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Related Concept Videos

Atomic Force Microscopy01:08

Atomic Force Microscopy

3.4K
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...
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Updated: Jun 12, 2025

Measuring the Mechanical Properties of Living Cells Using Atomic Force Microscopy
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Measuring the Mechanical Properties of Living Cells Using Atomic Force Microscopy

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Automated High-Throughput Atomic Force Microscopy Single-Cell Nanomechanical Assay Enabled by Deep Learning-Based

Rui Xiao1,2, Yanzhu Zhang1, Mi Li2

  • 1School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China.

Nano Letters
|September 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces deep learning-assisted Atomic Force Microscopy (AFM) for automated, high-throughput single-cell nanomechanical measurements. This method enhances efficiency and accuracy in mechanobiology research.

Keywords:
atomic force microscopyautomated high-throughput force spectroscopycell nucleus identificationdeep learning image recognitionsingle-cell force spectroscopysingle-cell indentation assay

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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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Area of Science:

  • Biophysics
  • Cell Biology
  • Nanotechnology

Background:

  • Mechanical forces are crucial for cellular functions, making single-cell mechanical properties a key research area.
  • Atomic Force Microscopy (AFM) is a standard tool for measuring these properties, but manual operation limits its throughput.
  • Existing AFM methods are time-consuming and operator-dependent, hindering large-scale studies.

Purpose of the Study:

  • To develop an automated, high-throughput method for single-cell nanomechanical measurements using AFM.
  • To integrate deep learning image recognition with AFM for precise cell and probe identification and manipulation.
  • To enable operator-independent and efficient nanomechanical assays on living cells.

Main Methods:

  • Utilized deep learning image recognition to identify cell structures and AFM probe in bright-field images.
  • Implemented automated sample stage and AFM probe movement based on image recognition.
  • Performed label-free identification for accurate probe positioning on specific cellular regions.
  • Conducted single-cell indentation assays and single-cell force spectroscopy.

Main Results:

  • Achieved automated, high-throughput nanomechanical measurements of single living cells.
  • Demonstrated accurate and sequential positioning of the AFM probe on specific cellular sites.
  • Significantly improved the efficiency and reduced operator dependency of AFM-based assays.
  • Validated the method for time-efficient single-cell indentation and force spectroscopy.

Conclusions:

  • Deep learning-assisted AFM provides a powerful, automated solution for high-throughput single-cell nanomechanics.
  • This technology offers operator-independent measurements, advancing mechanobiology research.
  • The method facilitates efficient and precise mechanical characterization of individual living cells.