Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of...

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Spatial transcriptomics (ST) offers spatially informed gene expression but has limited power for differential gene expression (DGE) in complex diseases like Alzheimer disease (AD) due to small sample sizes.
• Single-nucleus RNA sequencing (snRNA-seq) provides larger sample sizes for cell-type-specific (CTS) analyses but lacks spatial context.
• Integrating ST and snRNA-seq data can overcome limitations of individual methods for enhanced disease-specific DGE analysis.

Purpose of the Study:

• To integrate ST and snRNA-seq data to improve the power of spatially informed cell-type-specific (CTS) differential gene expression (DGE) analysis for AD-related phenotypes.
• To identify novel genes and pathways associated with AD pathogenesis by leveraging spatial and cell-type information.
• To discover potential therapeutic targets for AD by pinpointing layer- and cell-type-specific gene expression changes.

Main Methods:

• Utilized the CeLEry tool to infer six cortical layers from ∼1.5 million cells in snRNA-seq data from 436 postmortem dorsolateral prefrontal cortex (DLPFC) brains.
• Performed layer- and cell-type-specific (LCS) and CTS DGE analyses using a linear mixed model for β-amyloid, tangle density, and cognitive decline.
• Conducted gene set enrichment analyses on identified LCS DGE results, particularly for microglia in cortical layer 6 associated with β-amyloid.

Main Results:

• Identified 138 significant LCS genes (FDR q <0.05), including 103 for β-amyloid, 24 for tangle density, and 25 for cognitive decline.
• The majority of identified LCS genes, including AD risk genes like APOE, KCNIP3, and CTSD, were not detected by CTS analyses alone.
• Discovered 2 genes shared across all three phenotypes and 10 genes shared between two phenotypes. Gene set enrichment analysis identified 12 significant AD-related pathways in microglia.

Conclusions:

• Integrating spatial information with snRNA-seq data significantly enhances the power of spatially informed DGE analyses for complex diseases like AD.
• The identified LCS genes provide critical insights into AD pathogenesis and highlight potential novel therapeutic targets.
• This integrated approach advances the understanding of AD at a finer resolution, combining spatial and cellular information for robust biological discovery.