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 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.

Keywords: ImageJ; R; microglia; morphology; open science; tool development.

Visual Abstract

Figure 1.

Study overview. A, Outline of steps involved in MicrogliaMorphology and MicrogliaMorphologyR. B, Experimental mouse model used for dataset described throughout paper, made using Biorender. Example images from the dorsal hippocampus with individual microglia insets for each treatment condition. Microglia (Iba1) in yellow and DAPI nuclear stain in blue. Full size images scale bar 200 µm, insets 30 µm.

Figure 2.

Comparison of 3D versus EDF versus single-plane 2D image types. A, Samples represented in principal components space and colored by image type or morphology class. Each point is either a 2D, 3D, or EDF representation of 1 of 20 different cells. B, Comparison of changes across morphological classes when cells are represented in 2D, 3D, or EDF forms. Values on plots are z-scores (centered and scaled) calculated within image type. C, Spearman's correlation of PCs 1–2 after dimensionality reduction across image types. D, Individual Pearson’s correlations between image types for specific morphology features measured using AnalyzeSkeleton. E, Visual description of morphology features measured using AnalyzeSkeleton. See Extended Data Figure 2-1 and Extended Data Tables 2-1 and 2-2 for additional data.

Figure 3.

Characterization of morphological clusters in 2xLPS dataset. A, Spearman's correlation matrix of 27 features measured by MicrogliaMorphology [*abs(R) ≥ 0.8; p < 0.05]. B, Spearman's correlation of morphology measures to first three PCs after dimensionality reduction [*abs(R) ≥ 0.75; p < 0.05]. C, Cluster classes displayed in PCs 1–2 space. D, Average values for all 27 morphology features, scaled across clusters. E, Individual cells spatially registered back to original images and visually annotated by morphological class using ColorByCluster feature. See Extended Data Figure 3-1 and Extended Data Tables 3-1–3-4 for additional data.

Figure 4.

Analysis of morphological clusters and individual morphology measures across brain regions and antibody markers in 2xLPS dataset. A, LPS-induced shifts in morphological populations across antibodies and within brain regions (*p < 0.05; Bonferroni). B, Immunofluorescent images of the same microglial cells stained with Cx3cr1, Iba1, and P2ry12 in PBS and 2xLPS conditions. Scale bars, 50 µm. C, LPS-induced changes in cell area across antibodies and within brain regions (*p < 0.05; Bonferroni) See Extended Data Figure 4-1 and Extended Data Tables 4-1–4-4 for additional data.