mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities

Single-cell multiomics provides unique insight into the regulatory relationships across different biological layers such as the transcriptome and regulome. However, single-cell multiomics is limited by its ability to capture only static snapshots at the time of observation, restricting the reflection of dynamic state changes orchestrated across modalities. RNA velocity analysis of single cells allows for the prediction of temporal changes in the transcriptome; however, the inferred dynamics cannot be applied across all biological layers, specifically in the regulome. Therefore, to address this limitation, we developed multimodal velocity of single cells (mmVelo), a deep generative model designed to estimate cell state-dependent dynamics across multiple modalities. mmVelo estimates cell state dynamics based on spliced and unspliced mRNA expression, and uses multimodal representation learning to project these dynamics onto chromatin accessibility, inferring chromatin velocity at a single-peak resolution. We applied mmVelo to single-cell multiomics data from a developing mouse brain and validated the accuracy of the estimated chromatin accessibility dynamics. Furthermore, using the estimated dynamics, we identified the transcription factors that are crucial for chromatin accessibility regulation in mouse skin. Finally, using multiomics data as a bridge, we demonstrated that during human brain development, the dynamics of missing modalities can be inferred from single-modal data via cross-modal generation. Overall, mmVelo enhances our understanding of the dynamic interactions between modalities, offering insights into the regulatory relationships across molecular layers.