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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2020
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| Online Access: | https://hdl.handle.net/1721.1/124366 |
| _version_ | 1811087657555460096 |
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| author | Cleary, Brian Lowman Lin, Stacie Jaenisch, Rudolf Regev, Aviv Lander, Eric Steven |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Cleary, Brian Lowman Lin, Stacie Jaenisch, Rudolf Regev, Aviv Lander, Eric Steven |
| author_sort | Cleary, Brian Lowman |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:49:56Z |
| format | Article |
| id | mit-1721.1/124366 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:49:56Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1243662022-09-28T16:29:05Z Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming Cleary, Brian Lowman Lin, Stacie Jaenisch, Rudolf Regev, Aviv Lander, Eric Steven Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Computational and Systems Biology Program General Biochemistry, Genetics and Molecular 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. National Institutes of Health (U.S.) (grant HD045022) National Institutes of Health (U.S.) (grant R01 MH104610-15) National Institutes of Health (U.S.) (grant R01NS088538) 2020-03-26T18:55:47Z 2020-03-26T18:55:47Z 2019-02 2020-02-19T18:47:49Z Article http://purl.org/eprint/type/JournalArticle 0092-8674 https://hdl.handle.net/1721.1/124366 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) en 10.1016/j.cell.2019.01.006 Cell Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV PMC |
| spellingShingle | General Biochemistry, Genetics and Molecular Biology Cleary, Brian Lowman Lin, Stacie Jaenisch, Rudolf Regev, Aviv Lander, Eric Steven Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title | Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title_full | Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title_fullStr | Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title_full_unstemmed | Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title_short | Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming |
| title_sort | optimal transport analysis of single cell gene expression identifies developmental trajectories in reprogramming |
| topic | General Biochemistry, Genetics and Molecular Biology |
| url | https://hdl.handle.net/1721.1/124366 |
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