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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Comparing and Correcting Spectral Sensitivities between Multispectral Microscopes: A Prerequisite to Clinical

Margaret Eminizer1,2, Melinda Nagy1,2, Elizabeth L Engle3,4,5

  • 1Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21210, USA.

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|June 28, 2023
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Summary

Standardizing multispectral, multiplex immunofluorescence (mIF) microscopy is crucial for reproducible diagnostics. A simple calibration model significantly reduces image variations across different microscopes, enhancing clinical pathology applications.

Keywords:
calibrationhigh throughputimagingimmunofluorescence microscopypathologysystematics

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Area of Science:

  • Biomedical Imaging
  • Pathology
  • Computational Biology

Background:

  • Multispectral, multiplex immunofluorescence (mIF) microscopy enables detailed cellular analysis in tissues.
  • Reproducibility and standardization of mIF image data across different microscopes are essential for diagnostic applications.
  • Current mIF technologies require robust methods to ensure consistent data quality.

Purpose of the Study:

  • To characterize and correct variations in illumination intensity and spectral sensitivity among different mIF microscopes.
  • To develop and validate a calibration model for standardizing mIF image data.
  • To assess the impact of standardization on reducing image variability and improving marker expression accuracy.

Main Methods:

  • Scanning eight melanoma tissue samples on three different mIF microscopes.
  • Calculating average tissue region flux intensities to identify baseline variations.
  • Applying a sample-specific, microscope-specific calibration model to correct brightness and spectral differences.
  • Validating the calibration model on independent sample subsets.

Main Results:

  • Baseline average standard deviation across microscopes was 29.9%, reduced to 13.9% after initial corrections.
  • Calibration model application reduced variation to 2.9 ± 0.03% on validation subsets.
  • Variations in unmixed marker expressions decreased from 15.8% to 4.4% after correction.

Conclusions:

  • A simple calibration model can effectively standardize mIF microscopes.
  • Standardization significantly reduces image data variability, improving reproducibility.
  • Standardized mIF microscopy is feasible for clinical pathology laboratories.