. His hobbies include reading classic literature and participating in academic forums.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

• The New York Times. (2020, January 7). Personal Narrative Essay Winners. The New York Times. https://www.nytimes.com/2020/01/07/learning/personal-narrative-essay-winners.html