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We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply machine-learning approaches to discriminate different cell types and states. When employing the full 15 dimensional dataset, the technique robustly classifies individual cells based on their pluripotency, with accuracy above 95%. Rheological and morphological properties of cells while deforming were critical for this classification. We also show the application of this method in accurate classifying cells based on their viability, drug screening and detecting populations of malignant cells in mixed samples. We show that some of the extracted parameters are not linearly independent, and in fact we reach maximum classification accuracy by using only a subset of parameters. However, the informative subsets could vary depending on cell types in the sample. This work shows the utility of an assay purely based on intrinsic biophysical properties of cells to identify changes in cell state. In addition to a label-free alternative to flow cytometry in certain applications, this work, also can provide novel intracellular metrics that would not be feasible with labeled approaches (i.e. flow cytometry).

Micro-scale biological tools that have allowed probing of individual cells--from the genetic, to proteomic, to phenotypic level--have revealed important contributions of single cells to direct normal and diseased body processes. In analyzing single cells, sample heterogeneity between and within specific cell types drives the need for high-throughput and quantitative measurement of cellular parameters. In recent years, high-throughput single-cell analysis platforms have revealed rare genetic subpopulations in growing tumors, begun to uncover the mechanisms of antibiotic resistance in bacteria, and described the cell-to-cell variations in stem cell differentiation and immune cell response to activation by pathogens. This review surveys these recent technologies, presenting their strengths and contributions to the field, and identifies needs still unmet toward the development of high-throughput single-cell analysis tools to benefit life science research and clinical diagnostics.