Microglial morphometric analysis: so many options, so little consistency

Abstract

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.

Chronic activation of a negative engram induces behavioral and cellular abnormalities

“…pathogens, debris, injury), microglia retract and exhibit large soma size and thickening, entering an ‘ameboid’ state. Evidence has accumulated that microglial morphology may be more complex than this dichotomy between ‘surveying’ and ‘ameboid’ microglia (Vidal-Itriago et al, 2022; Leyh et al, 2021; Reddaway et al, 2023). Our chronic negative engram activation may induce an ‘insult’ on the brain, as evidenced by an increase in Iba1 expression and hyper-ramified morphology, indicating a complex change in microglial phenotype.…”

“…pathogens, debris, injury), microglia retract and exhibit large soma size and thickening, entering an 'ameboid' state. Evidence has accumulated that microglial morphology may be more complex than this dichotomy between 'surveying' and 'ameboid' microglia (Vidal-Itriago et al, 2022;Leyh et al, 2021;Reddaway et al, 2023).…”

Quantifying microglial morphology: an insight into function

“…Results using morphOMICS indicated that the morphology of microglia was brain region and sex specific, as discussed above [ 110 ]. For a comprehensive review of microglial morphology methods, please refer to [ 113 ].…”

Development of a high-throughput pipeline to characterize microglia morphological states at a single-cell resolution

“…(Dubbelaar et al, 2018;Paolicelli et al, 2022) In line with these efforts, there have been many recently published tools that classify and analyze microglia morphological subpopulations in an automated and high-throughput manner. (Clarke et al, 2021;Colombo et al, 2022;Hrj et al, 2023;Leyh et al, 2021;Reddaway et al, 2023;Salamanca et al, 2019;York et al, 2018) Using our toolset, MicrogliaMorphology and MicrogliaMorphologyR, we take a datainformed approach to characterize different populations of microglia morphologies and to available to download and all relevant materials including input images, data, and supporting code used in this study will be available on the OSF website when the paper is published.…”