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

**Authors:** Valentin T. Laroche, Rachel Cavill, Morteza Kouhsar, Joshua Müller, Rick A. Reijnders, Joshua Harvey, Adam R. Smith, Jennifer Imm, Jarno Koetsier, Luke Weymouth, Lachlan MacBean, Giulia Pegoraro, Lars Eijssen, Byron Creese, Gunter Kenis, Betty M. Tijms, Daniel van den Hove, Katie Lunnon, Ehsan Pishva

PMC · DOI: 10.1007/s00401-026-02990-y · Acta Neuropathologica · 2026-02-24

## TL;DR

This study identifies two distinct epigenetic subtypes of late-onset Alzheimer’s disease, each with unique microglial signatures, offering new insights into disease heterogeneity and potential for targeted therapies.

## Contribution

The study introduces epigenomic subtyping of LOAD, revealing novel microglial methylation patterns and distinct biological mechanisms across subtypes.

## Key findings

- Two consistent epigenomic subtypes of LOAD were identified across three independent cohorts.
- Subtype 1 is associated with immune-related processes, while subtype 2 involves neuronal and synaptic pathways.
- Both subtypes show distinct microglial methylation enrichment and state-dependent transcriptional shifts.

## Abstract

Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimer’s disease (LOAD) 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 underscores the need to expand subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation (DNAm) data from three independent postmortem brain cohorts (N = 826) to identify epigenetic subtypes of LOAD. We used unsupervised clustering to define subtype-specific DNAm patterns and validated them across cohorts. We then mapped subtype signatures to brain cell types using purified-cell DNAm profiles and integrated bulk and single-nucleus RNA-seq to assess each subtype’s impact on gene expression. Finally, we examined clinical and neuropathological correlates to evaluate biological and clinical significance. We identified two distinct epigenomic subtypes of LOAD, consistently observed across three cohorts. Both subtypes exhibit significant yet distinct microglial methylation enrichment. Bulk transcriptomic analyses highlighted distinct biological mechanisms underlying these subtypes: subtype 1 was enriched for immune-related processes, while subtype 2 was characterized by neuronal and synaptic pathways. Single-nucleus transcriptional profiling of microglia indicated that both subtypes share AD-associated innate-immune remodeling, with subtype differences emerging primarily as state-dependent transcriptional shifts rather than large changes in state abundance. Overall, subtype 1 showed a relative weighting toward more inflammatory microglial programs, whereas subtype 2 showed stronger transcriptional remodeling in specific microglial states alongside relatively greater engagement of regulatory and clearance-associated features. These findings reveal distinct epigenetic and functional microglial states underlying LOAD subtypes, advancing our understanding of disease heterogeneity. This work lays the groundwork for targeted therapeutic strategies tailored to specific molecular and cellular disease profiles.

The online version contains supplementary material available at 10.1007/s00401-026-02990-y.

## Linked entities

- **Diseases:** Alzheimer’s disease (MONDO:0004975)

## Full-text entities

- **Genes:** MGAT5 (alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase) [NCBI Gene 4249] {aka GNT-V, GNT-VA, MGAT5A, glcNAc-T V}, HSPA1B (heat shock protein family A (Hsp70) member 1B) [NCBI Gene 3304] {aka HSP70-1, HSP70-1B, HSP70-2, HSP70.1, HSP70.2, HSP72}, MAPT (microtubule associated protein tau) [NCBI Gene 4137] {aka DDPAC, FTD1, FTDP-17, MAPTL, MSTD, MTBT1}, TYMP (thymidine phosphorylase) [NCBI Gene 1890] {aka ECGF, ECGF1, MEDPS1, MNGIE, MTDPS1, PDECGF}, UNC93B1 (unc-93B1 regulator of TLR signaling) [NCBI Gene 81622] {aka IIAE1, UNC-93B, UNC93, UNC93B, Unc-93B1}, APOE (apolipoprotein E) [NCBI Gene 348] {aka AD2, APO-E, ApoE4, LDLCQ5, LPG}, SNCA (synuclein alpha) [NCBI Gene 6622] {aka NACP, PARK1, PARK4, PD1}, KRT16 (keratin 16) [NCBI Gene 3868] {aka CK16, FNEPPK, K16, K1CP, KRT16A, NEPPK}, EP300 (EP300 lysine acetyltransferase) [NCBI Gene 2033] {aka KAT3B, MKHK2, RSTS2, p300}, PRKCE (protein kinase C epsilon) [NCBI Gene 5581] {aka PKCE, nPKC-epsilon}, YWHAE (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon) [NCBI Gene 7531] {aka 14-3-3E, HEL2, KCIP-1, MDCR, MDS}, PDE7B (phosphodiesterase 7B) [NCBI Gene 27115] {aka bA472E5.1}, PTPN1 (protein tyrosine phosphatase non-receptor type 1) [NCBI Gene 5770] {aka PTP1B}, ATG16L2 (autophagy related 16 like 2) [NCBI Gene 89849] {aka ATG16B, WDR80}, HLA-DPA1 (major histocompatibility complex, class II, DP alpha 1) [NCBI Gene 3113] {aka DP(W3), DP(W4), DPA1, HLA-DP1A, HLA-DPA, HLADP}, APP (amyloid beta precursor protein) [NCBI Gene 351] {aka AAA, ABETA, ABPP, AD1, APPI, CTFgamma}, CEROX1 (cytoplasmic endogenous regulator of oxidative phosphorylation 1) [NCBI Gene 115804232], HLA-DPB1 (major histocompatibility complex, class II, DP beta 1) [NCBI Gene 3115] {aka DPB1, HLA-DP, HLA-DP1B, HLA-DPB}, VSIG4 (V-set and immunoglobulin domain containing 4) [NCBI Gene 11326] {aka CRIg, Z39IG}, CMTM5 (CKLF like MARVEL transmembrane domain containing 5) [NCBI Gene 116173] {aka CKLFSF5}, TARDBP (TAR DNA binding protein) [NCBI Gene 23435] {aka ALS10, TDP-43}, GPR37L1 (G protein-coupled receptor 37 like 1) [NCBI Gene 9283] {aka ET(B)R-LP-2, ETBR-LP-2, ETBRLP2}, PPARG (peroxisome proliferator activated receptor gamma) [NCBI Gene 5468] {aka CIMT1, FPLD3, GLM1, NR1C3, PPARG1, PPARG2}, PARP14 (poly(ADP-ribose) polymerase family member 14) [NCBI Gene 54625] {aka ARTD8, BAL2, PARP-14, pART8}, ARHGAP45 (Rho GTPase activating protein 45) [NCBI Gene 23526] {aka HA-1, HLA-HA1, HMHA1}, TGFB1 (transforming growth factor beta 1) [NCBI Gene 7040] {aka CAEND1, CED, DPD1, IBDIMDE, LAP, TGF-beta1}, ITSN1 (intersectin 1) [NCBI Gene 6453] {aka ITSN, SH3D1A, SH3P17}, CPEB1 (cytoplasmic polyadenylation element binding protein 1) [NCBI Gene 64506] {aka CPE-BP1, CPEB, CPEB-1, h-CPEB, hCPEB-1}, SPI1 (Spi-1 proto-oncogene) [NCBI Gene 6688] {aka AGM10, OF, PU.1, SFPI1, SPI-1, SPI-A}, STAT2 (signal transducer and activator of transcription 2) [NCBI Gene 6773] {aka IMD44, ISGF-3, P113, PTORCH3, STAT113}, BIN1 (bridging integrator 1) [NCBI Gene 274] {aka AMPH2, AMPHL, CNM2, SH3P9}
- **Diseases:** argyrophilic grain disease (MESH:C537394), hematopoietic malignancies (MESH:D019337), NFT (MESH:D055956), cerebral amyloid angiopathy (MESH:D016657), mQTL (OMIM:612306), brain atrophy (MESH:C566985), AD (MESH:D000544), atrophy (MESH:D001284), neuroinflammation (MESH:D000090862), FANS (MESH:C566014), Lewy body disease (MESH:D020961), inflammation (MESH:D007249), neurodegeneration (MESH:D019636), immune dysregulation (OMIM:614878), synaptic dysfunction (MESH:C536122), neuritic plaques (MESH:D058225), amyloid (MESH:C000718787), Dementia (MESH:D003704), DMPs (MESH:D012734), cerebrovascular disease (MESH:D002561), DEA (MESH:D001039), leukemia (MESH:D007938), death (MESH:D003643)
- **Chemicals:** glycan (MESH:D011134), Bisulfite (MESH:C042345), lipid (MESH:D008055), organic acid (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Cell lines:** LOAD — Homo sapiens (Human), Glycogen storage disease type II, Induced pluripotent stem cell (CVCL_0H84), MG4 — Trichoplusia ni (Cabbage looper), Spontaneously immortalized cell line (CVCL_Z093), S2 — Drosophila melanogaster (Fruit fly), Spontaneously immortalized cell line (CVCL_Z232)

## Full text

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## Figures

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Source: https://tomesphere.com/paper/PMC12932393