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Basic Science and Pathogenesis.

Jaclyn Beck1, Vilas Menon2

  • 1Sage Bionetworks, Seattle, WA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
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Summary
This summary is machine-generated.

This study identified five neuronal subtypes vulnerable to Alzheimer's disease (AD) by analyzing bulk RNA sequencing data. These findings advance our understanding of AD's cellular impact and guide future research.

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Area of Science:

  • Neuroscience
  • Genomics
  • Computational Biology

Background:

  • Alzheimer's disease (AD) impacts brain regions and cell populations differently.
  • High-resolution analysis of vulnerable cell populations is challenging due to single-cell transcriptomics costs and cell survivability issues.
  • Bulk RNA sequencing offers larger sample sizes but lacks cell-specific data.

Purpose of the Study:

  • To deconvolve cell type proportions in bulk RNA samples of human brains using existing single-cell RNA data.
  • To identify cell population changes associated with Alzheimer's disease.
  • To leverage the benefits of both single-cell and bulk RNA sequencing modalities.

Main Methods:

  • Analysis of three large-scale bulk RNA sequencing datasets (Mayo RNAseq, Mount Sinai Brain Bank, ROSMAP).
  • Creation of gene marker lists from five AD-related single-cell RNA datasets.
  • Application of six deconvolution algorithms (CIBERSORTx, DeconRNASeq, Dtangle, DWLS, MuSiC, Scaden) to estimate cell type proportions.

Main Results:

  • Significant AD-related decreases identified in 5 of 17 neuronal subtypes (p < 0.01), replicated across multiple algorithms and tissues.
  • Significant increases observed in astrocytes and oligodendrocytes.
  • CIBERSORTx, dtangle, and MuSiC, using Mathys 2019 markers, yielded the best deconvolution results.

Conclusions:

  • Identification of five neuronal subtypes potentially vulnerable to Alzheimer's disease.
  • Findings can guide future single-cell resolution experiments focusing on vulnerable neuronal populations.
  • Deconvolution method experimentation provides insights for analyzing similar data with rare cell types.