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Affinity imaging of red blood cells using an atomic force microscope.

M Grandbois1, W Dettmann, M Benoit

  • 1Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211, USA. grandboism@missouri.edu

The Journal of Histochemistry and Cytochemistry : Official Journal of the Histochemistry Society
|April 18, 2000
PubMed
Summary

Atomic force microscopy (AFM) visualized red blood cells (RBCs) using specific lectin binding. This affinity contrast imaging method successfully distinguished between group A and O RBCs based on adhesion forces.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Biomaterials

Background:

  • Distinguishing between different cell populations, such as blood groups, is crucial in various biological and medical applications.
  • Traditional imaging methods may not always provide sufficient contrast or specificity for certain cellular features.

Purpose of the Study:

  • To develop and demonstrate a novel imaging technique using atomic force microscopy (AFM) to differentiate between group A and group O red blood cells (RBCs).
  • To establish an imaging contrast mechanism based on specific receptor-ligand interactions and adhesion forces.

Main Methods:

  • Utilized an atomic force microscope (AFM) with a tip functionalized with Helix pomatia lectin.
  • Measured the adhesion force between the AFM tip and a mixed layer of group A and O RBCs pixel by pixel.

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  • Quantitatively analyzed the rupture forces of adhesion events.

    • Main Results:

    • Successfully produced an image of the mixed RBC layer with contrast based on specific receptor-ligand pair interactions.
    • Demonstrated the ability to discriminate between group A and group O RBCs using affinity contrast.
    • Measured a mean rupture force of 65 pN on group A RBCs and 35 pN on N-acetylgalactosamine tethered to agarose beads.

    Conclusions:

    • Specific lectin-RBC interactions provide sufficient contrast for affinity-based imaging.
    • AFM can be employed to create high-resolution images distinguishing cell populations based on molecular recognition.
    • This technique offers a quantitative approach to analyzing cell surface interactions and heterogeneity.