CellRank for directed single-cell fate mapping.
CellRank for directed single-cell fate mapping.
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DOI：
10.1038/s41592-021-01346-6
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发表时间：
2022-03
期刊：
影响因子：
48
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Lange M;Bergen V;Klein M;Setty M;Reuter B;Bakhti M;Lickert H;Ansari M;Schniering J;Schiller HB;Pe'er D;Theis FJ
计算轨迹推断使得能够从单细胞RNA测序实验重建细胞状态动力学。然而，轨迹推断要求生物过程的方向是已知的，这在很大程度上限制了其在正常发育中的分化系统的应用。在这里，我们提出了CellRank（cellrank.org）用于不同情况下的单细胞命运映射，包括再生，重编程和疾病，其方向是未知的。我们的方法结合了轨迹推断的鲁棒性与RNA速度的方向信息，考虑到细胞命运决定的渐进性和随机性，以及速度向量的不确定性。在胰腺发育数据上，CellRank自动检测初始、中间和终末群体，预测命运潜力，并可视化沿着个体谱系的连续基因表达趋势。应用于谱系追踪的细胞重编程数据，预测的命运概率正确地恢复重编程结果。CellRank还预测了损伤后肺再生过程中新的去分化轨迹，包括以前未知的中间细胞状态，我们通过实验证实了这一点。CellRank推断定向细胞状态转换和细胞命运，将RNA速度信息并入基于图的马尔可夫过程。
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.
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影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
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影响因子：
8.1
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Bastidas-Ponce A;Roscioni SS;Burtscher I;Bader E;Sterr M;Bakhti M;Lickert H
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作者：
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通讯作者：
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