Microglia Gene Regulatory Network Atlas in Alzheimer's Disease (MiGRAND)

Single-Cell Transcriptomics Explorer

This interactive database presents the results of a comprehensive single-cell RNA sequencing (scRNA-seq) analysis of microglia from Alzheimer's disease (AD) patients.

The data were obtained from the prefrontal cortex and include a total of 40,967 cells from female and 35,928 cells from male donors.

Microglia were clustered into 12 transcriptionally distinct subpopulations , within which multiple downstream analyses were conducted, including:

  • Differential gene expression analysis across AD disease states
  • Gene regulatory network (GRN) analysis and identification of disease-specific regulons
  • Integration of eQTL and GWAS datasets to explore regulatory variants associated with Alzheimer's disease

Cell Clustering

Cell Marker expression

Gene expression

Differential Expressed Genes

Visualization

Regulon Specificity Score

Regulon activity

Network by regulon

Network by gene

Gene set enrichment analysis for DEGs

Gene set enrichment analysis for Regulons

eQTL


GWAS

Workflow Overview

Workflow Diagram

1. Single-cell Data Collection

Publicly available single-cell RNA sequencing datasets of Alzheimer's Disease (AD) patients were collected from the prefrontal cortex region.

2. Microglia Extraction and Subclustering

Microglia cells were extracted based on cell type annotations. According to reference definitions, microglia were further subdivided into 12 transcriptionally distinct subpopulations.

3. Differential Expression Analysis

Within each microglial subpopulation, differential gene expression analysis was performed across different AD disease states using the Seurat R package.

4. Gene Regulatory Network (GRN) Construction

Transcriptional regulatory networks were reconstructed for each microglial subpopulation utilizing the pySCENIC pipeline.

5. Pathway Enrichment Analysis

Differentially expressed genes were subjected to functional enrichment analysis using the clusterProfiler R package to identify dysregulated biological pathways.

Scenic for GRN analysis

Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts S. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017 Nov;14(11):1083-1086. doi: 10.1038/nmeth.4463. Epub 2017 Oct 9. PMID: 28991892; PMCID: PMC5937676.