1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.
SLAS Discov. 2017 Mar;22(3):213-237. doi: 10.1177/2472555216682725. Epub 2017 Jan 6.
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
异质性是所有尺度生物系统的基本特性,必须在广泛的生物医学应用中加以解决,包括基础生物医学研究、药物发现、诊断和精准医学的实施。有许多已发表的方法可用于描述体外细胞和组织切片中的异质性。然而,目前还没有普遍接受的方法来检测和定量可应用于相对高通量工作流程的异质性。本综述和观点强调了捕获多路细胞水平数据的实验方法,以及需要对异质性的空间、时间和群体成分进行标准化度量的必要性。建议采用一组三个异质性指数,可将其应用于任何高通量工作流程,以优化决策过程。此外,还建议采用成对互信息方法来描述异质性的空间特征,特别是在基于组织的成像中。此外,时间异质性的度量还处于早期发展阶段。示例研究表明,对功能表型异质性的分析可用于指导对生物医学实验、药物发现、诊断以及为个体患者设计最佳治疗策略的解释的决策。