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The changing mouse embryo transcriptome at whole tissue and single-cell resolution.

Peng He1,2, Brian A Williams3, Diane Trout1

  • 1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.

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|July 31, 2020
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Summary

This study maps mouse polyA-RNA during development, revealing dynamic gene expression patterns. Neurogenesis and haematopoiesis are key drivers, with promoter de-repression identified as a major regulatory mechanism.

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Area of Science:

  • Developmental Biology
  • Genomics
  • Transcriptomics

Background:

  • Mammalian embryogenesis involves complex gene expression to establish tissue identity.
  • Understanding developmental gene regulation is crucial for developmental biology.

Purpose of the Study:

  • To systematically quantify the mouse polyA-RNA transcriptome during embryonic development.
  • To characterize the global structure and key drivers of differential gene expression.
  • To identify regulatory mechanisms, including transcription factor networks and epigenomic profiles.

Main Methods:

  • Systematic quantification of mouse polyA-RNA from embryonic day 10.5 to birth across 17 tissues.
  • Decomposition of tissue-level transcriptomes using single-cell RNA sequencing (scRNA-seq).
  • Integration of promoter sequence motifs with ENCODE epigenomic profiles and IDEAS models.

Main Results:

  • The developmental transcriptome is globally structured by cytodifferentiation, body-axis, and cell-proliferation gene sets.
  • Neurogenesis and haematopoiesis significantly dominate differential gene expression and cell types.
  • A promoter de-repression mechanism involving repressors was identified in neuronal expression clusters.
  • scRNA-seq analysis of developing limb identified 25 cell types with inferred lineage relationships.

Conclusions:

  • Dynamic gene expression, particularly neurogenesis and haematopoiesis, shapes mammalian embryonic development.
  • Promoter de-repression is a key regulatory mechanism in neuronal development.
  • Integrated analysis of transcriptomic and epigenomic data provides valuable resources for developmental research.