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Classification of Microglial Morphological Phenotypes Using Machine Learning
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 prominen...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276040/ https://www.ncbi.nlm.nih.gov/pubmed/34267628 http://dx.doi.org/10.3389/fncel.2021.701673 |
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author | Leyh, Judith Paeschke, Sabine Mages, Bianca Michalski, Dominik Nowicki, Marcin Bechmann, Ingo Winter, Karsten |
author_facet | Leyh, Judith Paeschke, Sabine Mages, Bianca Michalski, Dominik Nowicki, Marcin Bechmann, Ingo Winter, Karsten |
author_sort | Leyh, Judith |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8276040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82760402021-07-14 Classification of Microglial Morphological Phenotypes Using Machine Learning Leyh, Judith Paeschke, Sabine Mages, Bianca Michalski, Dominik Nowicki, Marcin Bechmann, Ingo Winter, Karsten Front Cell Neurosci Cellular Neuroscience 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. Frontiers Media S.A. 2021-06-29 /pmc/articles/PMC8276040/ /pubmed/34267628 http://dx.doi.org/10.3389/fncel.2021.701673 Text en Copyright © 2021 Leyh, Paeschke, Mages, Michalski, Nowicki, Bechmann and Winter. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cellular Neuroscience Leyh, Judith Paeschke, Sabine Mages, Bianca Michalski, Dominik Nowicki, Marcin Bechmann, Ingo Winter, Karsten Classification of Microglial Morphological Phenotypes Using Machine Learning |
title | Classification of Microglial Morphological Phenotypes Using Machine Learning |
title_full | Classification of Microglial Morphological Phenotypes Using Machine Learning |
title_fullStr | Classification of Microglial Morphological Phenotypes Using Machine Learning |
title_full_unstemmed | Classification of Microglial Morphological Phenotypes Using Machine Learning |
title_short | Classification of Microglial Morphological Phenotypes Using Machine Learning |
title_sort | classification of microglial morphological phenotypes using machine learning |
topic | Cellular Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276040/ https://www.ncbi.nlm.nih.gov/pubmed/34267628 http://dx.doi.org/10.3389/fncel.2021.701673 |
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