Breakthrough in Nanomechanical Analysis: Shenyang University of Technology Develops Deep Learning-Powered AFM Method

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Researchers from the Shenyang University of Technology in China have made a significant breakthrough in the field of nanomechanical analysis by developing a deep learning-powered atomic force microscopy (AFM) method that enables reliable and laborsaving AFM force measurements of numerous cells on diverse biointerfaces. The new method combines AFM-based single-cell indentation assay with vision foundation model-enabled image recognition, allowing for the accurate recognition of cells in real-time and the achievement of autonomous high-efficient AFM single-cell indentation assay.

The study, published in the journal Biochemical and Biophysical Research Communications, demonstrated the effectiveness of the proposed method on a variety of commonly used substrates, including regular cell culture dishes, hydrogels, microgrooves, and micropillars. The research highlights the potential of deep learning to enhance the capability of the AFM-based force spectroscopy toolbox for probing cell-ECM interactions, which is significant for the advancement of the field of mechanobiology.

Key Takeaways:

  • The research developed a deep learning-powered AFM method that enables reliable and laborsaving AFM force measurements of numerous cells on diverse biointerfaces.
  • The new method combines AFM-based single-cell indentation assay with vision foundation model-enabled image recognition, allowing for the accurate recognition of cells in real-time.
  • The study demonstrated the effectiveness of the proposed method on a variety of commonly used substrates, including regular cell culture dishes, hydrogels, microgrooves, and micropillars.
  • The research highlights the potential of deep learning to enhance the capability of the AFM-based force spectroscopy toolbox for probing cell-ECM interactions.
  • The study has been peer-reviewed and published in the journal Biochemical and Biophysical Research Communications.
  • The research has significant implications for the advancement of the field of mechanobiology.
  • The research team includes Zhihui Zhang, Haodong Huang, Lianqing Liu, and Mi Li from the Shenyang University of Technology.

Statistics:

  • The study used a pre-trained deep learning model to achieve autonomous high-efficient AFM single-cell indentation assay.
  • The research demonstrated the effectiveness of the proposed method on a variety of commonly used substrates.
  • The study highlighted the potential of deep learning to enhance the capability of the AFM-based force spectroscopy toolbox for probing cell-ECM interactions.
  • The research has been published in the journal Biochemical and Biophysical Research Communications.
  • The study is a significant breakthrough in the field of nanomechanical analysis.
  • The research team includes experts from the School of Artificial Intelligence at the Shenyang University of Technology.

Sources:

  • Deep learning-powered high-efficient atomic force microscopy single-cell nanomechanical analysis on diverse biointerfaces. Biochemical and Biophysical Research Communications, 2025;786:152761.
  • Biochemical and Biophysical Research Communications. Academic Press Inc Elsevier Science, 525 B St, Ste 1900, San Diego, CA 92101-4495, USA.
  • Shenyang University of Technology. School of Artificial Intelligence. Zhihui Zhang, Haodong Huang, Lianqing Liu, and Mi Li.
  • NewsRx. Studies from Shenyang University of Technology Provide New Data on Nanomechanical (Deep learning-powered high-efficient atomic force microscopy single-cell nanomechanical analysis on diverse biointerfaces). Nanotechnology Weekly. October 20, 2025; p 2149.