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Atomic Force Microscopy

Atomic Force Microscopy

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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Related Experiment Video

Updated: Jul 23, 2025

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

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

Published on: July 10, 2019

Cell recognition based on atomic force microscopy and modified residual neural network.

Junxi Wang¹, Mingyan Gao¹, Lixin Yang²

¹International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China; Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China.

Journal of Structural Biology

|July 14, 2023

Summary

This summary is machine-generated.

This study introduces a novel deep learning approach using atomic force microscopy (AFM) for accurate cell type recognition. The method effectively analyzes physical properties, enabling universal automated cell information analysis.

Keywords:

Atomic force microscopy Cell mechanical property Cell recognition Cell surface morphology Deep learning technology

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Area of Science:

Cell biology
Biophysics
Medical diagnostics

Background:

Cell recognition is crucial in biology and medicine.
Atomic Force Microscopy (AFM) offers valuable cell imaging.
Current methods using morphology or mechanical properties have limitations for general cancer detection.

Purpose of the Study:

To develop a universal and effective automated method for cell type recognition.
To leverage physical properties of cells combined with deep learning for enhanced analysis.
To optimize feature extraction and reduce computational costs in cell recognition.

Main Methods:

Utilized Atomic Force Microscopy (AFM) for cell imaging.
Employed a modified residual neural network with multi-scale convolutional fusion, attention mechanism, and depthwise separable convolution.
Analyzed cell surface morphology, adhesion, and Young's modulus for recognition.

Main Results:

Achieved high recognition rates for different cell groups (HL-7702/SMMC-7721 and SGC-7901/GES-1).
Demonstrated that combining physical properties (morphology, adhesion, Young's modulus) improves recognition accuracy.
Showcased improved recognition with optimal image resolution.

Conclusions:

Deep learning analysis of cell physical properties provides a universal method for automated cell recognition.
The optimized convolutional neural network enhances feature extraction efficiency and reduces operational costs.
This approach offers a promising tool for automated cell information analysis in biology and medicine.