Mapping Transcriptomic Vector Fields of Single Cells

Cells are complex dynamical systems, and a grand challenge is to reconstruct the governing dynamical equations. Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict future cell fates, employs differential geometry to extract underlying regulatory networks, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal the mechanism driving early appearance of megakaryocytes and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts specific drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step inadvancing quantitative and predictive theories of cell-state transitions.

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