Nuclear mechanical properties influence transcription and cell signaling. Atomic Force Microscopy (AFM) provides high-resolution topography and mechanical properties of living cells. Here, we introduce AFMap-UNet, a deep learning architecture that integrates AFM-derived and optical microscopy data to achieve precise nuclear segmentation. AFM topography maps were combined with enhanced optical channels and processed through a two-stage, region-guided U-Net pipeline, achieving a precision-recall curve area with an average precision of 99% and a median Dice coefficient of 96%. Moreover, AFMap-UNet retains its performance even on small training sets of 15 images, highlighting scalability for data-constrained settings typical of AFM studies. To our knowledge, this represents the first high-performance deep learning model for spatially resolved quantification of nuclear mechanics, enabling new applications in AFM-based cell analysis and disease modeling.
Rocha et al. (Sun,) studied this question.