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Related Concept Videos

Atomic Force Microscopy01:08

Atomic Force Microscopy

4.4K
Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
4.4K

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Related Experiment Video

Updated: Jan 15, 2026

Functionalization of Atomic Force Microscope Cantilevers with Single-T Cells or Single-Particle for Immunological Single-Cell Force Spectroscopy
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Functionalization of Atomic Force Microscope Cantilevers with Single-T Cells or Single-Particle for Immunological Single-Cell Force Spectroscopy

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Deep learning-powered high-efficient atomic force microscopy single-cell nanomechanical analysis on diverse

Haodong Huang1, Zhihui Zhang2, Lianqing Liu3

  • 1School of Artificial Intelligence, Shenyang University of Technology, Shenyang, 110870, China; State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China.

Biochemical and Biophysical Research Communications
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated atomic force microscopy (AFM) method using deep learning for efficient cell-ECM interaction analysis. This approach enhances throughput for mechanobiology research.

Keywords:
Atomic force microscopyDeep learning image recognitionExtracellular matrixHydrogelMicrogrooveMicropillar

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Area of Science:

  • Mechanobiology
  • Biophysics
  • Cellular Mechanics

Background:

  • The extracellular matrix (ECM) significantly influences cellular behavior.
  • Understanding cell-ECM interactions is vital for physiology and pathology.
  • Atomic force microscopy (AFM) is essential for single-cell force measurements but requires improved throughput and automation.

Purpose of the Study:

  • To develop a laborsaving and high-throughput method for AFM-based single-cell force measurements.
  • To integrate vision foundation models with AFM for automated cell recognition and indentation assays.
  • To enhance the study of cell-ECM interactions using advanced deep learning techniques.

Main Methods:

  • Combined AFM indentation assay with vision foundation model-enabled image recognition.
  • Utilized a pre-trained deep learning model for real-time cell recognition in bright-field images.
  • Achieved autonomous and high-efficiency AFM single-cell indentation assays.

Main Results:

  • Demonstrated reliable and laborsaving AFM force measurements on diverse biointerfaces.
  • Successfully verified the method's effectiveness on various substrates like hydrogels and microstructured surfaces.
  • Enabled accurate, real-time cell recognition for autonomous AFM assays.

Conclusions:

  • The proposed deep learning-enhanced AFM method significantly improves the capability of force spectroscopy for probing cell-ECM interactions.
  • This approach offers a promising advancement for mechanobiology research.
  • Facilitates a comprehensive understanding of physiological and pathological processes driven by cell-ECM dynamics.