Where AFM meets AI: profiling macrophage mechanophenotypes

Original story from the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (Shenzhen, China). A new method combining AFM with deep learning accurately profiles human macrophage mechanophenotypes. Macrophages drive key immune processes including inflammation, tissue repair and tumorigenesis via distinct polarization states, the accurate identification of which is vital for diagnosis and immunotherapy. However, methods like RNA sequencing and flow cytometry are often costly, time-consuming and unable to enable real-time, label-free, high-throughput detection. Atomic force microscopy (AFM) has emerged as a powerful tool in cell phenotyping by decoding the mechanobiological signatures of different cellular states, and AI enables rapid analysis of its complex data. However, macrophages remain underexplored using the combined approach. In a recent study, a team led by Yang Li from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences (Shenzhen, China) developed and validated a label-free, non-invasive method combining AFM with deep learning for accurate profiling of human macrophage mechanophenotypes and rapid identification of their polarization states. Genomic ‘shake up’ allows cells to play the roles of others Variability in how DNA is packaged allows cells to take on the identity of different cell types.