With a rapidly aging global human population, finding a cure for late onset neurodegenerative diseases has become an urgent enterprise. However, these efforts are hindered by the lack of understanding of what constitutes the phenotype of aged human microglia—the cell type that has been strongly implicated by genetic studies in the pathogenesis of age-related neurodegenerative disease. Here, we establish the set of genes that is preferentially expressed by microglia in the aged human brain. This HuMi_Aged gene set captures a unique phenotype, which we confirm at the protein level. Furthermore, we find this gene set to be enriched in susceptibility genes for Alzheimer’s disease and multiple sclerosis, to be increased with advancing age, and to be reduced by the protective APOEε2 haplotype. APOEε4 has no effect. These findings confirm the existence of an aging-related microglial phenotype in the aged human brain and its involvement in the pathological processes associated with brain aging.

Microglia are emerging as a key cell type in neurodegenerative diseases, yet human microglia are challenging to study in vitro. We developed an in vitro cell model system composed of human monocyte-derived microglia-like (MDMi) cells that recapitulated key aspects of microglia phenotype and function. We then used this model system to perform an expression quantitative trait locus (eQTL) study examining 94 genes from loci associated with Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. We found six loci (CD33, PILRB, NUP160, LRRK2, RGS1, and METTL21B) in which the risk haplotype drives the association with both disease susceptibility and altered expression of a nearby gene (cis-eQTL). In the PILRB and LRRK2 loci, the cis-eQTL was found in the MDMi cells but not in human peripheral blood monocytes, suggesting that differentiation of monocytes into microglia-like cells led to the acquisition of a cellular state that could reveal the functional consequences of certain genetic variants. We further validated the effect of risk haplotypes at the protein level for PILRB and CD33, and we confirmed that the CD33 risk haplotype altered phagocytosis by the MDMi cells. We propose that increased LRRK2 gene expression by MDMi cells could be a functional outcome of rs76904798, a single-nucleotide polymorphism in the LRKK2 locus that is associated with Parkinson’s disease.

Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulation can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer’s disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs). Gene expression data generated from a tissue sample reflects an average gene expression profile across heterogeneous populations of cells. Because composition of constituent cell-types can vary across individuals (due to technical or biological factors), differential gene expression analysis requires estimating and adjusting for such cellular heterogeneity. While many deconvolution algorithms for estimating cellular composition from tissue gene expression data have been tested extensively in blood, their performance when applied to brain tissue is unclear. To address this gap, we generated an immunohistochemistry (IHC) dataset for five major cell-types from brain, in order to apply and then assess deconvolution algorithms for application to brain gene expression datasets. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. Further, we show that adjusting for estimated cell-type proportions across individuals when conducting differential gene expression analysis is important in reducing false associations.

Poor sleep in older adults is associated with aging of the brain’s resident innate immune cells and impaired cognition.

Background: Identified as an Alzheimer’s disease (AD) susceptibility gene by genome wide-association studies, BIN1 has 10 isoforms that are expressed in the Central Nervous System (CNS). The distribution of these isoforms in different cell types, as well as their role in AD pathology still remains unclear. Methods: Utilizing antibodies targeting specific BIN1 epitopes in human post-mortem tissue and analyzing mRNA expression data from purified microglia, we identified three isoforms expressed in neurons and astrocytes (isoforms 1, 2 and 3) and four isoforms expressed in microglia (isoforms 6, 9, 10 and 12). The abundance of selected peptides, which correspond to groups of BIN1 protein isoforms, was measured in dorsolateral prefrontal cortex, and their relation to neuropathological features of AD was assessed. Results: Peptides contained in exon 7 of BIN1’s N-BAR domain were found to be significantly associated with AD-related traits and, particularly, tau tangles. Decreased expression of BIN1 isoforms containing exon 7 is associated with greater accumulation of tangles and subsequent cognitive decline, with astrocytic rather than neuronal BIN1 being the more likely culprit. These effects are independent of the BIN1 AD risk variant. Conclusions: Exploring the molecular mechanisms of specific BIN1 isoforms expressed by astrocytes may open new avenues for modulating the accumulation of Tau pathology in AD.

The neurodegenerative diseases Alzheimer’s disease (AD) and Parkinson’s disease (PD) both have a myriad of risk factors including genetics, environmental exposures, and lifestyle. However, aging is the strongest risk factor for both diseases. Aging also profoundly influences the immune system, with immunosenescence perhaps the most prominent outcome. Through genetics, mouse models, and pathology, there is a growing appreciation of the role the immune system plays in neurodegenerative diseases. In this review, we explore the intersection of aging and the immune system in AD and PD.