麻省理工学院和哈佛大学的布罗德研究所Luke J. O’Connor小组的一项最新研究报道了微扰图谱中差异表达的转录组分析。该研究于2025年4月21日发表于国际一流学术期刊《自然—遗传学》杂志上。

在这里，研究团队介绍了转录组范围内的差异表达分析（TRADE），这是一种用于真实差异表达效应分布的统计模型，可以适当地解释估计误差。TRADE估计了“转录组范围内的影响”，它量化了整个转录组的扰动的总影响。通过分析几个大型的Perturb-seq数据集，小组发现许多转录效应在标准分析中未被发现，但在总体主题贸易中出现。一个典型的基因扰动影响大约45个基因，而一个典型的基本基因影响超过500个。该团队发现不同细胞类型的扰动效应具有中等程度的一致性，确定了在不同剂量水平上转录反应发生定性变化的扰动，并阐明了神经精神疾病中遗传和转录组相关性之间的关系。

据了解，单细胞CRISPR筛选，如Perturb-seq，可以大规模地对遗传扰动进行转录组分析。然而，这些屏幕产生的数据是嘈杂的，许多影响可能没有被发现。

附：英文原文

Title: Transcriptome-wide analysis of differential expression in perturbation atlases

Author: Nadig, Ajay, Replogle, Joseph M., Pogson, Angela N., Murthy, Mukundh, McCarroll, Steven A., Weissman, Jonathan S., Robinson, Elise B., OConnor, Luke J.

Issue&Volume: 2025-04-21

Abstract: Single-cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are noisy, and many effects may go undetected. Here we introduce transcriptome-wide analysis of differential expression (TRADE)—a statistical model for the distribution of true differential expression effects that accounts for estimation error appropriately. TRADE estimates the ‘transcriptome-wide impact’, which quantifies the total effect of a perturbation across the transcriptome. Analyzing several large Perturb-seq datasets, we show that many transcriptional effects remain undetected in standard analyses but emerge in aggregate using TRADE. A typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene affects over 500. We find moderate consistency of perturbation effects across cell types, identify perturbations where transcriptional responses vary qualitatively across dosage levels and clarify the relationship between genetic and transcriptomic correlations across neuropsychiatric disorders.

DOI: 10.1038/s41588-025-02169-3

Source: https://www.nature.com/articles/s41588-025-02169-3