182 Chapter 8 CX3CR1CreER mice have greatly contributed to our understanding of microglia by identifying critical pathways that underlie microglial activation during neuroinflammation38,39. To discriminate between microglia and macrophages, Tmem119EGFP, Tmem119CreERT2, P2ry12CreER, HexbtdT and HexbCreERT2 models have recently been developed31,40,41. Among these models, Hexb transgenic lines have the advantage of stable Hexb expression following CNS damage, allowing microglia tracking or manipulation in these contexts31,42. Cytometry by time-of-flight (CyTOF) mass spectrometry, has allowed for the identification of macrophage subsets in the CNS which shift in the context of pathology23,43. While these elegantly developed techniques provide great insight into microglial heterogeneity, they have yet to be specifically applied to white matter microglia. A more recent advanced technique, single cell RNA-sequencing (scRNA-seq), allows the gathering of transcriptional profiles of thousands of cells from one sample to identify gene expression at a single cell level. This makes scRNA-seq an outstanding tool to investigate the heterogeneity of microglia across different brain regions and/or different cellular development stages. scRNA-seq has already progressed to allow more detailed studies to generate full-length cDNA (e.g. SMART-seq2) or cDNA with an unique molecular identifier (UMI) at the 5’ or 3’ end (e.g. MARS-seq, 10X Genomics). SMART-seq2 covers the entire transcriptome and has a very high accuracy and sensitivity44. MARS-seq, while less accurate than SMART-seq2, is less expensive and allows multiplexing of samples via molecular tagging45. Both full-length and tag-based methods are used for various fundamental applications such as cell-type discovery, gene expression analysis, tissue composition assessment and to investigate microglial heterogeneity in the CNS. Using SMART-seq2, a microglial gene signature for cell cycle phases of dividing microglia was found46. Additionally, MARS-seq was used to identify different phases of regulatory networks in microglia47. While scRNA-seq studies have identified heterogeneity of microglia, combining this advanced technique with spatial transcriptomics48 will help elucidate microglia heterogeneity in the context of pathology or cellular interactions, as has recently been reported in a mouse model of AD49, and in astrocytes in the context of neuroinflammation50. These advanced tools allow us to identify more diverse microglia states than possible with classical methods, revealing the full complement of microglial heterogeneity (Fig. 3). Regional and morphological differences of microglia Microglia numbers and morphology vary between brain regions in humans and mice although species differences are observed. For example, human microglia density is higher in white matter compared to grey matter (Table 1), converse to that observed in rodents5,51. Microglial density might reflect regional requirements for microglial support or surveillance52,53. Indeed, regional differences in expression of cell surface proteins related to environmental scanning have been documented in mice54. Furthermore, genes related to immune signalling, dampening of microglial activation, metabolism and the sensome are differentially expressed between the cortex, cerebellum, hippocampus and striatum of mice54. Despite this regional heterogeneity microglia retain a specific signature that distinguishes them from peripheral macrophages29,54-56. A recent study utilising bulk RNA-seq found distinct microglial signatures in the white matter compared to the grey matter in both MS brain and healthy controls, irrespective of disease57. For example, the NF-κB pathway was found to be engaged at a higher level in white matter microglia compared to their grey matter counterparts, possibly providing an explanation as to why microglial inflammation is increased in the former58.
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