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snMultiome Analysis of Putative Microglial Substates in Alzheimer's Disease DLPFC

Overview

This project characterises chromatin accessibility and transcriptional programs of putative disease-associated microglial (DAM) substates in human Alzheimer's disease (AD) dorsolateral prefrontal cortex (DLPFC) using single-nucleus multiome sequencing (snRNA-seq + snATAC-seq). The analysis builds on Anderson et al. 2023 (Cell Genomics), who identified AD-specific cis-regulatory elements genome-wide and nominated MAFB as a microglial AD-associated regulator. This project focuses specifically on microglial substates, testing whether MAFB and NR4A2 motif activity programs separate homeostatic-like from DAM-like microglia at the chromatin level — a microglial-substate-focused reanalysis that was not the primary focus of the original study.

Dataset

  • Source: GSE214979 — Anderson et al. 2023, Cell Genomics
  • Title: Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer's disease-specific cis-regulatory elements
  • Content: 105,332 nuclei from human DLPFC, 7 AD donors + 8 unaffected controls
  • Technology: 10x Genomics Multiome (matched snRNA-seq + snATAC-seq per nucleus)
  • Files used: GSE214979_filtered_feature_bc_matrix.h5 (1.3 GB), GSE214979_cell_metadata.csv.gz
  • Note: Fragment file (59.3 GB) not downloaded — coverage plots not generated by design

Biological Question

Do MAFB and NR4A2 motif activity programs distinguish homeostatic-like and putative disease-associated microglial substates in human AD DLPFC, and are DAM-associated genes supported by concordant changes in gene expression and chromatin accessibility?

Key Findings

  • Three putative microglial substates identified after Harmony batch correction and WNN joint embedding: DAM-like (64 nuclei), Homeostatic-like (1,220 nuclei), Intermediate (1,695 nuclei). Substates were assigned based on published DAM and homeostatic gene signatures (Keren-Shaul et al. 2017).

  • 74% of Homeostatic-like nuclei were from unaffected controls; 67% of Intermediate nuclei were from AD donors — consistent with, but not proving, a possible homeostatic-to-disease-associated continuum.

  • 42 chromatin peaks significantly more accessible in putative DAM-like microglia (Wilcoxon rank sum test, adjusted p < 0.05) — near genes including TNFRSF9, DOCK10, NRXN3, PCDH9, and NEDD4L. Most peaks are intergenic, representing candidate distal regulatory elements.

  • 15 significant peak-to-gene links at DAM and homeostatic marker loci (Pearson correlation, p < 0.05). Among tested loci, LPL and TYROBP showed the highest positive peak-to-gene correlations (r = 0.14 and r = 0.08 respectively), suggesting concordant chromatin opening and gene expression at these DAM marker loci.

  • MAFB and NR4A2 motif activity did not significantly differ between putative DAM-like and Homeostatic-like substates (MAFB proxy p = 0.938, NR4A2 p = 0.819, Wilcoxon rank sum test). This is attributed to the small DAM-like population (64 nuclei) and the absence of a MAFB-specific position weight matrix in JASPAR2020 and JASPAR2022 — a MAF family proxy (MA1520.1) was used instead.

  • Monocle3 pseudotime placed the Intermediate population at the highest mean pseudotime (4.19), supporting a possible transitional interpretation between homeostatic and DAM-like states.

Key Plots

Microglial substates — WNN Harmony UMAP Substates

Volcano plot — DAM-like vs Homeostatic-like (DESeq2) Volcano

Peak-to-gene links at DAM and homeostatic loci Peak-gene links

Monocle3 pseudotime — homeostatic to DAM Pseudotime

Pipeline Overview

01_metadata_filter.R      Filter metadata to microglia nuclei
subset_microglia.py       Slice H5 file to microglia RNA + ATAC matrices
02_load_object.R          Build Seurat object with RNA and ATAC assays
03_qc.R                   QC filtering
04_normalisation.R        Log normalisation (RNA), TF-IDF (ATAC)
05_dimreduction.R         PCA (RNA), LSI (ATAC), DepthCor
06_clustering_umap.R      Clustering and UMAP per modality
07_harmony_wnn.R          Harmony batch correction, WNN joint embedding
08_dam_scoring.R          DAM/homeostatic module scoring, substate assignment
09_differential_rna.R     DESeq2 pseudobulk differential expression
10_differential_atac.R    Differential chromatin accessibility
11_chromvar.R             chromVAR TF motif activity (633 JASPAR2020 motifs)
12_peak_gene_linkage.R    Peak-to-gene linkage at DAM and homeostatic loci
13_trajectory.R           Monocle3 pseudotime trajectory analysis

How to Run

Requirements

  • R >= 4.4
  • Python >= 3.9 with conda environment containing: h5py, scipy, pandas, numpy
  • R packages: Seurat, Signac, harmony, DESeq2, chromVAR, JASPAR2020, monocle3, SeuratWrappers

Setup

# Clone repository
git clone https://github.com/Varun-U-Gowda/ad-microglia-multiome.git
cd ad-microglia-multiome

# Download data from GEO (not included in repo — large files)
# Place in data/raw/
# GSE214979_filtered_feature_bc_matrix.h5
# GSE214979_cell_metadata.csv.gz

Run

# In R, from project root
source("run_pipeline.R")

Python subsetting step runs automatically via system() call in run_pipeline.R. Requires conda environment path set correctly in run_pipeline.R.

Tool Stack

Tool Purpose
Seurat v5 RNA processing, WNN integration, clustering
Signac ATAC processing, differential accessibility, peak-to-gene linkage
Harmony Donor batch correction
chromVAR TF motif activity per nucleus
JASPAR2020 TF motif database (633 human motifs)
DESeq2 Pseudobulk differential expression
Monocle3 Pseudotime trajectory analysis
BSgenome.Hsapiens.UCSC.hg38 Human genome reference
EnsDb.Hsapiens.v86 Gene annotations

Limitations

  • Small putative DAM-like population — 64 nuclei across 15 donors limits statistical power for pseudobulk DE, differential ATAC, and chromVAR analyses. Results should be interpreted cautiously.
  • MAFB absent from JASPAR2020 and JASPAR2022 — MAF family proxy (MA1520.1) used; future analysis should use ChIP-seq derived MAFB motifs from ENCODE or ReMap.
  • Fragment file not downloaded — coverage and genome track plots not generated (fragment file = 59.3 GB).
  • LSI component 2 retained — moderate depth correlation (r = -0.539) noted; sensitivity analysis with dims 3:20 is a future direction.
  • Observational data only — all findings are associative. Causal TF function cannot be inferred from snATAC-seq data.
  • Substate assignment is score-based — DAM-like and Homeostatic-like labels are derived from published module scores, not validated by orthogonal methods in this project.

Relation to Anderson et al. 2023

Anderson et al. identified AD-specific cis-regulatory elements genome-wide across all brain cell types and nominated MAFB as a microglial AD-associated regulator. This project builds on that result by performing a focused microglial substate analysis — subclustering microglia, scoring for published DAM signatures, and testing whether MAFB and NR4A2 motif activity separate putative DAM-like from homeostatic-like substates. The primary focus of Anderson et al. was atlas-level CRE discovery across cell types; the microglial substate regulatory axis tested here was not their primary question.

Citations

See CITATIONS.md for full citations of the dataset, biological references, and all tools used.

Project Timeline

February 2026 – June 2026

Author

Varun U Gowda