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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: May 30, 2025

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Published on: April 2, 2021

Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence.

Ophélie Thomas-Chemin¹, Sébastien Janel², Zeyd Boumehdi¹

¹LAAS-CNRS, CNRS, Université de Toulouse, 31400 Toulouse, France.

|January 30, 2025

Summary

This summary is machine-generated.

Atomic force microscopy (AFM) shows promise in biology but faces low data throughput, hindering its use in diagnostics. Automation and artificial intelligence (AI) are key to improving bio-AFM for medical applications.

Keywords:

AFM data processing Artificial intelligence (AI)Atomic force microscopy (AFM)Automation Bio-AFM Cellular analysis High-throughput AFM Machine learning Mechanical properties

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Last Updated: May 30, 2025

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

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Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Published on: March 20, 2018

Area of Science:

Biophysics
Cell Biology
Medical Diagnostics

Background:

Atomic force microscopy (AFM) is a mature technique in biology, providing topographical, mechanical, and adhesion data.
AFM is applied across molecular, cellular, and tissue scales in numerous biological studies.
Despite its capabilities, AFM is not yet a standard diagnostic tool in the biomedical field.

Purpose of the Study:

To identify the reasons limiting the diagnostic application of AFM in biomedicine.
To highlight the challenge of low data throughput in biological AFM (bio-AFM).
To review advancements in automating bio-AFM measurements and data analysis for diagnostics.

Main Methods:

Review of current automation efforts for AFM measurements on living cells.
Examination of developments in automated AFM data analysis.
Exploration of artificial intelligence (AI) applications for classifying cellular and tissue data.

Main Results:

Low data throughput is a primary limitation for bio-AFM in diagnostics.
Automation of AFM measurements and data analysis is progressing.
AI shows potential for distinguishing healthy from diseased cells/tissues using AFM data.

Conclusions:

Improving automation and data analysis, particularly with AI, is crucial for bio-AFM adoption.
A roadmap is proposed to facilitate the integration of bio-AFM into medical diagnostics.
Overcoming throughput limitations can unlock AFM's diagnostic potential.