Bruns Imke B, Li Yingxue, Stevens James L, van de Water Bob, Callegaro Giulia
Division of Cell Systems and Drug Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf421.
A fundamental goal of biological research is to determine the interactions and functional relationships between genes and their coded proteins that drive biological responses. Understanding the response of the global transcriptome in the context of pathogenesis and drug-related adversities can reveal gene-response relationships that contribute to biogical insights and more accurate and reliable mechanism-based safety assessments. Although transcriptomic data provide a framework to systematically determine gene activity, their high dimensionality and complexity can make interpretation and analysis challenging. Gene co-expression analysis addresses these difficulties in analyzing transcriptomics data by first constructing networks of genes that are co-expressed across treatments, reducing complexity, and then inferring biological relevance and gene-pathology associations for each network. Variation in gene expression in bulk tissue helps define co-expression relationships, but the cell type heterogeneity, inherent to bulk tissue, can also complicate biological interpretations. Consequently, interpretation of some tissue gene co-expression patterns may be subject to the confounding influence of variations in cellular composition obscuring intra-cell-type-specific co-expression network responses. In this review, we highlight methods designed to capture cell type-specific co-expression patterns and discuss their potential utility for understanding mechanisms of toxicity and pathogenesis.
生物学研究的一个基本目标是确定驱动生物反应的基因与其编码蛋白质之间的相互作用和功能关系。了解在发病机制和药物相关不良反应背景下的整体转录组反应，可以揭示有助于生物学见解以及更准确可靠的基于机制的安全性评估的基因-反应关系。虽然转录组数据提供了一个系统确定基因活性的框架，但其高维度和复杂性会使解读和分析具有挑战性。基因共表达分析通过首先构建跨处理共表达的基因网络、降低复杂性，然后推断每个网络的生物学相关性和基因-病理学关联，来解决转录组学数据分析中的这些困难。大块组织中基因表达的变化有助于定义共表达关系，但大块组织固有的细胞类型异质性也会使生物学解释变得复杂。因此，某些组织基因共表达模式的解释可能会受到细胞组成变化的混杂影响，从而掩盖细胞类型特异性共表达网络反应。在本综述中，我们重点介绍旨在捕获细胞类型特异性共表达模式的方法，并讨论它们在理解毒性和发病机制方面的潜在用途。
