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ORIGINAL RESEARCH article

Front. Cell. Neurosci.
Sec. Non-Neuronal Cells
Volume 18 - 2024 | doi: 10.3389/fncel.2024.1505048
This article is part of the Research Topic Spatial Microglial Identities and Functions in the Mammalian CNS: Unveiling Cellular Diversity and Functional Roles View all articles

MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer's disease mouse model

Provisionally accepted
Juan Pablo Maya-Arteaga Juan Pablo Maya-Arteaga Humberto Martínez-Orozco Humberto Martínez-Orozco Sofía Díaz-Cintra Sofía Díaz-Cintra *
  • Instituto de Neurobiología - UNAM Juriquilla, Querétaro, Mexico

The final, formatted version of the article will be published soon.

    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.

    Keywords: machine learning, pipeline, Hippocampus, clustering, image processing, Umap

    Received: 02 Oct 2024; Accepted: 19 Nov 2024.

    Copyright: © 2024 Maya-Arteaga, Martínez-Orozco and Díaz-Cintra. 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) or licensor 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.

    * Correspondence: Sofía Díaz-Cintra, Instituto de Neurobiología - UNAM Juriquilla, Querétaro, Mexico

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.