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AFM-Based Deep Learning Decodes Human Macrophage Mechanophenotypes
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DOI：10.1002/smtd.202500953.png)
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Abstract
En 中文
Macrophage polarization into inflammatory (M1) and repairing/healing (M2) functional phenotypes are fundamental mechanisms in immune defensive responses, tissue repair, and disease control. Conventional phenotyping approaches based on molecular biomarkers are limited by destructive protocols, static endpoint analyses, and a disregard for the biomechanical attributes of cells. In this study, an integrated artificial intelligence (AI)-atomic force microscopy (AFM) platform is introduced that enables label-free, mechanophenotyping of macrophages at single-cell resolution. Using nanoscale force mapping, morphological and nanomechanical profiles are captured details, such as Young's modulus, adhesion, and sphericity, across diverse macrophage activation states. These profiles are interpreted through a deep neural network (DNN) trained with pixel-wise data enhancement and a meta-confidence estimator for dynamic, robust classification. The system accurately distinguishes naïve (M0), M1, and M2 functional phenotypes of human macrophages, even across donor heterogeneity, in the absence of conventional immunolabeling. The method reveals mixed macrophage polarization states and correlates cytoskeletal remodeling with mechanical biomarkers, establishing a direct link between cellular mechanics and immune function. This platform introduces a dynamic, non-destructive strategy for immune monitoring, redefining cellular mechanics as a critical dimension in diagnostic and therapeutic contexts, and laying the groundwork for the emerging field of mechanoimmunology.
Keywords:
artificial intelligence
atomic force microscope
deep learning
macrophage differentiation
mechanotransduction
Journal
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