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MIC-MAC: An automated pipeline for high-throughput characterization and classification of three-dimensional microglia

Luis Salamanca1,2, Naguib Mechawar3, Keith K Murai4

  • 1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg.

Glia
|April 16, 2019
PubMed

Insights

Researchers developed MIC-MAC, a tool to analyze microglia morphology in brain diseases. This imaging pipeline reveals distinct microglial phenotypes in mouse and human samples, aiding disease research.

Area of Science:

  • Neuroscience
  • Immunology
  • Computational Biology

Background:

  • Microglia exhibit diverse phenotypic changes in brain diseases, with their exact role remaining unclear.
  • Understanding microglial heterogeneity and their interaction with the brain microenvironment is crucial but challenging.
  • Existing methods struggle to analyze microglial phenotypes in situ, especially in human postmortem samples.

Purpose of the Study:

  • To develop an advanced tool for characterizing microglial phenotypic variations in situ.
  • To enable detailed morphological analysis of microglia in both mouse and human brain samples.
  • To facilitate unbiased, large-scale assessment of microglial states in neurological conditions.

Main Methods:

  • Development of MIC-MAC (Microglia and Immune Cells Morphologies Analyser and Classifier) pipeline.
  • Semiautomatic segmentation, extraction, and classification of microglia and immune cells from 3D confocal image stacks.
  • Application of the imaging-based approach to analyze mouse aging and human Alzheimer's disease and dementia with Lewy bodies samples.

Main Results:

  • MIC-MAC successfully performed 3D morphological characterization and classification of thousands of individual microglia in situ.
  • The pipeline identified species- and disease-specific microglial morphological phenotypes.
  • Demonstrated the tool's capability in analyzing complex neurological disease models and patient tissues.

Conclusions:

  • MIC-MAC provides a powerful, imaging-based method for analyzing microglial morphology in situ.
  • The tool enables rapid, unbiased, and large-scale characterization of microglial states.
  • MIC-MAC serves as a precision diagnostic aid for studying microglia in brain diseases across species.
Keywords:
Alzheimer's diseaseclassificationheterogeneityhigh-throughputmachine learningmicrogliamorphology

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