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Prediction of single cell mechanical properties in microchannels based on deep learning
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Prediction of single cell mechanical properties in microchannels based on deep learning
Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a require-ment for advanced technical skills.To overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aim-ing at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels.In the present study,the computa-tional fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that character-ize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)models.Utilizing this dataset,a multi-input convolutional neu-ral network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional information.This approach accurately predicts the cell elastic modulus,with a coefficient of determination R2 of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089％.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R2 of 0.99,even when the stochastic noise is added to the simulated data.One significant fea-ture of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied.The model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to generalize.We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization.This algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical prop-erties.
Jiajie GONG、Xinyue LIU、Yancong ZHANG、Fengping ZHU、Guohui HU
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Shanghai Institute of Applied Mathematics and Mechanics,School of Mechanics and Engineering Science,Shanghai Frontier Science Center of Mechanoinformatics,Shanghai Key Laboratory of Mechanics in Energy Engineering,Shanghai University,Shanghai 200072,China
Shanghai Institute of Aircraft Mechanics and Control,Shanghai 200092,China
Department of Neurosurgery,Huashan Hospital,Shanghai Medical College,Fudan University,Shanghai 200040,China
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