Manchel Alexandra, Gee Michelle, Vadigepalli Rajanikanth
Daniel Baugh Institute of Functional Genomics/Computational Biology, Department of Pathology and Genomic Medicine, Thomas Jefferson University, Philadelphia, PA, USA.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA.
iScience. 2024 Nov 5;27(12):111322. doi: 10.1016/j.isci.2024.111322. eCollection 2024 Dec 20.
As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.
随着单细胞组学数据采样和获取方法以前所未有的速度不断积累，已开发出各种数据分析流程，用于推断细胞类型、细胞状态及其分布、状态转变、状态轨迹和状态相互作用。这带来了一个新机遇，即可以利用单细胞组学数据来生成高分辨率、高保真度的计算模型。在本综述中，我们讨论了如何利用单细胞组学数据构建计算模型，以在不同尺度上模拟生物系统。我们提出，可以将单细胞数据与生理信息整合，以生成器官特异性模型，然后将这些模型组装起来，生成多器官系统病理生理模型。最后，我们讨论了如何将通用的多器官模型提升到患者特异性水平，从而使其能够应用于临床环境。
