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

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...

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Published in	Cell Vol. 176; no. 4; pp. 928 - 943.e22
Main Authors	Schiebinger, Geoffrey, Shu, Jian, Tabaka, Marcin, Cleary, Brian, Subramanian, Vidya, Solomon, Aryeh, Gould, Joshua, Liu, Siyan, Lin, Stacie, Berube, Peter, Lee, Lia, Chen, Jenny, Brumbaugh, Justin, Rigollet, Philippe, Hochedlinger, Konrad, Jaenisch, Rudolf, Regev, Aviv, Lander, Eric S.
Format	Journal Article
Language	English
Published	United States Elsevier Inc 07.02.2019
Subjects	ancestors Animals Cell Differentiation - genetics Cells, Cultured Cellular Reprogramming - genetics cytokines descendants development Embryonic Stem Cells - metabolism Fibroblasts - metabolism Gene Expression Gene Expression Profiling - methods Gene Expression Regulation, Developmental - genetics Induced Pluripotent Stem Cells - metabolism iPSCs Mice optimal-transport paracrine interactions regulation reprogramming scRNA-seq sequence analysis Sequence Analysis, RNA - methods Single-Cell Analysis - methods trajectories transcription factors Transcription Factors - metabolism reprogramming iPSCs optimal-transport scRNA-seq development paracrine interactions regulation trajectories ancestors descendants
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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. [Display omitted] •Optimal transport analysis recovers trajectories from 315,000 scRNA-seq profiles•Induced pluripotent stem cell reprogramming produces diverse developmental programs•Regulatory analysis identifies a series of TFs predictive of specific cell fates•Transcription factor Obox6 and cytokine GDF9 increase reprogramming efficiency Application of a new analytical approach to examine developmental trajectories of single cells offers insight into how paracrine interactions shape reprogramming.
AbstractList	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. [Display omitted] •Optimal transport analysis recovers trajectories from 315,000 scRNA-seq profiles•Induced pluripotent stem cell reprogramming produces diverse developmental programs•Regulatory analysis identifies a series of TFs predictive of specific cell fates•Transcription factor Obox6 and cytokine GDF9 increase reprogramming efficiency Application of a new analytical approach to examine developmental trajectories of single cells offers insight into how paracrine interactions shape reprogramming. 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.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. 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 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. 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. 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.
Author	Tabaka, Marcin Brumbaugh, Justin Lee, Lia Lander, Eric S. Regev, Aviv Cleary, Brian Subramanian, Vidya Gould, Joshua Rigollet, Philippe Solomon, Aryeh Hochedlinger, Konrad Jaenisch, Rudolf Berube, Peter Liu, Siyan Lin, Stacie Chen, Jenny Schiebinger, Geoffrey Shu, Jian
AuthorAffiliation	13 Howard Hughes Medical Institute, Chevy Chase, MD, USA 10 Harvard Medical School, Boston, MA 02115, USA 7 Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA 9 Harvard Stem Cell Institute, Cambridge, MA 02138, USA 12 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 11 MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 4 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139 USA 5 Cancer Center, Massachusetts General Hospital, Boston, MA 02114 USA 2 Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA 3 Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA 6 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 1 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 14 Department of Systems Biology Harvard Med
AuthorAffiliation_xml	– name: 8 Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA – name: 9 Harvard Stem Cell Institute, Cambridge, MA 02138, USA – name: 2 Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA – name: 4 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139 USA – name: 1 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – name: 3 Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA – name: 7 Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA – name: 11 MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – name: 12 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – name: 10 Harvard Medical School, Boston, MA 02115, USA – name: 6 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – name: 5 Cancer Center, Massachusetts General Hospital, Boston, MA 02114 USA – name: 13 Howard Hughes Medical Institute, Chevy Chase, MD, USA – name: 14 Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA – name: 15 Biochemistry Program, Wellesley College, Wellesley, 02481, MA, USA
Author_xml	– sequence: 1 givenname: Geoffrey surname: Schiebinger fullname: Schiebinger, Geoffrey organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 2 givenname: Jian surname: Shu fullname: Shu, Jian email: jianshu@broadinstitute.org organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 3 givenname: Marcin surname: Tabaka fullname: Tabaka, Marcin organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 4 givenname: Brian surname: Cleary fullname: Cleary, Brian organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 5 givenname: Vidya surname: Subramanian fullname: Subramanian, Vidya organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 6 givenname: Aryeh surname: Solomon fullname: Solomon, Aryeh organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 7 givenname: Joshua surname: Gould fullname: Gould, Joshua organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 8 givenname: Siyan surname: Liu fullname: Liu, Siyan organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 9 givenname: Stacie surname: Lin fullname: Lin, Stacie organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 10 givenname: Peter surname: Berube fullname: Berube, Peter organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 11 givenname: Lia surname: Lee fullname: Lee, Lia organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 12 givenname: Jenny surname: Chen fullname: Chen, Jenny organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 13 givenname: Justin surname: Brumbaugh fullname: Brumbaugh, Justin organization: Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA – sequence: 14 givenname: Philippe surname: Rigollet fullname: Rigollet, Philippe organization: MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA – sequence: 15 givenname: Konrad surname: Hochedlinger fullname: Hochedlinger, Konrad organization: Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA – sequence: 16 givenname: Rudolf surname: Jaenisch fullname: Jaenisch, Rudolf organization: Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA – sequence: 17 givenname: Aviv surname: Regev fullname: Regev, Aviv email: aregev@broadinstitute.org organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 18 givenname: Eric S. surname: Lander fullname: Lander, Eric S. email: lander@broadinstitute.org organization: Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30712874$$D View this record in MEDLINE/PubMed
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Keywords	reprogramming iPSCs optimal-transport scRNA-seq development paracrine interactions regulation trajectories ancestors descendants
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Present Address: Weizmann Institute of Science, Rehovot, Israel Lead contact. Author Contributions JS and ESL conceived and designed the study of reprogramming in single-cell resolution. GS and ESL conceived the application of optimal transport; PR, AR, MT, and BC provided input on the development of the approach. MT, GS, BC, and JG developed WADDINGTON-OT. MT, GS, BC, ESL and A.R. analyzed the data, with assistance from J.S. All experiments were designed and performed by JS, with input from RJ and assistance from AS, SL, SL, PB, LL, JB, KH and VS. The manuscript was written by ESL, AR, GS, BC, MT, JS, and VS.
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Snippet	Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for... Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for...
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SubjectTerms	ancestors Animals Cell Differentiation - genetics Cells, Cultured Cellular Reprogramming - genetics cytokines descendants development Embryonic Stem Cells - metabolism Fibroblasts - metabolism Gene Expression Gene Expression Profiling - methods Gene Expression Regulation, Developmental - genetics Induced Pluripotent Stem Cells - metabolism iPSCs Mice optimal-transport paracrine interactions regulation reprogramming scRNA-seq sequence analysis Sequence Analysis, RNA - methods Single-Cell Analysis - methods trajectories transcription factors Transcription Factors - metabolism
Title	Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
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