Deep-Learning-Based Nanomechanical Vibration for Rapid and Label-Free Assay of Epithelial Mesenchymal Transition

Abstract

Cancer is a profound danger to our life and health. The classification and related studies of epithelial and mesenchymal phenotypes of cancer cells are key scientific questions in cancer research. Here, we investigated cancer cell colonies from a mechanical perspective and developed an assay for classifying epithelial/mesenchymal cancer cell colonies using the biomechanical fingerprint in the form of "nanovibration" in combination with deep learning. The classification method requires only 1 s of vibration data and has a classification accuracy of nearly 92.5%. The method has also been validated for the screening of anticancer drugs. Compared with traditional methods, the method has the advantages of being nondestructive, label-free, and highly sensitive. Furthermore, we proposed a perspective that subcellular structure influences the amplitude and spectrum of nanovibrations and demonstrated it using experiments and numerical simulation. These findings allow internal changes in the cell colony to be manifested by nanovibrations. This work provides a perspective and an ancillary method for cancer cell phenotype diagnosis and promotes the study of biomechanical mechanisms of cancer progression.