1 Department of Mechanical & Industrial Engineering, Institute of Biomaterials & Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
2 Division of Engineering Science, University of Toronto, Toronto, ON, Canada.
SLAS Discov. 2018 Jun;23(5):459-473. doi: 10.1177/2472555217738533. Epub 2017 Oct 19.
Recent advances in cell-based assays have involved the integration of single-cell analyses and microfluidics technology to facilitate both high-content and high-throughput applications. These technical advances have yielded large datasets with single-cell resolution, and have given rise to the study of cell population dynamics, but statistical analyses of these populations and their properties have received much less attention, particularly for cells cultured in microfluidic systems. The objective of this study was to perform statistical analyses using Pittsburgh Heterogeneity Indices (PHIs) to understand the heterogeneity and evolution of cell population demographics on datasets generated from a microfluidic single-cell-resolution cell-based assay. We applied PHIs to cell population data obtained from studies involving drug response and soluble factor signaling of multiple myeloma cancer cells, and investigated effects of reducing population size in the microfluidic assay on both the PHIs and traditional population-averaged readouts. Results showed that PHIs are useful for examining changing population distributions within a microfluidic setting. Furthermore, PHIs provided data in support of finding the minimum population size for a microfluidic assay without altering the heterogeneity indices of the cell population. This work will be useful for novel assay development, and for advancing the integration of microfluidics, cell-based assays, and heterogeneity analyses.
近年来，基于细胞的分析方法取得了进展，涉及单细胞分析和微流控技术的整合，以促进高通量和高内涵应用。这些技术进步产生了具有单细胞分辨率的大型数据集，并引发了对细胞群体动态的研究，但对这些群体及其特性的统计分析却受到的关注较少，特别是对于在微流控系统中培养的细胞。本研究的目的是使用匹兹堡异质性指数（PHI）进行统计分析，以了解微流控单细胞分辨率细胞分析产生的数据集上细胞群体人口统计学的异质性和演变。我们将 PHI 应用于从涉及多发性骨髓瘤癌细胞药物反应和可溶性因子信号的研究中获得的细胞群体数据，并研究了在微流控分析中减少群体大小对 PHI 和传统群体平均读数的影响。结果表明，PHI 可用于检查微流控环境中不断变化的群体分布。此外，PHI 提供的数据支持在不改变细胞群体异质性指数的情况下，找到微流控测定的最小群体大小。这项工作对于新型测定方法的开发以及推进微流控、基于细胞的测定和异质性分析的整合将非常有用。
