{"title":"开发高通量管道,以单细胞分辨率描述小胶质细胞形态状态。","authors":"Jennifer Kim, Paul Pavlidis, Annie Vogel Ciernia","doi":"10.1523/ENEURO.0014-24.2024","DOIUrl":null,"url":null,"abstract":"<p><p>As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We applied dimensionality reduction and clustering to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states in a mouse dataset that we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semiautomated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain region-specific manner. Within this pool of features, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and circularity. When considered together, these sets of features drove different morphological clusters. Our tools captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy-to-use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.</p>","PeriodicalId":11617,"journal":{"name":"eNeuro","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289588/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of a High-Throughput Pipeline to Characterize Microglia Morphological States at a Single-Cell Resolution.\",\"authors\":\"Jennifer Kim, Paul Pavlidis, Annie Vogel Ciernia\",\"doi\":\"10.1523/ENEURO.0014-24.2024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We applied dimensionality reduction and clustering to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states in a mouse dataset that we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semiautomated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain region-specific manner. Within this pool of features, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and circularity. When considered together, these sets of features drove different morphological clusters. Our tools captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy-to-use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.</p>\",\"PeriodicalId\":11617,\"journal\":{\"name\":\"eNeuro\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289588/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"eNeuro\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1523/ENEURO.0014-24.2024\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/1 0:00:00\",\"PubModel\":\"Print\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"eNeuro","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1523/ENEURO.0014-24.2024","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/1 0:00:00","PubModel":"Print","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Development of a High-Throughput Pipeline to Characterize Microglia Morphological States at a Single-Cell Resolution.
As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We applied dimensionality reduction and clustering to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states in a mouse dataset that we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semiautomated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain region-specific manner. Within this pool of features, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and circularity. When considered together, these sets of features drove different morphological clusters. Our tools captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy-to-use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.
期刊介绍:
An open-access journal from the Society for Neuroscience, eNeuro publishes high-quality, broad-based, peer-reviewed research focused solely on the field of neuroscience. eNeuro embodies an emerging scientific vision that offers a new experience for authors and readers, all in support of the Society’s mission to advance understanding of the brain and nervous system.