CellRank for directed single-cell fate mapping
By Marius Lange, Volker Bergen, Michal Klein, Manu N. Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, J. Schniering, H. Schiller and 2 more. Nature Methods, 2020
659 citations recorded by the source corpus.
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. CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/71e6d4a5c1ea5e47d6039edd456237b75aeba86f
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Citations in the corpus
- Spatial components of molecular tissue biology (2022, positive): “Methods developed for dissociation-based data can be directly applied to spatial data, thus providing trajectory visualization in a spatial context 2,67,68 .”