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

IF 36.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Nature Methods Pub Date : 2024-10-31 DOI:10.1038/s41592-024-02471-8
Alex R Lederer, Maxine Leonardi, Lorenzo Talamanca, Daniil M Bobrovskiy, Antonio Herrera, Colas Droin, Irina Khven, Hugo J F Carvalho, Alessandro Valente, Albert Dominguez Mantes, Pau Mulet Arabí, Luca Pinello, Felix Naef, Gioele La Manno
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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.

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利用流形约束的 RNA 速度模型进行统计推断,揭示细胞周期的速度调节。
在整个生物系统中,细胞的基因表达会发生协调变化,导致转录组动态在低维流形中展开。虽然可以利用 RNA 速度提取低维动态,但这些算法可能很脆弱,而且依赖于缺乏统计控制的启发式方法。此外,估计的矢量场与遍历的基因表达流形在动态上并不一致。为了应对这些挑战,我们引入了 RNA 速度的贝叶斯模型,该模型在一个重新制定的统一框架中将速度场和流形估计结合起来,确定了一个显式动态系统的参数。我们以细胞周期为重点,利用 VeloCycle 研究一维周期流形上的基因调控动态,并利用实时成像验证其推断细胞周期周期的能力。我们还应用 VeloCycle 揭示了区域定义的祖细胞和 Perturb-seq 基因敲除的速度差异。总之,VeloCycle 以模块化和统计一致的 RNA 速度推断框架扩展了单细胞 RNA 测序分析工具包。
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来源期刊
Nature Methods
Nature Methods 生物-生化研究方法
CiteScore
58.70
自引率
1.70%
发文量
326
审稿时长
1 months
期刊介绍: Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
期刊最新文献
Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics. Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations. Uncovering cell cycle speed modulations with statistical inference. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics. Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.
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