Inferring Single-Cell RNA Kinetics from Various Biological Priors

In the context of transcriptional dynamics modeled by ordinary differential equations (ODEs), the RNA level in a single cell is controlled by specific RNA kinetics parameters, which include transcription rate, splicing rate, and degradation rate. Investigating these single-cell RNA kinetics rates is pivotal for understanding RNA metabolism and the heterogeneity of complex tissues. Although metabolic labeling is an effective method to estimate these kinetics rates experimentally, it is not suitable for current large-scale conventional single-cell RNA sequencing (scRNA-seq) data. Moreover, existing methods for scRNA-seq often either neglect certain specific kinetics parameters or use inappropriate ways to fit the parameters. To address these issues, we introduce scRNAkinetics, a parallelized method that fits the kinetics parameters of the ODE for each cell using pseudo-time derived from various biological priors (e.g. cell lineage tree and differentiation potential). This approach allows for the estimation of the relative kinetics of each cell and gene in a scRNA-seq dataset. Validated on simulated datasets, scRNAkinetics can accurately infer the kinetics rates of transcription boosting, multi-branch, and time-dependent RNA degradation systems. Nevertheless, the inferred kinetics trends are concordant with previous studies on metabolic labeling and conventional scRNA-seq datasets. Furthermore, we show that scRNAkinetics can provide valuable insights into different regulatory schemes and validate the coupling between transcription and splicing in RNA metabolism. The open-source implementation of scRNAkinetics is available at https://github.com/poseidonchan/scRNAkinetics.