AFM-Based Deep Learning Decodes Human Macrophage Mechanophenotypes.

Journal: Small methods
Published Date: Jul 28, 2025

Abstract

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.

Authors

  • Jiaxin Chen
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Wenjie Yang
    Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. lisa_ywj@163.com.
  • Haonan Li
    Department of Biotechnology, College of Basic Medical Sciences, Dalian Medical University, China (H.L., J.W.).
  • Qi Li
    The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
  • Su Li
    School of Automation, Chongqing University, Chongqing, China.
  • Yingnan Liu
    Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
  • Fangfang Liu
    Art College, Southwest Minzu University, Sichuan, China.
  • Yunping Xu
    Institution of Transfusion Medicine, Shenzhen Blood Center, Shenzhen, Guangdong, 518000, China.
  • Yan-Zhong Chang
    College of Life Science, Hebei Normal University, Shijiazhuang, Hebei, 050016, China.
  • Martin Himly
    Division of Allergy & Immunology, Department of Biosciences & Medical Biology, Paris Lodron University of Salzburg, Salzburg, 5020, Austria.
  • Paola Italiani
    Institute of Biochemistry and Cell Biology, National Research Council (CNR), Napoli, 80131, Italy.
  • Diana Boraschi
    Laboratory of Inflammation and Vaccines, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
  • Guofang Zhang
    Shanghai Urban Development Research Institute Co., Ltd, Shanghai 200030, PR China.
  • Massimiliano Galluzzi
    Laboratory of Inflammation and Vaccines, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals