Map details the changes in gene activity that occur as the embryo develops and differentiates into different cell types.

The researchers sequenced messenger RNA from about 150,000 embyronic mouse cells at key stages of development
Scientists have mapped out the key molecular changes that orchestrate how embryonic mouse cells differentiate into the diverse cell types that will ultimately form all the different tissues and organs of the adult animal.
This “roadmap” of mouse embryogenesis will help researchers understand the molecular programs that control how different specialized cell types emerge from less differentiated cells as an embryo grows and develops not only for mice, but also for humans, said Jay Shendure, professor of genome sciences at the University of Washington School of Medicine, scientific director of the Brotman Baty Institute for Precision Medicine and a Howard Hughes Medical Institute Investigator.
“We begin life as a one-cell zygote and that cell, in just a few weeks or months, divides and differentiates into hundreds of cell types,” Shendure noted. “So, the question is: How does that happen? What paths do cells take? What are the genes that shape those decisions?”
The researchers reported their findings today in the journal Nature Genetics. Chengxiang Qiu, a computational biologist and doctoral student in the Shendure lab, was lead author of the paper.
To map out this process, Shendure and his colleagues drew on publicly available data collected from mouse studies that used a technique called single-cell RNA sequencing (scRNA-seq).
This technique allows scientists to identify and quantify the levels of different messenger RNA molecules within individual cells. Because these mRNA are copied from activated genes, their presence in a cell indicates which genes are active at a given point in time.
To supplement these data, the researchers also used scRNA-seq data they had generated from about 150,000 embryonic mouse cell nuclei sampled every two hours over a critical 24-hour period beginning on the eighth day of mouse embryonic development.
Intensive scRNA-seq of somite-resolved E8.5 mouse embryos

a, A new scRNA-seq dataset was generated from nuclei derived from individual E8.5 mouse embryos via an optimized sci-RNA-seq3 protocol to bridge existing data generated on E8.5 cells via 10x Genomics and E9.5 nuclei via sci-RNA-seq3.. b, 3D UMAP visualizations of the new E8.5 dataset (E8.5b). All nuclei colored by germ layer are shown in the center, along with separate embeddings of neuroectoderm (left), nonhematopoietic mesoderm (bottom right) and endoderm, extraembryonic and hematopoietic cell types (top right). c, Twelve mouse embryos, including a single primitive-streak-stage embryo and 11 embryos staged in 1-somite increments from 2 to 12 somites, were collected and their nuclei subjected to optimized sci-RNA-seq3. d, Re-embedded two-dimensional (2D) UMAP of cells annotated as forebrain, midbrain, hindbrain, spinal cord and neural crest. Arrows correspond to RNA velocity trends. e, The same UMAP as in d, colored by somite counts. The subset of cells from rhombomere 4 that appear to emerge the earliest are highlighted in red circles (Hoxa1+ and Hoxb1+). f, For each cell type with >100 profiled cells, we calculated the Pearson correlation coefficient between the somite number of each cell of that type and the average somite number of its five nearest neighbors in the global 3D UMAP embedding. Colors indicate germ layers. g, 3D visualization of the top three PCs of gene expression variation in NMPs, calculated on the basis of the 2,500 most highly variable genes. Cells are colored by the somite count of the originating embryo. Genes most strongly correlated (Pearson), either positively (red) or negatively (green), with each PC are listed. ExE, extraembryonic; r2–r5: rhombomeres 2–5.
Qiu combined these data, and after adjusting them to account for differences in the techniques used by different researchers, identified the states of the embryonic cells at 19 stages spanning from day 3.5 in the mouse embryonic development to day 13.5, when most of the key embryonic structures have appeared. In mice, gestation, from a single-cell zygote to a live-born pup, takes only 21 days.
The resulting map traces out the changes in gene activity as the cells develop and differentiate into different cell types. Of particular interest is the activity of genes for key regulatory proteins, called transcription factors, that orchestrate cell growth, development and function, said Qiu.
“As embryogenesis proceeds, the cells branch off into different cell types like branches of a tree,” said Qiu. “Transcription factors that are active at these branch points tend to be specific for a new cell type.” On the other hand, some transcription factors appear active in many different cell types, Qiu noted, suggesting they play a more general role in regulating cell growth and function.
Going forward the researchers hope to obtain scRNA-seq data from more and more points in embryonic development to capture changes in gene expression in more detail. The team hopes the new map, called TOME (Trajectories Of Mammalian Embryogenesis), will serve as a resource for researchers working on many aspects of embryonic development.
Source – University of Washington School of Medicine
Qiu C, Cao J, Martin BK, Li T, Welsh IC, Srivatsan S, Huang X, Calderon D, Noble WS, Disteche CM, Murray SA, Spielmann M, Moens CB, Trapnell C, Shendure J. (2022) Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat Genet 54(3):328-341. [article]
Map details the changes in gene activity that occur as the embryo develops and differentiates into different cell types.
The researchers sequenced messenger RNA from about 150,000 embyronic mouse cells at key stages of development
Scientists have mapped out the key molecular changes that orchestrate how embryonic mouse cells differentiate into the diverse cell types that will ultimately form all the different tissues and organs of the adult animal.
This “roadmap” of mouse embryogenesis will help researchers understand the molecular programs that control how different specialized cell types emerge from less differentiated cells as an embryo grows and develops not only for mice, but also for humans, said Jay Shendure, professor of genome sciences at the University of Washington School of Medicine, scientific director of the Brotman Baty Institute for Precision Medicine and a Howard Hughes Medical Institute Investigator.
The researchers reported their findings today in the journal Nature Genetics. Chengxiang Qiu, a computational biologist and doctoral student in the Shendure lab, was lead author of the paper.
To map out this process, Shendure and his colleagues drew on publicly available data collected from mouse studies that used a technique called single-cell RNA sequencing (scRNA-seq).
This technique allows scientists to identify and quantify the levels of different messenger RNA molecules within individual cells. Because these mRNA are copied from activated genes, their presence in a cell indicates which genes are active at a given point in time.
To supplement these data, the researchers also used scRNA-seq data they had generated from about 150,000 embryonic mouse cell nuclei sampled every two hours over a critical 24-hour period beginning on the eighth day of mouse embryonic development.
Intensive scRNA-seq of somite-resolved E8.5 mouse embryos
a, A new scRNA-seq dataset was generated from nuclei derived from individual E8.5 mouse embryos via an optimized sci-RNA-seq3 protocol to bridge existing data generated on E8.5 cells via 10x Genomics and E9.5 nuclei via sci-RNA-seq3.. b, 3D UMAP visualizations of the new E8.5 dataset (E8.5b). All nuclei colored by germ layer are shown in the center, along with separate embeddings of neuroectoderm (left), nonhematopoietic mesoderm (bottom right) and endoderm, extraembryonic and hematopoietic cell types (top right). c, Twelve mouse embryos, including a single primitive-streak-stage embryo and 11 embryos staged in 1-somite increments from 2 to 12 somites, were collected and their nuclei subjected to optimized sci-RNA-seq3. d, Re-embedded two-dimensional (2D) UMAP of cells annotated as forebrain, midbrain, hindbrain, spinal cord and neural crest. Arrows correspond to RNA velocity trends. e, The same UMAP as in d, colored by somite counts. The subset of cells from rhombomere 4 that appear to emerge the earliest are highlighted in red circles (Hoxa1+ and Hoxb1+). f, For each cell type with >100 profiled cells, we calculated the Pearson correlation coefficient between the somite number of each cell of that type and the average somite number of its five nearest neighbors in the global 3D UMAP embedding. Colors indicate germ layers. g, 3D visualization of the top three PCs of gene expression variation in NMPs, calculated on the basis of the 2,500 most highly variable genes. Cells are colored by the somite count of the originating embryo. Genes most strongly correlated (Pearson), either positively (red) or negatively (green), with each PC are listed. ExE, extraembryonic; r2–r5: rhombomeres 2–5.
Qiu combined these data, and after adjusting them to account for differences in the techniques used by different researchers, identified the states of the embryonic cells at 19 stages spanning from day 3.5 in the mouse embryonic development to day 13.5, when most of the key embryonic structures have appeared. In mice, gestation, from a single-cell zygote to a live-born pup, takes only 21 days.
The resulting map traces out the changes in gene activity as the cells develop and differentiate into different cell types. Of particular interest is the activity of genes for key regulatory proteins, called transcription factors, that orchestrate cell growth, development and function, said Qiu.
Going forward the researchers hope to obtain scRNA-seq data from more and more points in embryonic development to capture changes in gene expression in more detail. The team hopes the new map, called TOME (Trajectories Of Mammalian Embryogenesis), will serve as a resource for researchers working on many aspects of embryonic development.
Source – University of Washington School of Medicine
Qiu C, Cao J, Martin BK, Li T, Welsh IC, Srivatsan S, Huang X, Calderon D, Noble WS, Disteche CM, Murray SA, Spielmann M, Moens CB, Trapnell C, Shendure J. (2022) Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat Genet 54(3):328-341. [article]
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Map details the changes in gene activity that occur as the embryo develops and differentiates into different cell types.
The researchers sequenced messenger RNA from about 150,000 embyronic mouse cells at key stages of development
Scientists have mapped out the key molecular changes that orchestrate how embryonic mouse cells differentiate into the diverse cell types that will ultimately form all the different tissues and organs of the adult animal.
This “roadmap” of mouse embryogenesis will help researchers understand the molecular programs that control how different specialized cell types emerge from less differentiated cells as an embryo grows and develops not only for mice, but also for humans, said Jay Shendure, professor of genome sciences at the University of Washington School of Medicine, scientific director of the Brotman Baty Institute for Precision Medicine and a Howard Hughes Medical Institute Investigator.
The researchers reported their findings today in the journal Nature Genetics. Chengxiang Qiu, a computational biologist and doctoral student in the Shendure lab, was lead author of the paper.
To map out this process, Shendure and his colleagues drew on publicly available data collected from mouse studies that used a technique called single-cell RNA sequencing (scRNA-seq).
This technique allows scientists to identify and quantify the levels of different messenger RNA molecules within individual cells. Because these mRNA are copied from activated genes, their presence in a cell indicates which genes are active at a given point in time.
To supplement these data, the researchers also used scRNA-seq data they had generated from about 150,000 embryonic mouse cell nuclei sampled every two hours over a critical 24-hour period beginning on the eighth day of mouse embryonic development.
Intensive scRNA-seq of somite-resolved E8.5 mouse embryos
a, A new scRNA-seq dataset was generated from nuclei derived from individual E8.5 mouse embryos via an optimized sci-RNA-seq3 protocol to bridge existing data generated on E8.5 cells via 10x Genomics and E9.5 nuclei via sci-RNA-seq3.. b, 3D UMAP visualizations of the new E8.5 dataset (E8.5b). All nuclei colored by germ layer are shown in the center, along with separate embeddings of neuroectoderm (left), nonhematopoietic mesoderm (bottom right) and endoderm, extraembryonic and hematopoietic cell types (top right). c, Twelve mouse embryos, including a single primitive-streak-stage embryo and 11 embryos staged in 1-somite increments from 2 to 12 somites, were collected and their nuclei subjected to optimized sci-RNA-seq3. d, Re-embedded two-dimensional (2D) UMAP of cells annotated as forebrain, midbrain, hindbrain, spinal cord and neural crest. Arrows correspond to RNA velocity trends. e, The same UMAP as in d, colored by somite counts. The subset of cells from rhombomere 4 that appear to emerge the earliest are highlighted in red circles (Hoxa1+ and Hoxb1+). f, For each cell type with >100 profiled cells, we calculated the Pearson correlation coefficient between the somite number of each cell of that type and the average somite number of its five nearest neighbors in the global 3D UMAP embedding. Colors indicate germ layers. g, 3D visualization of the top three PCs of gene expression variation in NMPs, calculated on the basis of the 2,500 most highly variable genes. Cells are colored by the somite count of the originating embryo. Genes most strongly correlated (Pearson), either positively (red) or negatively (green), with each PC are listed. ExE, extraembryonic; r2–r5: rhombomeres 2–5.
Qiu combined these data, and after adjusting them to account for differences in the techniques used by different researchers, identified the states of the embryonic cells at 19 stages spanning from day 3.5 in the mouse embryonic development to day 13.5, when most of the key embryonic structures have appeared. In mice, gestation, from a single-cell zygote to a live-born pup, takes only 21 days.
The resulting map traces out the changes in gene activity as the cells develop and differentiate into different cell types. Of particular interest is the activity of genes for key regulatory proteins, called transcription factors, that orchestrate cell growth, development and function, said Qiu.
Going forward the researchers hope to obtain scRNA-seq data from more and more points in embryonic development to capture changes in gene expression in more detail. The team hopes the new map, called TOME (Trajectories Of Mammalian Embryogenesis), will serve as a resource for researchers working on many aspects of embryonic development.
Source – University of Washington School of Medicine
Qiu C, Cao J, Martin BK, Li T, Welsh IC, Srivatsan S, Huang X, Calderon D, Noble WS, Disteche CM, Murray SA, Spielmann M, Moens CB, Trapnell C, Shendure J. (2022) Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat Genet 54(3):328-341. [article]
Related Posts
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
Unlocking the past – new method helps gain insights into old tissue
New RNA sequencing model improves sequencing depth planning for UMI transcriptomics
Combining RNA sequencing and pathology images identifies glioblastoma subgroups linked to survival
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