Unveiling microglial heterogeneity in spatial context

Microglia exhibit remarkable plasticity, transitioning between diverse functional states in response to their surrounding environment via molecular mechanisms that remain only partially described. Transcriptomics remains the best available means of describing microglial state transitions, with spatially resolved methods offering the necessary environmental context. Multiplexed error-robust fluorescence in situ hybridization (MERFISH) offers the spatial resolution needed to distinguish individual microglia and neighboring cells but at the cost of a limited set of transcripts.
To create an optimal compressed spatial transcriptomic readout for microglia, we built on a comprehensive single-cell RNA sequencing (scRNA-seq), capturing a diverse landscape of mouse microglial subpopulations across development, aging, and in response to Lysolethicin (LC) injection-induced brain injury. We then utilized high-dimensional weighted gene co-expression network analysis (hdWGCNA) to identify “eigengenes” or functional modules of transcripts with interconnected expression patterns, reflecting coregulated biological processes. We further analyzed each eigengene by gene ontology and pathway enrichment analysis to find genes representing distinct processes that were likely coregulated. From this analysis, we constructed a compressed list of highly informative genes for microglia MERFISH within the scope of scRNA-seq data. Mice were prepared under matching conditions to scRNA-seq, and their brains were prepared under RNAse-free conditions for MERFISH. Concurrently, mouse microglia IMG cells were used to optimize MERFISH probe concentrations, data collection strategies, and image post processing. Data collection is in progress.
Our hdWGCNA analysis reveals that functional modules are activated to varying degrees within individual microglial subpopulations, suggesting a suite of potentially concurrent processes rather than mutually exclusive states. This framework offers a novel approach to studying cellular heterogeneity in diverse disease contexts, moving beyond rigid subpopulation classifications to capture the dynamic continuum of microglial activity and its spatial distribution.