Fischer David S, Villanueva Martin A, Winter Peter S, Shalek Alex K
Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Rev Genet. 2025 Mar 10. doi: 10.1038/s41576-025-00821-6.
Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances - from single-cell and spatial profiling to CRISPR genome editing and machine learning - have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases. However, adapting systems-inspired approaches to dissect human disease complexity is challenging, given that discrepancies between the biological features of human tissues and the experimental models typically used to probe function (which we term 'translational distance') can confound insight. Here we review how samples, measurements and analyses can be contextualized within overall multiscale human disease processes to mitigate data and representation gaps. We then examine ways to bridge the translational distance between systems-inspired human discovery loops and model system validation loops to empower precision interventions in the era of single-cell genomics.
系统生物学旨在通过测量、分析和扰动的迭代循环，全面洞察细胞系统的分子运作机制。这一框架在单细胞模式生物中取得了显著成功，并且近期的实验和计算进展——从单细胞和空间分析到CRISPR基因组编辑以及机器学习——已经带来了利用此类策略预防、诊断和治疗人类疾病的令人兴奋的可能性。然而，鉴于人体组织的生物学特征与通常用于探究功能的实验模型之间的差异（我们称之为“转化距离”）可能会混淆见解，将受系统启发的方法应用于剖析人类疾病复杂性具有挑战性。在这里，我们回顾了如何在整体多尺度人类疾病过程中对样本、测量和分析进行情境化处理，以减轻数据和表征差距。然后，我们研究了弥合受系统启发的人类发现循环与模型系统验证循环之间的转化距离的方法，以便在单细胞基因组学时代实现精准干预。
