noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts

Abstract Modern single-cell transcriptomics has revolutionized biological research, but because of its destructive nature, it provides only static snapshots. Computational approaches that infer RNA velocity from the ratio of unspliced to spliced mRNA levels can be used to predict how gene expression changes over time. However, information about unspliced and spliced transcripts is not always available and may change on a timescale too short to accurately infer transitions between cellular states. Here we present noSpliceVelo, a novel technique for reconstructing RNA velocity without relying on unspliced and spliced transcripts. Instead, it exploits the temporal relationship between the variance and mean of bursty gene expression using a well-established biophysical model. When evaluated on datasets describing mouse pancreatic endocrinogenesis, mouse and human erythroid maturation, and neuronal stimulation in mouse embryonic cortex, noSpliceVelo performed comparably or better than scVelo, a splicing-based approach. In addition, noSpliceVelo inferred key biophysical parameters of gene regulation, specifically burst size and frequency, potentially distinguishing between transcriptional and epigenetic regulation.