Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Peijie Zhou,Qing Nie,Tiejun Li,Shuxiong Wang

doi:10.1038/s41467-021-25548-w

Peijie Zhou, Qing Nie + Show 2 more

Open Access

https://doi.org/10.1038/s41467-021-25548-w

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Abstract

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

Highlights

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging
Instead of fitting or solving the high-dimensional Eq (1) directly, here we recover the dynamical structure of its solution using a multi-scale data-driven approach, as described below
In induced pluripotent stem cells (iPSCs) data, we found that MuTrans, PAGA and VarID are consistent in recovering the bifurcation dynamics toward En and M states (Supplementary Fig. 15)

Summary

Introduction

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. Transition cells are characterized by their transient dynamics during cell-fate switch[3], or their mixed identities from multiple cell states[5], different from the well-defined stable cell states[6,7] that usually express marker genes with distinct biological functions. Dynamic modeling provides a natural way to characterize transition cells[3], allowing multiscale description of cell-fate transition (Fig. 1a and Supplementary Fig. 1) Such models analogize cells undergoing transition to particles confined in multiple potential wells with randomness[17,18], for which the transient states correspond to saddle points and the stable cell states correspond to attractors[19,20,21] of the underlying dynamical system a Discrete. Despite widely use of dynamical systems concepts to illustrate cell-fate decision[4], direct inference via dynamical models for transitions from singlecell transcriptome data is lacking

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Conclusion