Epigenomic subtypes of late-onset Alzheimer's disease reveal distinct microglial signatures

Abstract

Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimer's disease contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This insight underscores the need to expand molecular subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation data from three independent postmortem brain cohorts (n = 831) to identify epigenetic subtypes of late-onset Alzheimer's disease. Unsupervised clustering approaches were employed to identify distinct DNA methylation patterns, with subsequent cross-cohort validation to ensure robustness and reproducibility. To explore the cell-type specificity of the identified epigenomic subtypes, we characterized their methylation signatures utilizing DNA methylation profiles derived from purified brain cells. Transcriptomic data from bulk and single-cell RNA sequencing were integrated to examine the functional impact of epigenetic subtypes on gene expression profiles. Finally, we performed statistical analyses to investigate associations between these DNA methylation-defined subtypes and clinical or neuropathological features, aiming to elucidate their biological significance and clinical implications. We identified two distinct epigenomic subtypes of late-onset Alzheimer's disease, each defined by reproducible DNA methylation patterns across three cohorts. Both subtypes exhibit cell-type-specific DNA methylation profiles. Subtype 1 and subtype 2 show significant microglial methylation enrichment, with odds ratios (OR) of 1.6 and 1.3, respectively. The minimal overlap between them suggests distinct microglial states. Additionally, subtype 2 displays strong neuronal (OR = 1.6) and oligodendrocyte (OR = 3.6) enrichment. Bulk transcriptomic analyses further highlighted divergent biological mechanisms underpinning these subtypes, with subtype 1 enriched for immune-related processes, and subtype 2 characterized predominantly by neuronal and synaptic functional pathways. Single-cell transcriptional profiling of microglia revealed subtype-specific inflammatory states: subtype 1 represented a state of chronic innate immune hyperactivation with impaired resolution, while subtype 2 exhibited a more dynamic inflammatory profile balancing pro-inflammatory signaling with reparative and regulatory mechanisms. This study highlights the molecular heterogeneity of late-onset Alzheimer's disease by identifying two epigenetic subtypes with distinct cell-type-specific DNA methylation patterns. Their alignment with previously defined molecular classifications underscores their relevance in disease pathogenesis. By linking these subtypes to inflammatory microglial activity, our findings provide a foundation for future precision medicine approaches in Alzheimer's research and treatment.

Keywords: Alzheimer’s disease; DNA methylation; Epigenetics; Inflammation; Microglia; Subtyping.

Figure 1.. Cross-cohort generalizability of DNAm-based clusters.

(a) Visual representation of the correlation-based replication of the identified clusters across three cohorts using Hierarchical (Left), and K-means (Right) methods. The first block (LOAD-S1) includes UKBBN cluster A, PITT-ADRC cluster B, and ROSMAP cluster A, while the second block (LOAD-S2) includes UKBBN cluster B, PITT-ADRC cluster C, and ROSMAP cluster B. (b) Spatial overlap analysis showing cluster assignments across the three cohorts. In this example, the clusters were confirmed through iterative projections, where UKBBN cohort’s first two latent spaces were projected onto the PTT-ADRC and ROSMAP to verify spatial overlap across datasets.

Figure 2.. Characterization of the methylomic signatures of predicted LOAD subtypes.

(a) Visualization of first two PCs for LOAD subtypes across all cohorts. Plot of the first two PCs (PC1 and PC2) for DNAm profiles from all samples in the UKBBN, PITT-ADRC, and ROSMAP cohorts. Samples are labeled according to their subtype assignments: Subtype 1 (LOAD-S1) and Subtype 2 (LOAD-S2). ‘Unassigned’ samples are also indicated, representing clusters without matching correlations across cohorts. (b) Venn diagram illustrating the overlap of differentially methylated positions (DMPs) associated with LOAD-S1, LOAD-S2, and the Unassigned group relative to overall LOAD. Minimal overlap is observed between LOAD-S1 and LOAD-S2, highlighting their distinct methylomic profiles. (c) CpG overlap of the cell type-specific methylation signatures for overall LOAD, LOAD-S1, LOAD-S2 and Unassigned group derived from FANS data. LOAD-S1 shows a stronger association with microglial DMPs, whereas LOAD-S2 displays significant contributions from neuronal, oligodendrocyte, and microglial DNAm profiles. (d) Subtype-specific methylation quantitative trait loci (mQTL) mapping and genomic analysis. Key subtype-specific CpG sites annotated to AD-related genes are shown (Right).

Figure 3.. Transcriptomic characterization of LOAD epigenomic Subtypes.

(a) Heatmap showing Z-scores for the top 20 enriched Gene Ontology (GO) terms in LOAD-S1 and LOAD-S2 subtypes. Z-scores indicate the deviation of observed gene counts from expected values, normalized by standard deviation. LOAD-S1 is enriched in immune-related pathways, reflecting a dominant immune/inflammatory signature, while LOAD-S2 shows enrichment in synaptic and neuronal pathways. Immune pathways prevalent in LOAD-S1 are demonstrating weak enrichment in LOAD-S2, and vice versa. (b) Comparative analysis of differentially expressed genes (DEGs) across seven microglial (MG) states (MG 0–6) highlights distinct gene expression profiles for LOAD-S1 and LOAD-S2, with minimal overlap, underscoring subtype-specific transcriptional landscapes. (c) GO enrichment analysis of microglial single-cell DEGs reveals shared enrichment in the ‘regulation of innate immune response’ (GO:0045088) pathway, but with distinct contributing genes. LOAD-S1-specific pathways include NF-kappaB regulation (GO:0051092), inflammasome signaling (GO:0141085), NLRP3 assembly (GO:1900225), MHC class II binding (GO:0023026), and T cell activation (GO:0002287). LOAD-S2-specific pathways include TLR4 signaling (GO:0034142), receptor signaling in phagocytosis (GO:0002433), and inflammatory response regulation (GO:0050727, GO:0050729, GO:0061702).