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

Choi, S; Hill, D; Guo, L; Nicholas, R; Papadopoulos, D; Cordeiro, MF; (2022) Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms. Scientific Reports , 12 , Article 1806. 10.1038/s41598-022-05815-6. Green open access

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Abstract

The resident macrophages of the central nervous system, microglia, are becoming increasingly implicated as active participants in neuropathology and ageing. Their diverse and changeable morphology is tightly linked with functions they perform, enabling assessment of their activity through image analysis. To better understand the contributions of microglia in health, senescence, and disease, it is necessary to measure morphology with both speed and reliability. A machine learning approach was developed to facilitate automatic classification of images of retinal microglial cells as one of five morphotypes, using a support vector machine (SVM). The area under the receiver operating characteristic curve for this SVM was between 0.99 and 1, indicating strong performance. The densities of the different microglial morphologies were automatically assessed (using the SVM) within wholemount retinal images. Retinas used in the study were sourced from 28 healthy C57/BL6 mice split over three age points (2, 6, and 28-months). The prevalence of ‘activated’ microglial morphology was significantly higher at 6- and 28-months compared to 2-months (p <.05 and p <.01 respectively), and ‘rod’ significantly higher at 6-months than 28-months (p < 0.01). The results of the present study propose a robust cell classification SVM, and further evidence of the dynamic role microglia play in ageing.

Type: Article
Title: Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41598-022-05815-6
Publisher version: https://doi.org/10.1038/s41598-022-05815-6
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10143803
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