Cell type-specific changes identified by single-cell transcriptomics in Alzheimer's disease

Abstract

The rapid advancement of single-cell transcriptomics in neurology has allowed for profiling of post-mortem human brain tissue across multiple diseases. Over the past 3 years, several studies have examined tissue from donors with and without diagnoses of Alzheimer's disease, highlighting key changes in cell type composition and molecular signatures associated with pathology and, in some cases, cognitive decline. Although all of these studies have generated single-cell/nucleus RNA-seq or ATAC-seq data from the full array of major cell classes in the brain, they have each focused on changes in specific cell types. Here, we synthesize the main findings from these studies and contextualize them in the overall space of large-scale omics studies of Alzheimer's disease. Finally, we touch upon new horizons in the field, in particular advancements in high-resolution spatial interrogation of tissue and multi-modal efforts-and how they are likely to further advance mechanistic and target-selection studies on Alzheimer's disease.

Fig. 1

A Current bulk profiling approaches that have generated large-scale data from post-mortem human brain tissue in the context of AD. Single-cell approaches have recently been scaled up to allow for a similar type of profiling, but have not reached the donor numbers interrogated using bulk techniques. B General workflow for single-cell approaches applied to human brain tissue. After isolation, individual cells or nuclei are encapsulated into wells or droplets, where lysis, reverse transcription, and amplification occurs, usually with some degree of pooling. Barcoded cellular/nucleus cDNA is then sequenced, and reads can be demultiplexed by cell/nucleus barcode and aligned to the transcriptome, ultimately generating expression profiles for each cell/nucleus. Panel A was created using the BioRender package

Fig. 2

General analytical workflow for large-scale single-cell/nucleus RNA-seq data from human control and AD samples. A Starting from the genes × cells/nuclei counts table, most analysis workflows identify high variance genes, then perform dimensionality reduction, and ultimately call clusters in reduced-dimension space. This clustering may be iterative, where larger clusters are then re-analyzed, starting from the first step, to identify subgroups (enlarged inset on the right). It is important to note that often a separate reduced-dimension embedding is used for visualization, as opposed to the embedding used for clustering. B–D After clusters have been identified, the analysis workflow looks to identify differences in gene expression across conditions in each major cell class (B) or subcluster (C) or in the relative proportions of each cell class or cluster across conditions (D). These differences form the basis of understanding cell type-specific changes associated with the disease

Fig. 3

Schematic summary of selected findings from multiple sc/snRNA-seq studies, organized by cell type and publication date (italics indicate the Biorxiv versions of manuscripts). Studies have identified differences in all major cell types in brain tissue from healthy donors versus donors with AD diagnoses. Some of these differences are highlighted only in a subset of studies, suggesting the need for further exploration of reproducibility and consistency of findings. This figure was created using the BioRender package

Fig. 4

Open questions regarding cell type dysregulation and interactions that have not been extensively addressed by the studies described here. Some of these questions (left) are better served with the integration of data from new modalities and combined approaches (top right). This figure was created using the BioRender package