Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
Name
nihms-1519815.pdf
Description
Accepted version
Size
1.48 MB
Format
Adobe PDF
Checksum (MD5)
f77f9f575c75f31e84182fc1de5f6ed6
Author(s)
Cleary, Brian Lowman
Lin, Stacie
Jaenisch, Rudolf
Regev, Aviv
Lander, Eric Steven
Date Issued
February 2019
Journal
Cell
Publisher
Elsevier BV
Citation
Schiebinger, Geoffrey et al. "Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming." Cell 176 (2019): 928-943 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
Subjects
General Biochemistry, Genetics and Molecular Biology
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Computational and Systems Biology Program
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cell.2019.01.006
