Single-cell multiregion epigenomic rewiring in Alzheimer's disease progression and cognitive resilience

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, yet its epigenetic underpinnings remain elusive. Here, we generate and integrate single-cell epigenomic and transcriptomic profiles of 3.5 million cells from 384 postmortem brain samples across 6 regions in 111 AD and control individuals. We identify over 1 million candidate cis-regulatory elements (cCREs), organized into 123 regulatory modules across 67 cell subtypes. We define large-scale epigenomic compartments and single-cell epigenomic information and delineate their dynamics in AD, revealing widespread epigenome relaxation and brain-region-specific and cell-type-specific epigenomic erosion signatures during AD progression. These epigenomic stability dynamics are closely associated with cell-type proportion changes, glial cell-state transitions, and coordinated epigenomic and transcriptomic dysregulation linked to AD pathology, cognitive impairment, and cognitive resilience. This study provides critical insights into AD progression and cognitive resilience, presenting a comprehensive single-cell multiomic atlas to advance the understanding of AD.

Keywords: Alzheimer's disease; cognitive resilience; epigenomic erosion; epigenomic information; epigenomic stability; epigenomics; exhaustion; microglial activation; regulatory network; single-cell multiomics.

Figure 1.. Single-cell epigenomic and transcriptomic landscape of AD across six brain regions

(A) Overview of study design, ROSMAP sample collection, single-cell profiling, and data analyses. (B and C) UMAP embeddings of 1.2 million snATAC nuclei (B) and 2.3 million snRNA nuclei (C), annotated into 7 major cell classes and 67 subtypes. Cell classes and brain regions are color-coded on the left; cell-subtype annotations are shown on the right for both modalities. (D) Heatmaps showing cell-subtype abundance across brain regions (left), gene activity/expression of 7,520 cell-subtype-specific genes across snATAC/snRNA (middle), and TF motif deviations (right). See also Figures S1 and S2, Table S1, and Data S1, pages 1–3.

Figure 2.. cCREs, cCRE modules, gene activity, expression, and GWAS risk variant interpretation across human brain cell types

(A) Overview of 1,010,153 cCREs organized into 123 distinct modules. Top: cell embeddings based on cCRE module activity, showing representative modules enriched in specific cell types. Middle: cCRE embeddings illustrating the distribution of cCREs within each representative module. Bottom: heatmap showing the activity of the 123 cCRE modules (columns) across various cell types and brain regions (rows). Cell types associated with each cCRE module are color-coded according to their cell subtype and subclass at the bottom. (B) snATAC gene activity for genes associated with the corresponding 123 cCRE modules from (A). (C) snRNA gene expression for genes associated with the corresponding 123 cCRE modules from (A). (D) LDSC enrichment analysis of GWAS risk variants across 67 brain cell subtypes for 90 neurological, psychiatric, neurodegenerative, and other complex traits. Statistical significance of GWAS enrichment is represented as −log₁₀(p value). See also Figure S3.

Figure 3.. Large-scale epigenomic compartment dynamics in AD across brain regions and cell types

(A) Heatmap of 27,993 100-kb genome-wide bins grouped into 25 ECGs, showing active (pink) and repressive (blue) compartments across 7 cell classes, 6 brain regions, and 3 AD stages. Highlighted are cell-type-specific active compartments and compartment switches in AD, including activation of repressive chromatin and repression of active regions. Bar plots (top) summarize compartment activity by brain region, cell class, and pathology. Bottom annotations include CpG density, A/T content, LAD/SPAD enrichment, and chromatin states. (B) Integrative analysis of LAD proportion, A/T content, epigenomic compartment states, and AD-associated dynamics across chromosomes. Left to right: bar plot of nuclear lamina interactions (ranked by LAD proportion), violin plot of A/T content (higher in repressive chromosomes), dot plots of active/repressive compartment proportions, and dynamic scores indicating compartment switching during AD. Positive values indicate repressive-to-active transitions; negative values reflect the reverse. (C) Chromatin accessibility increases in LADs and decreases in SPADs in lateAD across cell classes (Wilcoxon test). (D) Genome-wide view of epigenomic changes in lateAD. Top-left: whole-genome view of log₂(fold change) in snATAC signals (lateAD versus nonAD), with blue indicating repression and red indicating activation. Bottom-left: zoom-in views highlight global activation of repressive chr18 and repression of active chr19. Right: snATAC changes across brain regions and glial cell types, highlighting vulnerable brain regions with epigenomic dynamics. (E) Schematic model of compromised compartmentalization and epigenomic relaxation in AD. See also Figure S4 and Data S1, pages 4 and 5.

Figure 4.. Epigenomic information changes during AD progression across brain regions and cell types

(A) Heatmap of epigenomic information changes in 7 cell classes and 67 subtypes across 6 brain regions in AD. Bar plot on the right summarizes epigenomic information differences between lateAD and nonAD. Cell types with pronounced information loss are marked in blue; vulnerable brain regions are highlighted with black boxes. (B) UMAP and ridge plots showing glial subtypes and their activation/inflammation scores. (C) Density plots of epigenomic information in glial subtypes across AD stages. (D) Epigenomic information changes along the microglial activation trajectory across AD stages. (E) AD-related GWAS enrichment across the microglial activation trajectory across AD stages. (F) Modified epigenetic Waddington’s landscape model depicting glial-state transitions during AD progression—from homeostatic to reactive/activated and eventually to exhausted states. Homeostatic glial cells initially gain epigenomic information and identity upon activation, supporting reactivity function. However, with sustained activation, they progressively lose this information, culminating in an exhausted state characterized by diminished epigenomic identity in lateAD. See also Figure S5.

Figure 5.. Epigenomic information dynamics link to CR and cell-type composition changes in AD

(A) Heatmap showing higher epigenomic information in cognitively resilient individuals compared to cognitively vulnerable individuals, across brain regions, cell classes, and top-ranked vulnerable subtypes. (B) Negative correlation between epigenomic information changes in AD progression and CR. Vulnerable subtypes (top left) exhibit epigenomic information loss in AD but preservation in resilience. (C) Cell-type enrichment or depletion patterns linked to epigenomic information dynamics, CR, and AD pathology progression, shown as log₂(odds ratio) heatmaps. (D) Pearson correlations between cell-type fraction changes linked to epigenomic information dynamics, CR, and AD pathology progression. (E) Positive association between epigenomic information loss and cell-type depletion in AD based on snATAC data, highlighting vulnerable subtypes. (F) Schematic model illustrating AD-associated cellular community changes, including epigenomic information loss, selective neuronal depletion in vulnerable brain regions, and exhaustion of activated glial cells. See also Figure S6.

Figure 6.. Regulatory networks linking epigenomic information dynamics to AD pathology progression, cognitive function, and resilience

(A) Overview of DEGs, DARs, AD-GWAS risk genes, TFs, histone modifications, and chromatin states associated with epigenomic information dynamics and their links to AD pathology progression, cognitive function, and resilience. (B) GO term and pathway enrichment for DEGs associated with higher (red) or lower (blue) epigenomic information across brain regions and cell types. (C) Network of TFs and histone modifications enriched in DEGs with lower epigenomic information. Circles represent brain regions by cell class; diamonds indicate enriched factors. Edge thickness reflects enrichment significance −log₁₀(p value). (D) Representative differential expression of AD-GWAS genes associated with epigenomic information dynamics across brain regions and cell classes. (E) Chromatin-state enrichment of DARs associated with higher (top) or lower (bottom) epigenomic information. (F) Correlation between sample-level epigenomic information and AD pathology, cognitive function, and resilience. (G) Heatmap showing the Spearman correlations of log₂(fold change) for DARs and DEGs linked to epigenomic information dynamics, AD pathology progression, and cognitive measures. See also Figures S7, S8, S9, and S10 and Data S1, page 6.