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 used dimensionality reduction and clustering approaches to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states. Using ImageJ tools, we first developed a semi-automated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain subregion-specific manner. Within this pool of morphology measures, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and cell circularity. When considered together, these sets of features drove different morphological clusters. Furthermore, our analysis toolset 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.