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In Nano letters ; h5-index 188.0

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.

Wang Hongxin, Zhang Han, Da Bo, Lu Dabao, Tamura Ryo, Goto Kenta, Watanabe Ikumu, Fujita Daisuke, Hanagata Nobutaka, Kano Junko, Nakagawa Tomoki, Noguchi Masayuki


Living cell cytology, Machine learning, Mechanobiology, Mechanomics, Medical AFM