{"doi":"10.64898/2026.01.30.702946","title":"Early microglial priming in Alzheimer’s disease revealed by ME-seq","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Epigenetic modifications, particularly DNA methylation, change dynamically with aging and are implicated in Alzheimer’s Disease (AD), yet how methylation interfaces with transcriptional and chromatin regulation at single-cell resolution remains poorly understood. Progress has been limited by a lack of scalable technologies capable of jointly profiling these regulatory layers. Here, we present ME-seq, a highly scalable technologies capable of simultaneously profiling DNA methylation, gene expression, and chromatin accessibility, while achieving a 100-fold reduction in cost. We generated over 400,000 single-nucleus trimodal profiles from the aging and AD mouse brain across ages, producing the first such atlas of neurodegeneration. We found AD progression triggers pronounced, disease-specific shifts in cellular composition, characterized by accelerated epigenetic aging and the expansion of disease-associated microglia (DAM). Integrative analyses, including aging clocks, revealed that DNA methylation acts as an early priming layer preceding transcriptional activation with IRF1 identified as a methylation-sensitive transcription factor serving as a gatekeeper for DAM activation. Our results establish ME-seq as a transformative tool for large-scale epigenomic dissection, revealing DNA methylation as a primary coordinator of cell-state transitions in the aging brain.</jats:p>\n                <jats:sec id=\"s1\">\n                  <jats:title>Graphic abstract</jats:title>\n                  <jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\">\n                    <jats:graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"702946v1_ufig1\" position=\"float\" orientation=\"portrait\"/>\n                  </jats:fig>\n                </jats:sec>","journal":null,"year":null,"id":614292,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":376292,"name":"Anurupa Ghosh","orcid":"0000-0001-7028-9792","position":1,"is_corresponding":false},{"id":471199,"name":"Zhe Wang","orcid":"0000-0002-3939-4452","position":2,"is_corresponding":false},{"id":1582882,"name":"Zhicong Liao","orcid":null,"position":3,"is_corresponding":false},{"id":1582883,"name":"Michael Appiah","orcid":null,"position":4,"is_corresponding":false},{"id":128945,"name":"Jiayi Li","orcid":"0009-0007-5757-3861","position":5,"is_corresponding":false},{"id":1027986,"name":"Mimi Zhang","orcid":"0000-0003-3273-540X","position":6,"is_corresponding":false},{"id":329938,"name":"Federico Di Tullio","orcid":"0000-0001-9756-976X","position":7,"is_corresponding":false},{"id":1582888,"name":"Agata Kurowski","orcid":null,"position":8,"is_corresponding":false},{"id":1538415,"name":"John Fullard","orcid":null,"position":9,"is_corresponding":false},{"id":476926,"name":"Elvin Wagenblast","orcid":"0000-0002-0709-2759","position":10,"is_corresponding":false},{"id":2335,"name":"Martin J. Walsh","orcid":"0000-0001-8339-8285","position":11,"is_corresponding":false},{"id":27629,"name":"Alison Goate","orcid":"0000-0002-0576-2472","position":12,"is_corresponding":false},{"id":1203,"name":"Panos Roussos","orcid":"0000-0002-4640-6239","position":13,"is_corresponding":false},{"id":597955,"name":"Ka Lung Cheung","orcid":null,"position":14,"is_corresponding":false},{"id":684537,"name":"Nan Yang","orcid":"0000-0003-3663-1288","position":15,"is_corresponding":false},{"id":3622,"name":"Sai Ma","orcid":"0000-0002-9785-7929","position":16,"is_corresponding":false},{"id":622859,"name":"Bohan Zhu","orcid":"0009-0003-9823-8630","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Early microglial priming in Alzheimer’s disease revealed by ME-seq","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Epigenetic modifications, particularly DNA methylation, change dynamically with aging and are implicated in Alzheimer’s Disease (AD), yet how methylation interfaces with transcriptional and chromatin regulation at single-cell resolution remains poorly understood. Progress has been limited by a lack of scalable technologies capable of jointly profiling these regulatory layers. Here, we present ME-seq, a highly scalable technologies capable of simultaneously profiling DNA methylation, gene expression, and chromatin accessibility, while achieving a 100-fold reduction in cost. We generated over 400,000 single-nucleus trimodal profiles from the aging and AD mouse brain across ages, producing the first such atlas of neurodegeneration. We found AD progression triggers pronounced, disease-specific shifts in cellular composition, characterized by accelerated epigenetic aging and the expansion of disease-associated microglia (DAM). Integrative analyses, including aging clocks, revealed that DNA methylation acts as an early priming layer preceding transcriptional activation with IRF1 identified as a methylation-sensitive transcription factor serving as a gatekeeper for DAM activation. Our results establish ME-seq as a transformative tool for large-scale epigenomic dissection, revealing DNA methylation as a primary coordinator of cell-state transitions in the aging brain.</jats:p>\n                <jats:sec id=\"s1\">\n                  <jats:title>Graphic abstract</jats:title>\n                  <jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\">\n                    <jats:graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"702946v1_ufig1\" position=\"float\" orientation=\"portrait\"/>\n                  </jats:fig>\n                </jats:sec>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41676571","pmcid":null,"openalex_id":"https://openalex.org/W7127155511","authors":[],"funders":[{"funder_name":"NCI NIH HHS","grant_id":"U01CA290442","title":null},{"funder_name":"NCI NIH HHS","grant_id":"R61CA297881","title":null},{"funder_name":"NCI NIH HHS","grant_id":"R21CA301237","title":null},{"funder_name":"NIAID NIH HHS","grant_id":"R01AI168004","title":null},{"funder_name":"","grant_id":"Friedman Brain Institute's Research Scholar","title":null}],"total_grants":5,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2026,"count":2}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.64898/2026.01.30.702946","host_type":"repository"},{"url":"https://doi.org/10.64898/2026.01.30.702946","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.64898/2026.01.30.702946","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41676571","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12889493","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12889493/","host_type":"repository"}],"fields_of_study":["Epigenetics and DNA Methylation","Neuroinflammation and Neurodegeneration Mechanisms","Single-cell and spatial transcriptomics"],"mesh_terms":[],"keywords":["Epigenetics","DNA methylation","Chromatin","Epigenomics","Histone","Transcriptome","Transcription factor","Reprogramming","Priming (agriculture)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T12:15:05.749301Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}