Avoiding false discoveries: Revisiting an Alzheimer’s disease snRNA-Seq dataset

Abstract

Arising From: Mathys, H. et al. Nature (2019). https://doi.org/10.1038/s41586–019–1195–2 Mathys et al., conducted the first single-nucleus RNA-Seq study (snRNA–Seq) of Alzheimer′s disease (AD). The authors profiled the transcriptomes of approximately 80,000 cells from the prefrontal cortex, collected from 48 individuals – 24 of which presented with varying degrees of AD pathology. With bulk RNA-Seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single–cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type–specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues with best–practice approaches to snRNA–Seq processing and differential expression, resulting 892 times fewer differentially expressed genes at a false discovery rate (FDR) of 0.05.

Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements

“…One potential concern is that multiplex, single-cell CRISPR screens that treat cells as pseudo-replicates have the potential to suffer from an overestimation of differentially expressed genes due to the statistical dependency between cells originating from the same starting population 39 . Our singleton validation results allay this concern, but we cannot fully rule out overestimation.…”

An overview of human single-cell RNA sequencing studies in neurobiological disease

“…Like Grubman et al, they found upregulation of protein folding genes, molecular chaperone genes, and global stress response genes in multiple cell types in late-stage AD samples vs early-stage AD samples. Importantly, it is worth noting that a recent preprint reanalyzed this data set using best-practice approaches and found different results ( Murphy et al, 2023 ), underscoring the importance of re-assessing datasets based on consistently-evolving platforms.…”