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Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

View ORCID ProfileDavid S. Fischer, Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, View ORCID ProfileJan Hasenauer, View ORCID ProfileRene Maehr, View ORCID ProfileFabian J. Theis
doi: https://doi.org/10.1101/219188
David S. Fischer
1HelmholzZentrum München, Institute of Computational Biology, 85764 Neuherberg, Germany
2TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
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Anna K. Fiedler
1HelmholzZentrum München, Institute of Computational Biology, 85764 Neuherberg, Germany
3Technical University of Munich, Department of Mathematics, 85748 Garching bei München, Germany
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Eric Kernfeld
4Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA 01655, USA
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Ryan M. J. Genga
4Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA 01655, USA
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Jan Hasenauer
1HelmholzZentrum München, Institute of Computational Biology, 85764 Neuherberg, Germany
3Technical University of Munich, Department of Mathematics, 85748 Garching bei München, Germany
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Rene Maehr
4Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA 01655, USA
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Fabian J. Theis
1HelmholzZentrum München, Institute of Computational Biology, 85764 Neuherberg, Germany
2TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
3Technical University of Munich, Department of Mathematics, 85748 Garching bei München, Germany
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Abstract

Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in T-cell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell’s state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates time-series and genetic knock-out information with such transcriptome-based descriptions in order to describe and analyze the real-time evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cell-state-dependent growth and differentiation of thymic T-cell development. The model approximates a developmental potential function (Waddington’s landscape) and suggests that thymic T-cell development is biphasic and not strictly deterministic before beta-selection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.

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Posted November 14, 2017.
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Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series
David S. Fischer, Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, Jan Hasenauer, Rene Maehr, Fabian J. Theis
bioRxiv 219188; doi: https://doi.org/10.1101/219188
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Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series
David S. Fischer, Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, Jan Hasenauer, Rene Maehr, Fabian J. Theis
bioRxiv 219188; doi: https://doi.org/10.1101/219188

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