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Resistive Pulse Sensing (RPS) is a key label-free technology to measureparticles and single-cell size distribution. As a growing corpus of evidencesupports that cancer cells exhibit distinct mechanical phenotypes from healthycells, expanding the method from size to mechanical sensing could represent apertinent and innovative tool for cancer research. In this paper, we infer thecells compressibility by using acoustic radiation pressure to deflect flowingcells in a microchannel, and use RPS to sense the subpopulations of cells andparticles at each acoustic power level. We develop and validate a linear modelto analyze experimental data from a large number of particles. Thishigh-precision linear model is complemented by a more robust (yet lessdetailed) statistical model to analyze datasets with fewer particles. Comparedto current acoustic cell phenotyping apparatus based on video cameras, theproposed approach is not limited by the optical diffraction, frame rate, datastorage or processing speed, and may ultimately constitute a step forwardtowards point-of-care acousto-electrical phenotyping and acoustic phenotypingof nanoscale objects such as exosomes and viruses.

Mechanical characterization of cells and microspheres sorted by acoustophoresis with in-line resistive pulse sensing