从时间分辨单细胞 RNA 测序的动态相关性中揭示全球转录调控。

Cell systems Pub Date : 2024-08-21 Epub Date: 2024-08-08 DOI:10.1016/j.cels.2024.07.002
Dimitris Volteras, Vahid Shahrezaei, Philipp Thomas
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引用次数: 0

摘要

单细胞转录组学揭示了细胞间转录活性的显著变化。然而,从静态快照中确定转录动态机制仍具有挑战性。因此,我们仍然不知道是什么驱动了单细胞中的全局转录动态。我们提出了一个基因表达的随机模型,该模型通过代谢标记协议和细胞周期报告器,利用单细胞 RNA 测序的时间维度,计算生长和分裂细胞中与细胞大小和细胞周期相关的速率。我们开发了一种并行且高度可扩展的近似贝叶斯计算方法,该方法可纠正技术差异,并在整个转录组范围内准确量化细胞周期中的绝对突变频率、突变大小和降解率。利用贝叶斯模型选择,我们揭示了转录率与细胞大小之间的比例关系,并揭示了依赖于细胞周期的转录组中的基因调控波。我们的研究表明,动态相关性的随机建模可以识别转录调控的全球机制。补充信息中包含了本文透明的同行评审过程记录。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing.

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

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