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 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.

Repeated LPS induces training and tolerance of microglial responses across brain regions

“…Using MicrogliaMorphology and MicrogliaMorphologyR, as previously described (41), we quantified shifts in morphological populations within the same brain regions assessed for RNA-seq (frontal cortex, hippocampus, striatum) from male and female mice given 1-4xLPS. (Fig.…”

“…Maximum projection EDF images only compile the pixels of highest intensity at any given position in a z-stack to construct a new 2D image which retains the 3D information. Using ImageJ, we created .tiff images of each fluorescent channel from .czi files and selected and saved .tiffs of brain regions of interest (ROIs) to use as input for downstream morphological analysis in MicrogliaMorphology and MicrogliaMorphologyR, as detailed in Kim et al, 2023 (41). The parameters used for MicrogliaMorphology included: auto local thresholding using the Mean method and radius 100 with an area filter of 0.01613 - 0.1808 in^2 (or 153.3358 – 1718.73 um^2 with .czi conversion applied factor of .325um/pixel applied to scale).…”

“…We assessed how percentages of morphological clusters for each mouse changed with LPS treatment by fitting a generalized linear mixed model to measure percentage changes as a factor of cluster and treatment interactions for each brain region and sex with mouseID as a repeated measure (percentage ∼ ClusterID*Treatment + (1|MouseID)) using MicrogliaMorphologyR, which calls to the glmmTMB R package. (41,44) We had to analyze each sex and brain region separately because the males had an additional treatment group (3xLPS) that was not present in the females and the model converged when considering ClusterID*Treatment*BrainRegion interactions together. The model was assessed using ANOVA and tests between groups were corrected across for comparisons made within each cluster (∼Treatment|Cluster) using the BH method.…”

Early-Life Gut Inflammation Drives Sex-Dependent Shifts in the Microbiome-Endocrine-Brain Axis

“…We therefore examined changes in microglial activation state in several brain regions following behavior at P110. Using our newly developed microglial morphology analysis tool (Kim, Jennifer et al, 2023), we measured 27 morphological features from 8,349 microglia in the dorsal hippocampus (average of 417 per mouse) (Figure 7), 5,971 microglia in the hypothalamus (average of 426 per mouse) and 8,482 microglia in the cortex (average of 446 per mouse) (Supplemental Figure 3). We identified four clusters of different morphologies (Figure 7A, Supplemental Figure 3D) including microglia showing ameboid, ramified, rod-like and hypertrophic characteristics.…”