Diversity of transcriptomic microglial phenotypes in aging and Alzheimer's disease

Abstract

The morphological plasticity of microglia has fascinated neuroscientists for 100 years. Attempts to classify functional phenotypes are hampered by similarities between endogenous brain microglia and peripheral myeloid cells that can enter the brain under pathological conditions. Recent advances in single‐cell ‐omic methodologies have led to an explosion of data regarding gene expression in microglia. Herein, we review the diversity of microglial phenotypes in healthy brains, aging, and Alzheimer's disease (AD); identify knowledge gaps in the body of evidence; and suggest areas in which new knowledge would be useful. Data from human samples and mouse models are compared and contrasted. Understanding the molecular complexity of the microglial response repertoire will suggest new avenues for therapeutic treatments in AD.

Microglia Heterogeneity in Alzheimer’s Disease: Insights From Single-Cell Technologies

“…As studies have shown concordance between single-cell and single-nucleus transcriptome profiles, snRNA-seq is becoming a tool for studying cellular transcriptional heterogeneity in brain tissues particularly for human brain, for which often only frozen material is available. Microglial signatures in human AD brain samples obtained through snRNA-seq show considerable heterogeneity and can differ from the DAM expression signature detected in AD mouse models ( Mathys et al, 2019 ; Alsema et al, 2020 ; Boche and Gordon, 2021 ; Chen and Colonna, 2021 ; Gerrits et al, 2021 ; Figure 1 ). An initial snRNA-seq analysis of the brain tissues from three patients with Mendelian or sporadic AD showed that it is possible to identify different cell types from frozen brains of patients with different forms of AD and discovered five differentially expressed genes ( EEF1A1 , GLULL , KIAA1217 , LDLRAD3 , and SPP1 ) that are consistently associated with microglia in all three samples ( Del-Aguila et al, 2019 ; Table 2 ).…”

“…Microglia states can now be defined by the expression profiling of specific gene sets that are differentially expressed and used to describe cell subpopulations. Single-cell transcriptomic technologies enable unbiased characterization of microglia subtypes and states during transition from normal to disease and response to therapies ( Gerrits et al, 2020 ; Masuda et al, 2020 ; Boche and Gordon, 2021 ; Chen and Colonna, 2021 ). The comprehensive genome-wide analysis by scRNA-seq and other single-cell technologies helps to systematically resolve microglia heterogeneity in AD.…”

“…The comprehensive genome-wide analysis by scRNA-seq and other single-cell technologies helps to systematically resolve microglia heterogeneity in AD. Single-cell analysis can also further identify signaling pathways, regulatory factors and potential markers related to microglia, thus providing more insights into microglial response in AD ( Olah et al, 2020 ; Boche and Gordon, 2021 ; Chen and Colonna, 2021 ; Provenzano et al, 2021 ; Summarized in Tables 1 , 2 ).…”

“…The discrepancies between human microglial signatures and their mouse counterparts could be explained by the fact that human brain samples usually represent a terminal stage of AD with amyloid and tau pathology, as well as extensive neuronal loss, while mouse models might just recapitulate either earlier stages of the disease characterized by Aβ accumulation or frontotemporal dementia–like tauopathy without amyloidosis ( Alsema et al, 2020 ; Masuda et al, 2020 ; Boche and Gordon, 2021 ; Chen and Colonna, 2021 ; Provenzano et al, 2021 ). There are many other biological reasons, such as the different brain regions from where the samples are taken, and the genetic background or ethnic origins of AD patients cohorts analyzed, that could contribute to variations in human AD profiles from different studies.…”

“…Additionally, the overall quality of human RNA samples could be poorer than mouse samples due to postmortem intervals preceding human sample collection and processing. Further improvement in tissue collection and processing would potentially reduce variations in human samples and human–mouse discrepancies ( Alsema et al, 2020 ; Lewcock et al, 2020 ; Masuda et al, 2020 ; Boche and Gordon, 2021 ; Chen and Colonna, 2021 ; Gerrits et al, 2021 ; Provenzano et al, 2021 ). Notably, a comparison of nuclear (snRNA-seq) and total cellular transcriptomes (scRNA-seq) of human microglia in a study revealed that a small population of genes is depleted in nuclei, while most genes show similar relative abundances in cells and nuclei ( Thrupp et al, 2020 ).…”