New horizons in the stormy sea of multimodal single-cell data integration.
New horizons in the stormy sea of multimodal single-cell data integration.
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多模式单单元数据集成的风暴海洋中的新视野。
DOI：
10.1016/j.molcel.2021.12.012
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发表时间：
2022-01-20
期刊：
影响因子：
16
通讯作者：
Vogel C
中科院分区：
文献类型：
--
作者：
Jackson CA;Vogel C
虽然RNA表达的测量已经主导了单细胞分析的世界，但新的单细胞技术越来越多地允许收集不同的数据模式，测量不同的分子，结构连接和分子间相互作用。整合由此产生的多模态单细胞数据集是一个新的生物信息学挑战。同样重要的是，这对实验室科学家来说是一个新的实验设计挑战，他们不仅要为每种数据模式选择无数的技术，而且还面临着实验设计的新挑战。最终目标是设计，执行和分析多模式单细胞实验，这些实验不仅仅是描述性的，而且能够学习新的因果和机械生物学。这一目标需要严格考虑分析背后的目标，其范围可能从绘制细胞群体的异质性到组装系统范围的因果网络，这可以进一步理解细胞功能，并最终导致组织和器官的模型。我们回顾了实现这一目标的步骤和挑战。单细胞转录组学现在是一项成熟的技术;在单细胞水平上测量蛋白质、脂质、小分子代谢物和其他分子表型的方法正在迅速发展。整合这些单细胞读数，使得每个细胞具有多种类型的数据的测量，例如转录组、蛋白质组和代谢组，预期允许鉴定高度特异性的细胞亚群，并为推断因果生物学机制提供基础。
While measurements of RNA expression have dominated the world of single cell analyses, new single cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating of the resulting multi-modal single cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality, but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multi-modal single cell experiments which are more than just descriptive, but enable learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks which can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges towards this goal. Single cell transcriptomics is now a mature technology; methods to measure proteins, lipids, small molecule metabolites, and other molecular phenotypes at the single cell level are rapidly developing. Integrating these single cell readouts, so that each cell has measurements of multiple types of data, e.g. transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.
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