Abstract
Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.
Highlights
Cells undergo state transitions during many biological processes, including development, reprogramming, regeneration and cancer, and they typically do so in a highly asynchronous fashion[1]
We show that CellRank generalizes beyond normal development by applying it to a reprogramming dataset, where predicted fate bias correctly recovers lineage-tracing-derived ground truth
Based on the inferred potentials, CellRank charts gene expression dynamics as cells take on different fates and identifies putative regulators of cell-fate decisions
Summary
Cells undergo state transitions during many biological processes, including development, reprogramming, regeneration and cancer, and they typically do so in a highly asynchronous fashion[1]. Single-cell RNA sequencing (scRNA-seq) successfully captures the heterogeneity that results from these processes, but it loses lineage relationships, since each cell can be measured only once To mitigate this problem, scRNA-seq can be combined with lineage tracing methods[2,3] that use heritable barcodes to follow clonal evolution over long time scales, or metabolic labeling methods[4,5,6] that use the ratio of nascent to mature RNA molecules to link observed gene expression profiles over short time windows. CellRank outperforms methods that do not include velocity information, and is available as a scalable, user-friendly open-source software package with documentation and tutorials at https://cellrank.org
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