Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Geoffrey Schiebinger; Jian Shu; Marcin Tabaka; Brian Cleary; Vidya Subramanian; Aryeh Solomon; Joshua Gould; Siyan Liu; Stacie Lin; Peter Berube; Lia Lee; Jenny Chen; Justin Brumbaugh; Philippe Rigollet; Konrad Hochedlinger; Rudolf Jaenisch; Aviv Regev; Eric S Lander

doi:10.1016/j.cell.2019.01.006

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Cell. 2019 Feb 7;176(4):928-943.e22. doi: 10.1016/j.cell.2019.01.006. Epub 2019 Jan 31.

Authors

Geoffrey Schiebinger¹, Jian Shu², Marcin Tabaka³, Brian Cleary⁴, Vidya Subramanian³, Aryeh Solomon³, Joshua Gould³, Siyan Liu⁵, Stacie Lin⁶, Peter Berube³, Lia Lee³, Jenny Chen⁷, Justin Brumbaugh⁸, Philippe Rigollet⁹, Konrad Hochedlinger¹⁰, Rudolf Jaenisch¹¹, Aviv Regev¹², Eric S Lander¹³

Affiliations

¹ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. Electronic address: jianshu@broadinstitute.org.
³ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁴ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
⁵ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Biochemistry Program, Wellesley College, Wellesley, MA 02481, USA.
⁶ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁷ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA.
⁸ Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Harvard Medical School, Boston, MA 02115, USA.
⁹ MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
¹⁰ Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
¹¹ Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
¹² Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. Electronic address: aregev@broadinstitute.org.
¹³ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA. Electronic address: lander@broadinstitute.org.

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.

Keywords: ancestors; descendants; development; iPSCs; optimal-transport; paracrine interactions; regulation; reprogramming; scRNA-seq; trajectories.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Cell Differentiation / genetics
Cells, Cultured
Cellular Reprogramming / genetics*
Embryonic Stem Cells / metabolism
Fibroblasts / metabolism
Gene Expression
Gene Expression Profiling / methods*
Gene Expression Regulation, Developmental / genetics
Induced Pluripotent Stem Cells / metabolism
Mice
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods*
Transcription Factors / metabolism

Substances

Transcription Factors

Abstract

Publication types

MeSH terms

Substances

Grants and funding