瑞士洛桑联邦理工学院Gioele La Manno等研究人员合作发现，统计推断与一个流形约束的RNA速度模型相结合揭示细胞周期速度的调节。2024年10月31日，《自然—方法学》杂志在线发表了这项成果。

研究人员表示，在生物系统中，细胞经历协调的基因表达变化，导致转录组动态在低维流形中展开。虽然可以使用RNA速度提取低维动态，但这些算法可能脆弱，并依赖于缺乏统计控制的启发式方法。此外，估计的向量场与经过的基因表达流形并不动态一致。

为了解决这些挑战，研究人员引入了一种RNA速度的贝叶斯模型，将速度场和流形估计耦合在一个重新构建的统一框架中，从而识别显式动态系统的参数。研究人员专注于细胞周期，开发了VeloCycle以研究一维周期性流形上的基因调控动态，并使用实时成像验证其推断细胞周期周期性的能力。使用实时成像还应用VeloCycle揭示了在区域性定义的祖细胞和Perturb-seq基因敲除中速度差异。

总体而言，VeloCycle扩展了单细胞RNA测序分析工具包，提供了一个模块化和统计一致的RNA速度推断框架。

附：英文原文

Title: Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Author: Lederer, Alex R., Leonardi, Maxine, Talamanca, Lorenzo, Bobrovskiy, Daniil M., Herrera, Antonio, Droin, Colas, Khven, Irina, Carvalho, Hugo J. F., Valente, Alessandro, Dominguez Mantes, Albert, Mulet Arab, Pau, Pinello, Luca, Naef, Felix, La Manno, Gioele

Issue&Volume: 2024-10-31

Abstract: Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

DOI: 10.1038/s41592-024-02471-8

Source: https://www.nature.com/articles/s41592-024-02471-8