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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Sample Preparation for Mass Cytometry Analysis
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Different approaches to Imaging Mass Cytometry data analysis.

Vladan Milosevic1

  • 1Department of Clinical Medicine, Centre for Cancer Biomarkers CCBIO, University of Bergen, Bergen 5020, Norway.

Bioinformatics Advances
|April 24, 2023
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Summary
This summary is machine-generated.

Imaging Mass Cytometry (IMC) offers high-multiplex protein detection for tissue analysis, especially in cancer research. This review synthesizes classical and specialized image analysis tools for IMC data, aiding researchers in selecting appropriate methodologies.

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Area of Science:

  • Biomedical Imaging
  • Computational Pathology
  • Proteomics

Background:

  • Imaging Mass Cytometry (IMC) is an advanced platform for multiplexed protein detection in tissues.
  • IMC provides high spatial resolution data crucial for understanding tissue histology and pathophysiology.
  • Its application is particularly significant in cancer biology and tumor microenvironment research.

Purpose of the Study:

  • To systematically review classical image analysis tools applicable to IMC data.
  • To provide an overview of specialized tools developed exclusively for IMC data analysis.
  • To assist researchers in selecting optimal methodologies for their specific IMC data analysis needs.

Main Methods:

  • Literature review of existing image analysis techniques.
  • Categorization of tools based on their applicability to IMC data.
  • Synopsis of classical and novel IMC-specific analysis pipelines.

Main Results:

  • IMC data analysis requires approaches distinct from traditional microscopy image analysis.
  • A growing number of analysis tools are emerging for IMC-derived data.
  • Both general and specialized tools can be effectively utilized for IMC data interpretation.

Conclusions:

  • IMC is a powerful tool for spatial biology, particularly in oncology.
  • Effective analysis of IMC data relies on selecting appropriate computational tools.
  • This review serves as a guide for researchers navigating IMC data analysis methodologies.