Microglial morphometric analysis: so many options, so little consistency

Reddaway, Jack, Richardson, Peter Eulalio, Bevan, Ryan J. ORCID: https://orcid.org/0000-0002-2557-2887, Stoneman, Jessica and Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967 2023. Microglial morphometric analysis: so many options, so little consistency. Frontiers in Neuroinformatics 17 , 1211188. 10.3389/fninf.2023.1211188

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	Frontiers Media
ISSN:	1662-5196
Funders:	This work was supported by the Hodge Foundation Ph.D. Studentship, the Wellcome Trust Ph.D. Fellowship, the UK Dementia Research Institute Programme Grant, and the UKRI Future Leaders Fellowship MR/T020296/2
Date of First Compliant Deposit:	14 August 2023
Date of Acceptance:	5 July 2023
Last Modified:	05 Jun 2024 01:08
URI:	https://orca.cardiff.ac.uk/id/eprint/161706

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