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Human microglia play a pivotal role in neurological diseases, but few targeted therapies that directly modulate microglial state or function exist due to an incomplete understanding of microglial heterogeneity. This thesis aims to advance our understanding of microglial heterogeneity by using single-cell RNA sequencing to profile live human microglia from autopsies or surgical resections across diverse neurological diseases and using computational tools to infer chemical and genetic regulators of specific microglial substates. Chapter 1 provides an overview of microglial ontogeny, function, and known heterogeneity, especially in disease contexts. It also describes the steadily increasing disease burden seen in neurological disease as well as the lack of efficacious treatments and future directions for microglia-targeted therapies. Chapter 2 focuses on microglial heterogeneity in an understudied disease, ALS, describing population structure shifts seen in ALS across cortex and spinal cord. Chapter 3 instead focuses on exploring underlying cross-disease microglial population structure, identifying subsets with metabolic and functional properties, as well as subsets enriched in susceptibility genes for neurodegenerative disease. We then demonstrate applications of this type of data by using our resource to annotate other datasets. Chapter 4 leverages this data in another way, by identifying and validating candidates for chemically and genetically inducing subtype-specific states in vitro. Notably, we show that Camptothecin downregulates the transcriptional signature of disease-enriched subsets and upregulates a signature previously shown to be depleted in Alzheimer’s. Finally, I review our findings and discuss future directions for the field.

Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples.

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