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

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.

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

“…Recent developments in computations, algorithm design, and GPU based computing have led to the exploration of machine learning in different domains of medicine and biological sciences, including in cell quantification [ 68 – 81 ]. In recent years various tools have been developed for characterization, detection, and classification of microglial and glial cells; all these tools unfortunately require one or more pre-processing steps (e.g., application of filters and threshold to enhance the structures of interest), as well as the use of several different software packages for image preparation and subsequent quantification [ 69 , 82 , 83 ].…”

Activity of a Novel Anti-Inflammatory Agent F-3,6′-dithiopomalidomide as a Treatment for Traumatic Brain Injury

“…The activation of microglia after TBI usually occurs within 24 h [ 16 , 69 , 99 ] and extends from weeks to months [ 100 ]. In response to various microenvironments, microglia are morphologically and functionally dynamic cells that can change from ramified to completely lacking processes with a larger cell body (amoeboid), usually associated with phagocytic functions [ 101 , 102 , 103 ]. Early microglial activation after TBI may induce the restoration process of homeostasis in the brain.…”

Astrocyte CCN1 stabilizes neural circuits in the adult brain

“…3O). Dimensionality reduction via principal components analysis and k-means clustering were performed (Methods) to classify microglia into four morphotypesamoeboid, rod-like, ramified, and hyperramified (49)(50)(51) (Extended Data Fig. 8A and C).…”

“…3O). Dimensionality reduction via principal components analysis and k-means clustering were performed (Methods) to classify microglia into four morphotypes – amoeboid, rod-like, ramified, and hyper-ramified ( 49–51 ) (Extended Data Fig.8A and C). Microglial morphology is directly related to their function, with amoeboid and rod-like morphologies thought to reflect more phagocytotic states, ramified morphologies reflecting a homeostatic role, and hyper-ramified reflecting a response to stress or loss of sensory input ( 52, 53 ).…”