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.
Keywords: cancer and cancer drugs; cell-based assays; chip technology and methods; microfluidics; statistical analyses.