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Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data

Author

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  • Qi Jiang
  • Shuo Zhang
  • Lin Wan
Abstract
Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in a nonlinear quadratic term. We then recast the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the adjoint method of optimal control. We evaluated GraphFP on the time series scRNA-seq data set of embryonic murine cerebral cortex development. We illustrated that it 1) reconstructs cell state potential energy, which is a measure of cellular differentiation potency, 2) faithfully charts the probability flows between paired cell states over the dynamic processes of cell differentiation, and 3) accurately quantifies the stochastic dynamics of cell type frequencies on probability simplex in continuous time. We also illustrated that GraphFP is robust in terms of cluster labelling with different resolutions, as well as parameter choices. Meanwhile, GraphFP provides a model-based approach to delineate the cell-cell interactions that drive cell differentiation. GraphFP software is available at https://github.com/QiJiang-QJ/GraphFP.Author summary: Dynamic inference of cell development processes from time series scRNA-seq data is a major challenge. Here, we present GraphFP, a coherent computational framework that simultaneously reconstructs the cell state-transition complex potential energy landscape and infers cell-cell interactions from time series single-cell transcriptomic data. Based on the mathematical framework of nonlinear Fokker-Planck equation on graph, GraphFP models the stochastic dynamics of the cell state/type frequencies on probability simplex in continuous time, where the free energy with a nonlinear quadratic interaction term is employed to characterize cell-cell interactions. We formulate the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the celebrated adjoint method. GraphFP allows for 1) reconstructing cell state potential energy, which is a measure of cellular differentiation potency, 2) charting the probability flows between paired cell states over dynamic processes, 3) quantifying the stochastic dynamics of cell type frequencies on probability simplex in continuous time, and 4) delineating cell-cell interactions that drive cell differentiation. We show how GraphFP can be used to faithfully reveal and accurately quantify the cell development processes using the embryonic murine cerebral cortex development time series scRNA-seq dataset.

Suggested Citation

  • Qi Jiang & Shuo Zhang & Lin Wan, 2022. "Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data," PLOS Computational Biology, Public Library of Science, vol. 18(1), pages 1-22, January.
    • Handle: RePEc:plo:pcbi00:1009821
    DOI: 10.1371/journal.pcbi.1009821
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    References listed on IDEAS

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    1. Amos Tanay & Aviv Regev, 2017. "Scaling single-cell genomics from phenomenology to mechanism," Nature, Nature, vol. 541(7637), pages 331-338, January.
    2. Jifan Shi & Tiejun Li & Luonan Chen & Kazuyuki Aihara, 2019. "Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-17, November.
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