Nanoscale stress microscopic analysis by fusion of machine learning and atomic force microscope spectroscopy

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Cells rely on cytoskeletal prestress to sense and transmit external force signals to induce chemical and mechanical responses. The intracellular spatial distribution of prestress thus determines the basic cytoskeletal functionalities of maintaining cellular homeostasis, which is lacking in diseased cell status including cancer. In this work, we measured spatially resolved prestress produced by local actomyosin machinery in a living cell with multivariable AFM force spectroscopy. The mechanical stimuli applied by the AFM probe were found to induce cellular prestress responses, in both normal cells and cancer cells, but in distinctively different manner at time scales from sub-second to hours. We demonstrated that a simple machine learning algorithm can be applied on un-segmented prestress distribution data to differentiate cancer cells and normal cells with accuracy above 95%. It promises a new biomarker for cancer cytology diagnosis on difficult cell specimens with high morphological similarities.