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Automated characterisation of microglia in ageing mice using image processing and supervised machine learning

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Microglia, the immune cells of the brain, change shape with age. This study developed a machine learning tool to automatically classify microglial morphology, revealing significant age-related changes in their activation states.

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Area of Science:

  • Neuroscience
  • Immunology
  • Computational Biology

Background:

  • Microglia, the resident macrophages of the central nervous system, are increasingly recognized for their roles in neuropathology and aging.
  • Microglial morphology is closely linked to their function, making it a key indicator of their activity.
  • Accurate and efficient methods are needed to quantify microglial morphology for research into health, senescence, and disease.

Purpose of the Study:

  • To develop and validate a machine learning model for the automatic classification of microglial morphology.
  • To assess age-related changes in microglial morphology in the mouse retina.

Main Methods:

  • A support vector machine (SVM) machine learning model was trained to classify retinal microglial cells into five morphotypes.
  • The SVM model achieved high performance with an area under the receiver operating characteristic curve between 0.99 and 1.
  • The densities of different microglial morphologies were automatically quantified in wholemount retinal images from mice aged 2, 6, and 28 months.

Main Results:

  • The prevalence of 'activated' microglial morphology significantly increased with age (6 and 28 months vs. 2 months).
  • A 'rod' morphology was significantly more prevalent at 6 months compared to 28 months.
  • The study demonstrated the robust performance of the SVM for cell classification and provided evidence for dynamic microglial changes during aging.

Conclusions:

  • A reliable machine learning-based SVM classifier for microglial morphology was developed.
  • Significant age-dependent alterations in microglial morphology were observed in the mouse retina.
  • These findings highlight the dynamic involvement of microglia in the aging process.