德国慕尼黑亥姆霍兹中心Fabian J. Theis和美国纪念斯隆凯特琳癌症中心Dana Pe’er 共同合作取得了新的突破。他们为定向单细胞命运图谱绘制开发出了CellRank工具。相关论文于2022年1月13日在线发表于《自然—方法学》杂志上。

在这里，研究人员提供了CellRank (https://cellrank.org)工具，用于在多种情况下进行单细胞命运谱图绘制，包括再生、重编程和疾病，这些生物过程的方向是未知的。他们的方法结合了轨迹推断的稳健性和RNA速度的方向性信息，综合考虑了细胞命运决定的渐进性和随机性，以及速度矢量的不确定性。

在胰腺发育数据上，CellRank自动检测初始、中间和终末群体，预测命运潜能，并可视化沿各个谱系的连续基因表达趋势。应用于谱系追踪细胞重编程数据，预测的命运概率可以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，其中包括了之前未知的中间细胞状态，研究人员在实验中证实了这一点。

据了解，计算轨迹推理可以从单细胞RNA测序实验中重建细胞状态动力学。然而，轨迹推理要求生物过程的方向是已知的，这在很大程度上限制了它在正常发育系统中的应用。

附：英文原文

Title: CellRank for directed single-cell fate mapping

Author: Lange, Marius, Bergen, Volker, Klein, Michal, Setty, Manu, Reuter, Bernhard, Bakhti, Mostafa, Lickert, Heiko, Ansari, Meshal, Schniering, Janine, Schiller, Herbert B., Peer, Dana, Theis, Fabian J.

Issue&Volume: 2022-01-13

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.

DOI: 10.1038/s41592-021-01346-6

Source: https://www.nature.com/articles/s41592-021-01346-6