MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Cellular Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncel.2024.1505048/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846143075840163840 |
|---|---|
| author | Juan Pablo Maya-Arteaga Humberto Martínez-Orozco Sofía Diaz-Cintra |
| author_facet | Juan Pablo Maya-Arteaga Humberto Martínez-Orozco Sofía Diaz-Cintra |
| author_sort | Juan Pablo Maya-Arteaga |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-79c05cd81ae842289b7dfe7ae11635e1 |
| institution | Kabale University |
| issn | 1662-5102 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular Neuroscience |
| spelling | doaj-art-79c05cd81ae842289b7dfe7ae11635e12024-12-03T04:22:17ZengFrontiers Media S.A.Frontiers in Cellular Neuroscience1662-51022024-12-011810.3389/fncel.2024.15050481505048MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse modelJuan Pablo Maya-ArteagaHumberto Martínez-OrozcoSofía Diaz-CintraMicroglia 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.https://www.frontiersin.org/articles/10.3389/fncel.2024.1505048/fullmachine learningpipelinehippocampusclusteringimage processingUMAP |
| spellingShingle | Juan Pablo Maya-Arteaga Humberto Martínez-Orozco Sofía Diaz-Cintra MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model Frontiers in Cellular Neuroscience machine learning pipeline hippocampus clustering image processing UMAP |
| title | MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model |
| title_full | MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model |
| title_fullStr | MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model |
| title_full_unstemmed | MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model |
| title_short | MorphoGlia, an interactive method to identify and map microglia morphologies, demonstrates differences in hippocampal subregions of an Alzheimer’s disease mouse model |
| title_sort | morphoglia an interactive method to identify and map microglia morphologies demonstrates differences in hippocampal subregions of an alzheimer s disease mouse model |
| topic | machine learning pipeline hippocampus clustering image processing UMAP |
| url | https://www.frontiersin.org/articles/10.3389/fncel.2024.1505048/full |
| work_keys_str_mv | AT juanpablomayaarteaga morphogliaaninteractivemethodtoidentifyandmapmicrogliamorphologiesdemonstratesdifferencesinhippocampalsubregionsofanalzheimersdiseasemousemodel AT humbertomartinezorozco morphogliaaninteractivemethodtoidentifyandmapmicrogliamorphologiesdemonstratesdifferencesinhippocampalsubregionsofanalzheimersdiseasemousemodel AT sofiadiazcintra morphogliaaninteractivemethodtoidentifyandmapmicrogliamorphologiesdemonstratesdifferencesinhippocampalsubregionsofanalzheimersdiseasemousemodel |