Single-cell network biology for resolving cellular heterogeneity in human diseases.
Single-cell network biology for resolving cellular heterogeneity in human diseases.
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DOI：
10.1038/s12276-020-00528-0
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发表时间：
2020-11
影响因子：
12.8
通讯作者：
Lee I
中科院分区：
文献类型：
--
作者：
Cha J;Lee I
了解细胞的异质性是生物学和医学的圣杯。在多细胞生物体中，具有相同基因组的细胞表现出各种各样的行为。遗传电路的基础细胞类型的身份将促进不同的细胞状态的分化和维持的监管程序的理解。这种细胞类型特异性基因网络可以从单个细胞的共调控模式中推断出来。使用组织样品的转录组谱分析的常规方法仅提供不同细胞类型的平均信号。因此，用基于组织的转录组数据重建特定细胞类型的基因调控网络是不可行的。最近，单细胞组学技术出现，能够捕获每个细胞的转录组景观。虽然单细胞基因表达研究已经开辟了新的途径，但使用单细胞转录组数据的网络生物学将进一步加速我们对细胞异质性的理解。在这篇综述中，我们提供了单细胞网络生物学的概述，并总结了从单细胞RNA测序（scRNA-seq）数据进行网络推断的方法开发的最新进展。然后，我们描述了细胞类型特异性基因网络如何被用来研究特定于疾病相关细胞类型和细胞状态的调控程序。此外，利用scRNA数据，对个人或患者特异性基因网络进行建模是可行的。因此，我们还介绍了单细胞网络生物学在精准医学中的潜在应用。我们设想在不久的将来，系统生物学将快速转向单细胞网络分析。从单细胞RNA测序数据集重建的基因调控网络使研究人员能够更好地了解导致复杂人类疾病的分子电路和细胞状态。韩国延世大学首尔的Junha Cha和Insuk Lee回顾了“单细胞网络生物学”的概念，该概念涉及使用计算算法对数千个细胞的基因表达数据进行计算，以推断各种生物背景下的功能相互作用。这种分析单细胞中信使RNA谱的系统生物学方法正在帮助研究人员发现新的信号通路，这些通路可以作为疾病生物标志物或治疗靶点。在未来，患者特定的个人基因网络模型可以解释为什么某些遗传变异会影响疾病风险。这项研究也可能最终导致新型的个性化医疗。
Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future. Gene regulatory networks reconstructed from single-cell RNA sequencing datasets are allowing researchers to better understand the molecular circuits and cell states that contribute to complex human disease. Junha Cha and Insuk Lee from Yonsei University in Seoul, South Korea, review the concept of ‘single-cell network biology’, which involves using computational algorithms on genetic expression data from thousands of cells to infer functional interactions in various biological contexts. This systems biology approach to analyzing the profiles of messenger RNA in single cells is helping researchers discover new signaling pathways that could serve as disease biomarkers or therapeutic targets. In the future, patient-specific models of personal gene networks could explain why certain genetic variants affect disease risk. This research could also eventually lead to new types of individualized medical treatments.
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