Development of a High-Throughput Pipeline to Characterize Microglia Morphological States at a Single-Cell Resolution

Abstract

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 we used to demonstrate the utility of our toolset. Using ImageJ, we first developed a semi-automated 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.Significance StatementWe developed an accessible, user-friendly, and open-source computational toolset for microglia morphology segmentation and analysis. While there has been considerable progress in the field to develop automated microglia morphology segmentation tools, the majority of published tools are not openly available nor well-documented and there has been less transparency about the methods used to analyze the resulting morphological measures. Using our toolset, we took a data-informed approach to characterize different classes of microglia morphologies and to statistically model how membership across these forms dynamically changes across brain regions in an experimental mouse model. Application of our toolset will yield novel insights into microglia morphology differences at a single-cell resolution and in a spatially-resolved manner across many different research questions.