Abstract
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 cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.
Highlights
A hallmark of metazoans is the ability of a single zygote to differentiate into a multitude of cell types while maintaining the same genome
Fourth, leveraging the analytical vector field reconstructed directly from scRNA-seq datasets, we develop two principled methods, least action paths (LAPs) and in silico perturbation, to make non-trivial predictions of optimal paths and key drivers of cell-fate transitions, as well as outcomes of genetic perturbations
Consider a two-gene toggle-switch motif (Huang et al, 2007; Wang et al, 2010) that appears frequently in cell differentiation, such as the PU.1/SPI1-GATA1 regulatory network involved in hematopoiesis (Figure 1A1)
Summary
A hallmark of metazoans is the ability of a single zygote to differentiate into a multitude of cell types while maintaining the same genome. To illustrate this process, Waddington introduced the epigenetic landscape, a metaphor in which differentiation proceeds similar to a ball sliding downhill into various valleys (Waddington, 1957). Neglecting stochasticity, the time derivative of the cell state, or its velocity, ðx_ðtÞ Þ, is governed by a set of ordinary differential equations (ODEs) determined by the underlying GRN, expressed as x_ðtÞ = fðxðtÞ Þ, where f is a vector field function of the instantaneous cell states ðxðtÞ Þ. Efforts have been made to perform whole-cell simulations of bacteria (Karr et al, 2012; Macklin et al, 2020), it remains a grand challenge to reconstruct the vector field (a vector-valued function that assigns a vector, such as the transcriptomic velocity vector, to any point in the state space, such as the observed or unobserved transcriptomic expression configuration; see Box 1) representing the time evolution of a genome-wide expression state in mammalian cells from experimental data
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