High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning
Atomic force microscopy (AFM) is extensively applied to measure the nanomechanical properties of living cells. Despite its popularity, some applications on mechanobiology are limited by the low throughput of the technique. Currently, the analysis of AFM-nanoindentation data is performed by model fit...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/400880 |
| Acceso en línea: | http://hdl.handle.net/10261/400880 https://api.elsevier.com/content/abstract/scopus_id/105002607625 |
| Access Level: | acceso abierto |
| Palabra clave: | atomic force microscopies mammalian cells mechanobiologies nanoindentations nanorheologies |
| Sumario: | Atomic force microscopy (AFM) is extensively applied to measure the nanomechanical properties of living cells. Despite its popularity, some applications on mechanobiology are limited by the low throughput of the technique. Currently, the analysis of AFM-nanoindentation data is performed by model fitting. Model fitting is slow, data intensive, and prone to error. Herein, a supervised machine-learning regressor is developed for transforming AFM force-distance curves into nanorheological behavior. The method reduces the computational time required to process a force volume of a cell made of 2.62 × 105 curves from several hours to minutes. In fact, the regressor increases the throughput by 50-fold. The training and the validation of the regressor are performed by using theoretical curves derived from a contact mechanics model that combined power-law rheology with bottom effect corrections and functional data analysis. The regressor predicts the modulus and the fluidity coefficient of mammalian cells with a relative error below 4%. |
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