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

Author

Listed:
  • Peng He

    (California Institute of Technology
    European Bioinformatics Institute (EMBL-EBI))

  • Brian A. Williams

    (California Institute of Technology)

  • Diane Trout

    (California Institute of Technology)

  • Georgi K. Marinov

    (Stanford University)

  • Henry Amrhein

    (California Institute of Technology)

  • Libera Berghella

    (California Institute of Technology)

  • Say-Tar Goh

    (California Institute of Technology)

  • Ingrid Plajzer-Frick

    (Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory)

  • Veena Afzal

    (Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory)

  • Len A. Pennacchio

    (Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory
    Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory
    University of California, Berkeley)

  • Diane E. Dickel

    (Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory)

  • Axel Visel

    (Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory
    Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory
    University of California, Merced)

  • Bing Ren

    (University of California, San Diego)

  • Ross C. Hardison

    (Pennsylvania State University)

  • Yu Zhang

    (Pennsylvania State University)

  • Barbara J. Wold

    (California Institute of Technology)

Abstract

During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate.

Suggested Citation

  • Peng He & Brian A. Williams & Diane Trout & Georgi K. Marinov & Henry Amrhein & Libera Berghella & Say-Tar Goh & Ingrid Plajzer-Frick & Veena Afzal & Len A. Pennacchio & Diane E. Dickel & Axel Visel &, 2020. "The changing mouse embryo transcriptome at whole tissue and single-cell resolution," Nature, Nature, vol. 583(7818), pages 760-767, July.
  • Handle: RePEc:nat:nature:v:583:y:2020:i:7818:d:10.1038_s41586-020-2536-x
    DOI: 10.1038/s41586-020-2536-x
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    Citations

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    Cited by:

    1. Martin Arostegui & R. Wilder Scott & Kerstin Böse & T. Michael Underhill, 2022. "Cellular taxonomy of Hic1+ mesenchymal progenitor derivatives in the limb: from embryo to adult," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    2. Lindsey R. Conroy & Harrison A. Clarke & Derek B. Allison & Samuel Santos Valenca & Qi Sun & Tara R. Hawkinson & Lyndsay E. A. Young & Juanita E. Ferreira & Autumn V. Hammonds & Jaclyn B. Dunne & Robe, 2023. "Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Wanying Wu & Jinyang Zhang & Xiaofei Cao & Zhengyi Cai & Fangqing Zhao, 2022. "Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Michael J. Geuenich & Dae-won Gong & Kieran R. Campbell, 2024. "The impacts of active and self-supervised learning on efficient annotation of single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    5. Angela Tung & Megan M. Sperry & Wesley Clawson & Ananya Pavuluri & Sydney Bulatao & Michelle Yue & Ramses Martinez Flores & Vaibhav P. Pai & Patrick McMillen & Franz Kuchling & Michael Levin, 2024. "Embryos assist morphogenesis of others through calcium and ATP signaling mechanisms in collective teratogen resistance," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    6. Rachel K. Lex & Weiqiang Zhou & Zhicheng Ji & Kristin N. Falkenstein & Kaleigh E. Schuler & Kathryn E. Windsor & Joseph D. Kim & Hongkai Ji & Steven A. Vokes, 2022. "GLI transcriptional repression is inert prior to Hedgehog pathway activation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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