Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human preimplantation epiblast

Giuliano G Stirparo, Thorsten Boroviak, Ge Guo, View ORCID ProfileJennifer Nichols, View ORCID ProfileAustin Smith, View ORCID ProfilePaul Bertone
doi: https://doi.org/10.1101/222760
Giuliano G Stirparo
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thorsten Boroviak
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ge Guo
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer Nichols
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
2Department of Physiology, Development and Neuroscience, University of Cambridge, Tennis Court Road, Cambridge CB2 3EG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jennifer Nichols
Austin Smith
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
3Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Austin Smith
Paul Bertone
1Wellcome Trust – Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Paul Bertone
  • For correspondence: bertone@stemcells.cam.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Single-cell profiling techniques create opportunities to delineate cell fate progression in mammalian development. Recent studies provide transcriptome data from human preimplantation embryos, in total comprising nearly 2000 individual cells. Interpretation of these data is confounded by biological factors such as variable embryo staging and cell-type ambiguity, as well as technical challenges in the collective analysis of datasets produced with different sample preparation and sequencing protocols. Here we address these issues to assemble a complete gene expression time course spanning human preimplantation embryogenesis. We identify key transcriptional features over developmental time and elucidate lineage-specific regulatory networks. We resolve post hoc cell-type assignment in the blastocyst, and define robust transcriptional prototypes that capture epiblast and primitive endoderm lineages. Examination of human pluripotent stem cell transcriptomes in this framework identifies culture conditions that sustain a naïve state pertaining to the inner cell mass. Our approach thus clarifies understanding both of lineage segregation in the early human embryo and of in vitro stem cell identity, and provides an analytical resource for comparative molecular embryology.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted November 21, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human preimplantation epiblast
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human preimplantation epiblast
Giuliano G Stirparo, Thorsten Boroviak, Ge Guo, Jennifer Nichols, Austin Smith, Paul Bertone
bioRxiv 222760; doi: https://doi.org/10.1101/222760
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human preimplantation epiblast
Giuliano G Stirparo, Thorsten Boroviak, Ge Guo, Jennifer Nichols, Austin Smith, Paul Bertone
bioRxiv 222760; doi: https://doi.org/10.1101/222760

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Developmental Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4094)
  • Biochemistry (8784)
  • Bioengineering (6490)
  • Bioinformatics (23378)
  • Biophysics (11763)
  • Cancer Biology (9164)
  • Cell Biology (13269)
  • Clinical Trials (138)
  • Developmental Biology (7420)
  • Ecology (11380)
  • Epidemiology (2066)
  • Evolutionary Biology (15110)
  • Genetics (10408)
  • Genomics (14017)
  • Immunology (9133)
  • Microbiology (22087)
  • Molecular Biology (8792)
  • Neuroscience (47418)
  • Paleontology (350)
  • Pathology (1421)
  • Pharmacology and Toxicology (2483)
  • Physiology (3710)
  • Plant Biology (8060)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2213)
  • Systems Biology (6019)
  • Zoology (1251)