Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub (https://github.com/welch-lab/MultiVelo).

Automated summary

This paper introduces a model called MultiVelo, which extends the RNA velocity framework using differential equations to incorporate epigenomic data. The model improves the accuracy of cell fate prediction by estimating the temporal relationship between chromatin accessibility and gene expression. The study reveals different classes of genes and cell states, and identifies time lags between transcription factor expression and binding site accessibility, as well as disease-associated SNP accessibility and linked gene expression.