Dissection and integration of bursty transcriptional dynamics for complex systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

The source code, Jupyter notebooks, and R markdown files for reproducing figures and results in this paper are available at https://doi.org/10.5281/zenodo.10826412 (75). TopicVelo is available as an open-source Python package for public use at https://github.com/RiesenfeldGroup/TopicVelo (76). The gastrulation (10), bone marrow (28), dentate gyrus (29), and pancreas (30) data are available in the scVelo package (7). The human hematopoiesis scNT-seq (22) and ILCs data (31) are available in the NCBI Gene Expression Omnibus (GEO) under accession numbers GSE193517 and GSE149622, respectively.