Microglia are the brain’s immunocompetent macrophages with a unique feature that
allows surveillance of the surrounding microenvironment and subsequent reactions to
tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking
morphological plasticity is one of their prominent characteristics and the categorization of
microglial cell function based on morphology is well established. Frequently, automated
classification of microglial morphological phenotypes is performed by using quantitative
parameters. As this process is typically limited to a few and especially manually chosen
criteria, a relevant selection bias may compromise the resulting classifications. In our
study, we describe a novel microglial classification method by morphological evaluation
using a convolutional neuronal network on the basis of manually selected cells in addition
to classical morphological parameters. We focused on four microglial morphologies,
ramified, rod-like, activated and amoeboid microglia within the murine hippocampus
and cortex. The developed method for the classification was confirmed in a mouse
model of ischemic stroke which is already known to result in microglial activation
within affected brain regions. In conclusion, our classification of microglial morphological
phenotypes using machine learning can serve as a time-saving and objective method
for post-mortem characterization of microglial changes in healthy and disease mouse
models, and might also represent a useful tool for human brain autopsy samples.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84332 |
| Date | 27 March 2023 |
| Creators | Leyh, Judith, Paeschke, Sabine, Mages, Bianca, Michalski, Dominik, Nowicki, Marcin, Bechmann, Ingo, Winter, Karsten |
| Publisher | Frontiers Research Foundation |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1662-5102, 701673 |
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