Speaker
Description
With single-cell genomics datasets, it is possible to assay the transcriptome in thousands to millions of cells as they undergo development, differentiation and external perturbation. These data are ripe for investigation into how regulation of RNA processing governs the myriad cell states comprising these systems.
However, despite our ability to assay these states, single-cell data is sparse and noisy, a result of both innate biological features of the RNA molecules and technical effects in RNA capture and sequencing. Standard analyses often rely on heuristic application of dimensionality reductions to remove noise, and focus on mean-level (observational) changes in gene expression to define cell states.
Here we demonstrate how stochastic, biophysical models of RNA processing can be applied to snapshot single-cell data to reveal underlying mechanisms of observed gene expression patterns across heterogenous cell states. By explicitly realizing the biophysical and technical processes underlying the sparse, discrete molecular data, we can reveal altered dynamics of bursty transcription, splicing and degradation between populations of cells. We can additionally resolve heterogeneous regulation within these populations, and propose combinatorial strategies of regulation guiding these distinct fates. Together this work enables hypothesis-driven experimentation by proposing key points of transcriptional regulation underlying higher-level changes in gene expression and noise.
