英国威康桑格研究所Omer Ali Bayraktar团队的一项最新研究发现了Cell2fate推断RNA速度模块以改进细胞命运预测。相关论文于2025年3月3日发表在《自然—方法学》杂志上。

在这里，课题组研究人员提出了cell2fate，这是一种基于速度ODE线性化的RNA速度公式，它允许以完全贝叶斯的方式求解生物物理上更精确的模型。因此，cell2fate将RNA速度溶液分解成模块，提供了RNA速度和统计降维之间的生物物理联系。研究小组在现实环境中对细胞命运进行了全面的基准测试，展示了增强的可解释性和能力，以重建罕见和成熟细胞类型的复杂动态和弱动态信号。最后，课题组人员将cell2fate应用于发育中的人类大脑，在那里课题组人员将RNA速度模块空间映射到组织结构上，将组织的空间组织与转录的时间动态联系起来。

据介绍，RNA速度利用剪接和未剪接RNA计数中包含的时间信息来推断转录动力学。现有的速度模型通常依赖于粗略的生物物理简化或数值近似来解决潜在的常微分方程（ODE），这可能会影响具有挑战性的设置的准确性，例如复杂或弱的转录速率变化跨越细胞轨迹。

附：英文原文

Title: Cell2fate infers RNA velocity modules to improve cell fate prediction

Author: Aivazidis, Alexander, Memi, Fani, Kleshchevnikov, Vitalii, Er, Sezgin, Clarke, Brian, Stegle, Oliver, Bayraktar, Omer Ali

Issue&Volume: 2025-03-03

Abstract: RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.

DOI: 10.1038/s41592-025-02608-3

Source: https://www.nature.com/articles/s41592-025-02608-3