Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell...

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Alzheimer's disease (AD) is marked by neuron loss and gliosis.
• Bulk tissue gene expression studies may misinterpret cell-type composition changes as transcriptional dysregulation.
• Understanding cell-type specific changes is crucial for accurate Alzheimer's research.

Purpose of the Study:

• To differentiate between cell-type proportion shifts and true transcriptional regulation in Alzheimer's disease gene expression data.
• To identify conserved gene co-expression modules affected by Alzheimer's disease.
• To validate findings across multiple independent transcriptomic datasets.

Main Methods:

• Mined five large transcriptomic Alzheimer's disease datasets for conserved gene co-expression modules.
• Analyzed differential gene expression and co-expression within modules between AD and control samples.
• Utilized cell-type deconvolution to assess the contribution of cell-type proportion changes versus transcriptional regulation.

Main Results:

• Increased microglia module expression in AD is explained by higher microglia cell proportions.
• Decreased neuron module expression and altered co-expression in AD suggest altered neuronal pathway regulation.
• Astrocyte module changes are attributed to astrogliosis and gene activation, correlating with AD biomarkers.

Conclusions:

• Combinatorial analysis of transcriptomic data can distinguish cell-type composition effects from true regulatory changes in Alzheimer's disease.
• Identified specific pathways and transcription factors potentially driving neuronal and astrocyte dysregulation in AD.
• This approach provides a more accurate understanding of the molecular underpinnings of Alzheimer's disease.