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Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming

Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, Eric S. Lander
doi: https://doi.org/10.1101/191056
Geoffrey Schiebinger
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
11MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Jian Shu
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Marcin Tabaka
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Brian Cleary
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA
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Vidya Subramanian
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Aryeh Solomon
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Siyan Liu
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
15Biochemistry Program, Wellesley College, Wellesley, 02481, MA, USA
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Stacie Lin
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
6Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Peter Berube
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Lia Lee
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Jenny Chen
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
4Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139 USA
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Justin Brumbaugh
5Cancer Center, Massachusetts General Hospital, Boston, MA 02114 USA
7Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA
8Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
9Harvard Stem Cell Institute, Cambridge, MA 02138, USA
10Harvard Medical School, Boston, MA 02115, USA
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Philippe Rigollet
11MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
12Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Konrad Hochedlinger
7Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA
8Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
9Harvard Stem Cell Institute, Cambridge, MA 02138, USA
13Howard Hughes Medical Institute, Chevy Chase, MD, USA
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Rudolf Jaenisch
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
3Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA
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Aviv Regev
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
6Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
13Howard Hughes Medical Institute, Chevy Chase, MD, USA
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Eric S. Lander
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
6Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
14Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA
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Abstract

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

Footnotes

  • Email: lander{at}broadinstitute.org (E.S.L.), aregev{at}broadinstitute.org (A.R.); jianshu{at}broadinstitute.org (J.S.)

  • 1 A limitation of Waddington’s landscape is that it is cell-autonomous (i.e. doesn’t include effects of other cells). Our model is actually more general.

  • 2 For example, one simple cost function is squared Euclidean distance c(x, y) = ||x - y||2.

  • 3 For example, imagine that ℙt is a mixture of Gaussians with time-varying mixture weights.

  • 4 Advection, a term borrowed from fluid mechanics, refers to the transport of a substance by bulk motion. The constraint that the divergence of the flow is equal to the rate of change of ρ means that ρ flows according to the velocity field v, without gaining or losing mass.

  • 5 As we discuss in Appendix S2, we have had some initial success with a rectified-linear function class. Following Cleary et al. 2017 [SMAF], we impose low-rank and sparsity constraints on the linear part, and we apply the function only to the transcription factors in Xti. Each low-rank component can then be interpreted as a regulatory module of transcription factors acting on a module of regulated genes.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 27, 2017.
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Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, Eric S. Lander
bioRxiv 191056; doi: https://doi.org/10.1101/191056
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Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, Eric S. Lander
bioRxiv 191056; doi: https://doi.org/10.1101/191056

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