Your browser doesn't support javascript.
loading
Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.
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.
Affiliation
  • Schiebinger G; 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.
  • Shu J; 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.
  • Tabaka M; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Cleary B; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
  • Subramanian V; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Solomon A; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Gould J; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Liu S; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Biochemistry Program, Wellesley College, Wellesley, MA 02481, USA.
  • Lin S; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Berube P; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Lee L; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Chen J; 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.
  • Brumbaugh J; 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,
  • Rigollet P; MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Hochedlinger K; 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 Hu
  • Jaenisch R; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
  • Regev A; 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.
  • Lander ES; 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.
Cell ; 176(4): 928-943.e22, 2019 02 07.
Article in En | MEDLINE | ID: mdl-30712874
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.
Subject(s)
Key words
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Profiling / Cellular Reprogramming / Single-Cell Analysis Type of study: Prognostic_studies Limits: Animals Language: En Journal: Cell Year: 2019 Type: Article Affiliation country: United States
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Profiling / Cellular Reprogramming / Single-Cell Analysis Type of study: Prognostic_studies Limits: Animals Language: En Journal: Cell Year: 2019 Type: Article Affiliation country: United States