MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer's disease mouse model.
Juan Pablo Maya-Arteaga, Humberto Martínez-Orozco, Sofía Diaz-Cintra
{"title":"MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer's disease mouse model.","authors":"Juan Pablo Maya-Arteaga, Humberto Martínez-Orozco, Sofía Diaz-Cintra","doi":"10.3389/fncel.2024.1505048","DOIUrl":null,"url":null,"abstract":"<p><p>Microglia are dynamic central nervous system cells crucial for maintaining homeostasis and responding to neuroinflammation, as evidenced by their varied morphologies. Existing morphology analysis often fails to detect subtle variations within the full spectrum of microglial morphologies due to their reliance on predefined categories. Here, we present MorphoGlia, an interactive, user-friendly pipeline that objectively characterizes microglial morphologies. MorphoGlia employs a machine learning ensemble to select relevant morphological features of microglia cells, perform dimensionality reduction, cluster these features, and subsequently map the clustered cells back onto the tissue, providing a spatial context for the identified microglial morphologies. We applied this pipeline to compare the responses between saline solution (SS) and scopolamine (SCOP) groups in a SCOP-induced mouse model of Alzheimer's disease, with a specific focus on the hippocampal subregions CA1 and Hilus. Next, we assessed microglial morphologies across four groups: SS-CA1, SCOP-CA1, SS-Hilus, and SCOP-Hilus. The results demonstrated that MorphoGlia effectively differentiated between SS and SCOP-treated groups, identifying distinct clusters of microglial morphologies commonly associated with pro-inflammatory states in the SCOP groups. Additionally, MorphoGlia enabled spatial mapping of these clusters, identifying the most affected hippocampal layers. This study highlights MorphoGlia's capability to provide unbiased analysis and clustering of microglial morphological states, making it a valuable tool for exploring microglial heterogeneity and its implications for central nervous system pathologies.</p>","PeriodicalId":12432,"journal":{"name":"Frontiers in Cellular Neuroscience","volume":"18 ","pages":"1505048"},"PeriodicalIF":4.2000,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653188/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Cellular Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fncel.2024.1505048","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Microglia are dynamic central nervous system cells crucial for maintaining homeostasis and responding to neuroinflammation, as evidenced by their varied morphologies. Existing morphology analysis often fails to detect subtle variations within the full spectrum of microglial morphologies due to their reliance on predefined categories. Here, we present MorphoGlia, an interactive, user-friendly pipeline that objectively characterizes microglial morphologies. MorphoGlia employs a machine learning ensemble to select relevant morphological features of microglia cells, perform dimensionality reduction, cluster these features, and subsequently map the clustered cells back onto the tissue, providing a spatial context for the identified microglial morphologies. We applied this pipeline to compare the responses between saline solution (SS) and scopolamine (SCOP) groups in a SCOP-induced mouse model of Alzheimer's disease, with a specific focus on the hippocampal subregions CA1 and Hilus. Next, we assessed microglial morphologies across four groups: SS-CA1, SCOP-CA1, SS-Hilus, and SCOP-Hilus. The results demonstrated that MorphoGlia effectively differentiated between SS and SCOP-treated groups, identifying distinct clusters of microglial morphologies commonly associated with pro-inflammatory states in the SCOP groups. Additionally, MorphoGlia enabled spatial mapping of these clusters, identifying the most affected hippocampal layers. This study highlights MorphoGlia's capability to provide unbiased analysis and clustering of microglial morphological states, making it a valuable tool for exploring microglial heterogeneity and its implications for central nervous system pathologies.
期刊介绍:
Frontiers in Cellular Neuroscience is a leading journal in its field, publishing rigorously peer-reviewed research that advances our understanding of the cellular mechanisms underlying cell function in the nervous system across all species. Specialty Chief Editors Egidio D‘Angelo at the University of Pavia and Christian Hansel at the University of Chicago are supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.