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

Peijie Zhou, Shuxiong Wang, Tiejun Li, Qing Nie

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.