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

Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs

Edward Ren, Sungmin Kim, Saad Mohamad, Samuel F. Huguet, Yulin Shi, Andrew R. Cohen, View ORCID ProfileEugenia Piddini, View ORCID ProfileRafael Carazo Salas
doi: https://doi.org/10.1101/2021.07.31.454574
Edward Ren
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sungmin Kim
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saad Mohamad
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel F. Huguet
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yulin Shi
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew R. Cohen
2Department of Electrical and Computer Engineering, Drexel University, 3120-40 Market Street, Suite 313, Philadelphia, PA 19104, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eugenia Piddini
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eugenia Piddini
Rafael Carazo Salas
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rafael Carazo Salas
  • For correspondence: rafael.carazosalas@bristol.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

SUMMARY

Predicting how stem cells become patterned and differentiated into target tissues is key for optimising human tissue design. Here, we established DEEP-MAP - for deep learning-enhanced morphological profiling - an approach that integrates single-cell, multi-day, multi-colour microscopy phenomics with deep learning and allows to robustly map and predict cell fate dynamics in real-time without a need for cell state-specific reporters. Using human pluripotent stem cells (hPSCs) engineered to co-express the histone H2B and two-colour FUCCI cell cycle reporters, we used DEEP-MAP to capture hundreds of morphological- and proliferation-associated features for hundreds of thousands of cells and used this information to map and predict spatiotemporally single-cell fate dynamics across germ layer cell fates. We show that DEEP-MAP predicts fate changes as early or earlier than transcription factor-based fate reporters, reveals the timing and existence of intermediate cell fates invisible to fixed-cell technologies, and identifies proliferative properties predictive of cell fate transitions. DEEP-MAP provides a versatile, universal strategy to map tissue evolution and organisation across many developmental and tissue engineering contexts.

Competing Interest Statement

The authors have declared no competing interest.

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 August 01, 2021.
Download PDF
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.
Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
(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
Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
Edward Ren, Sungmin Kim, Saad Mohamad, Samuel F. Huguet, Yulin Shi, Andrew R. Cohen, Eugenia Piddini, Rafael Carazo Salas
bioRxiv 2021.07.31.454574; doi: https://doi.org/10.1101/2021.07.31.454574
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
Edward Ren, Sungmin Kim, Saad Mohamad, Samuel F. Huguet, Yulin Shi, Andrew R. Cohen, Eugenia Piddini, Rafael Carazo Salas
bioRxiv 2021.07.31.454574; doi: https://doi.org/10.1101/2021.07.31.454574

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

  • Cell Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4107)
  • Biochemistry (8811)
  • Bioengineering (6513)
  • Bioinformatics (23452)
  • Biophysics (11786)
  • Cancer Biology (9202)
  • Cell Biology (13316)
  • Clinical Trials (138)
  • Developmental Biology (7431)
  • Ecology (11406)
  • Epidemiology (2066)
  • Evolutionary Biology (15144)
  • Genetics (10433)
  • Genomics (14037)
  • Immunology (9168)
  • Microbiology (22150)
  • Molecular Biology (8806)
  • Neuroscience (47554)
  • Paleontology (350)
  • Pathology (1427)
  • Pharmacology and Toxicology (2490)
  • Physiology (3730)
  • Plant Biology (8078)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2220)
  • Systems Biology (6036)
  • Zoology (1252)