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Related Concept Videos

Atomic Force Microscopy01:08

Atomic Force Microscopy

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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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Studying the Cytoskeleton01:17

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The cytoskeletal architecture can be studied using different microscopic and biochemical techniques. Electron microscopy was instrumental in discovering the cytoskeletal architecture around the 1960s, which allowed obtaining structural information at a high-resolution level. However, the sample preparation procedure often limits this ability in biological samples. Several protocols have been developed over the years to optimize sample preparation. In one of the protocols known as rotary...
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Measurement of Liver Stiffness Using Atomic Force Microscopy Coupled with Polarization Microscopy
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Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy

Gauthier Gadouas1,2,3, Guillaume Tosato1,2,3,4, Luca Costa3,5

  • 1Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U1194, 208 Avenue Des Apothicaires, 34298, Montpellier, France.

Scientific Reports
|March 21, 2026
PubMed
Summary

This study links colon cancer tissue stiffness to clinical factors using atomic force microscopy and AI. Findings reveal novel associations with tumor characteristics, aiding in understanding cancer progression and therapy resistance.

Keywords:
Atomic force microscopyColon cancerMachine learningRheologySpatial biologySpatial statistics

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Area of Science:

  • Biophysics
  • Computational Biology
  • Oncology

Background:

  • Cellular physical forces are crucial in biological processes.
  • Tumor stiffness is linked to cancer progression, tumorigenicity, and therapy resistance.

Purpose of the Study:

  • To integrate atomic force microscopy, statistical modeling, and machine learning in colon cancer.
  • To correlate local tissue stiffness maps with clinical and phenotypical parameters.

Main Methods:

  • Utilized atomic force microscopy to map colon cancer tissue stiffness.
  • Applied statistical modeling to identify associations between stiffness and clinical data.
  • Employed machine learning to infer clinical parameters from stiffness data.

Main Results:

  • Identified known associations (age, tumor stage) and novel links (RAS mutations, colon localization, DNA repair deficiencies) with stiffness.
  • Machine learning models successfully inferred clinical parameters from stiffness data.
  • Developed a computational framework for integrated analysis of clinical parameters and stiffness.

Conclusions:

  • Tissue stiffness is a significant factor in colon cancer, associated with various clinical and molecular characteristics.
  • The integrated computational approach provides a framework for understanding tumor heterogeneity and progression.
  • This methodology can advance personalized medicine by linking mechanical properties to clinical outcomes.