ArticleCell2023
Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion.
Open access · hybridAbstract read
In one paragraph
Article in Cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 2 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
129citing papers in PubMed, 2 pooled it
26.8field-weighted citation impact, top 1% of its field
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
129 citing papers in PubMed, 2 syntheses or guidelines pooled it, 175 citations in OpenAlex.
- Improving reproducibility of differentially expressed genes in single-cell transcriptomic studies of neurodegenerative diseases through meta-analysis.Nature communications · 2025Pooled it
- Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders.GigaScience · 2025Pooled it
- Review
- Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer's disease.Nature communications · 2026Article
- Reduced expression of Brain Expressed X-linked genes in Alzheimer's disease.Journal of Alzheimer's disease : JAD · 2026Article
- Genome-wide association analyses highlight the neuronal contribution to multiple sclerosis susceptibility.Nature genetics · 2026Article
- Alzheimer's disease through the lens of mesenchymal drift: the collapse of cellular identity.BMB reports · 2026Review
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- Coordinated dysregulation of modular gene activity in human neuropathologies.bioRxiv : the preprint server for biology · 2026Article
- Alzheimer's Disease as a Multi-Layer Network Disorder: A Systems Biology Framework Integrating Multi-Omics Mechanisms.Biomedicines · 2026Review
- Single-cell multiomics connects 3D genome and transcriptome alterations in Alzheimer's disease.Science (New York, N.Y.) · 2026Article
- KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease.iScience · 2026Article
- Single-cell profiling of DNA methylation in autism spectrum disorder prefrontal cortex reveals distinct regulatory and aging signatures.Cell genomics · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- Epigenetic mechanisms of inflammatory memory in the central nervous system.Nature immunology · 2026Review
- mMolecular medicine reports · 2026Review
- FAM134B-mediated ER-phagy degrades APP and suppresses Alzheimer's disease pathology.The EMBO journal · 2026Article
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Omics-driven strategies for identifying biomarkers in Alzheimer's disease.Metabolic brain disease · 2026Review
- A computational framework for epigenetic plasticity in memory.Brain : a journal of neurology · 2026Review
69 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
17 authors at 6 institutions in 3 countries.
Xushen XiongComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China.
Benjamin T JamesComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Carles A BoixComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Yongjin P ParkComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Kyriaki GalaniComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Matheus B VictorPicower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Na SunComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Lei HouComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Li-Lun HoComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Julio ManteroComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
Aine Ni ScannailPicower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Vishnu DileepPicower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Weixiu DongDepartment of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
Hansruedi MathysPicower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
David A BennettRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA.
Li-Huei TsaiThe Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: lhtsai@mit.edu.
Manolis KellisComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Electronic address: manoli@mit.edu.
Broad Institute · USMassachusetts Institute of Technology · USRush University Medical Center · USUniversity of California San Diego · USUniversity of Pittsburgh · USVassar College · US
Funding
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1MEPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3MRush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7MRISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4MMulti-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
$13.7MPathway discovery, validation and compound identification for Alzheimer's disease - SupplementU01AG046152 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2013 to 2017
$13.6MSingle Cell Transcriptomic and Epigenomic Dissection of Opioid and Cocaine Responses in HIVU01DA053631 · NIDA · BROAD INSTITUTE, INC. · PI HEIMAN, MYRIAM, KELLIS, MANOLIS · 2021 to 2025
$12.6MMapping the vulnerable locus coeruleus pathways in aging and ADU01AG077227 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Kwanghun Chung, Li-Huei Tsai · 2022 to 2026
$9.6MIdentification of TDP-43 Modifiers Through Single-Cell Transcriptional and Epigenomic Dissection of ALS and FTLD-MNDR01NS127187 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BELZIL, VERONIQUE, DONNELLY, CHRISTOPHER JAMES · 2021 to 2025
$9.1MEpigenomic, transcriptional and cellular dissection of Alzheimer's variantsR01AG058002 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI HYMAN, BRADLEY T., JAENISCH, RUDOLF · 2017 to 2021
$7.9MElucidating the Molecular Mechanisms of Neuropsychiatric Symptoms in Alzheimer's DiseaseR01AG062335 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2018 to 2022
$6.5MSingle-cell epigenomic and trancriptional dissection of sex-specific differences in Alzheimer’s DiseaseR01AG074003 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2021 to 2025
$5.4MNHGRI NIH HHS R01 HG008155NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG072975NIA NIH HHS R01 AG015473NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG058002NIA NIH HHS R01 AG062335NIA NIH HHS R01 AG067151NIA NIH HHS R01 AG074003NIA NIH HHS R01 AG081017NIA NIH HHS R56 AG067151NIA NIH HHS RF1 AG054012NIA NIH HHS RF1 AG057473NIA NIH HHS RF1 AG062377NIA NIH HHS U01 AG046152NIA NIH HHS U01 AG061356NIA NIH HHS U01 AG077227NIDA NIH HHS U01 DA053631NIGMS NIH HHS T32 GM139790NIMH NIH HHS R01 MH109978NIMH NIH HHS U01 MH119509NINDS NIH HHS R01 NS127187NINDS NIH HHS RF1 NS129032NINDS NIH HHS U01 NS110453NINDS NIH HHS UG3 NS115064NINDS NIH HHS UH3 NS115064
Abstract
Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.
Indexed as
Alzheimer DiseaseBrainGene Expression RegulationEpigenomeEpigenomicsGenome-Wide Association StudyHumansAlzheimer’s diseaseATAC-QTLepigenomeepigenome erosionfine-mappingGWASmultimodal integrationpeak-to-gene linking
Identifiers
What OpenQuestion holds
Textmetadata
LicenceTDM
Read underepoch 390
Registered trials
None linked
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.