A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research
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Abstract
We initiated the systematic profiling of the dorsolateral prefrontal cortex obtained from a subset of autopsied individuals enrolled in the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP), which are jointly designed prospective studies of aging and dementia with detailed, longitudinal cognitive phenotyping during life and a quantitative, structured neuropathologic examination after death. They include over 3,322 subjects. Here, we outline the first generation of data including genome-wide genotypes (n=2,090), whole genome sequencing (n=1,179), DNA methylation (n=740), chromatin immunoprecipitation with sequencing using an anti-Histone 3 Lysine 9 acetylation (H3K9Ac) antibody (n=712), RNA sequencing (n=638), and miRNA profile (n=702). Generation of other omic data including ATACseq, proteomic and metabolomics profiles is ongoing. Thanks to its prospective design and recruitment of older, non-demented individuals, these data can be repurposed to investigate a large number of syndromic and quantitative neuroscience phenotypes. The many subjects that are cognitively non-impaired at death also offer insights into the biology of the human brain in older non-impaired individuals.
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Early EWAS identified differentially methylated regions in genes including ANK1 and BIN1, suggesting that epigenetic modifications may converge on biological pathways previously implicated by genetic studies.
“Early epigenome-wide association studies (EWAS) identified differentially methylated regions (DMRs) in genes including ankyrin 1 (ANK1) and BIN1, suggesting that epigenetic modifications may converge on biological pathways previously implicated by genetic studies124,152.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [124]
The References section cites an earlier study titled “Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci,” and Technical Validation says methylation findings were replicated. But this atlas does not itself state that the findings were differentially methylated regions or draw the proposed connection to pathways implicated by genetic studies. (LLM, conf=0.77)
Experimental — accuracy not yet measuredLarge-scale studies such as ROS/MAP have used multi-omic data from dorsolateral prefrontal cortex tissue to identify Alzheimer's disease subtypes that exhibit distinct cognitive trajectories.
“Large-scale studies such as ROS/MAP project have utilized multi-omic data from dorsolateral prefrontal cortex tissue to identify AD subtypes that exhibit distinct cognitive trajectories.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [124]
The Background & Summary describes multi-omic data from ROSMAP dorsolateral prefrontal cortex tissue and a measure of each participant’s cognitive-decline trajectory (page not provided). It does not report using those data to identify Alzheimer’s disease subtypes with distinct cognitive trajectories. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAMP-AD resource includes post mortem RNA-seq datasets from ROSMAP, MSSM, and Mayo Clinic, encompassing >1200 individuals across seven brain regions.
“This resource includes post mortem RNA‐seq datasets from the Religious Orders Study and Memory and Aging Project (ROSMAP) 40 , Mount Sinai School of Medicine (MSSM), 39 and Mayo Clinic, 38 encompassing > 1200 individuals across seven brain regions (Figure 6A).”
From Unique transcriptomic alterations in 5XFAD;PS19 mouse model identify glial lipid dysregulation and coordinated microglial–oligodendrocyte responses (2026) · cites this paper as [40]
The DLPFC controls working memory and executive function and is a key neocortical region affected in AD.
“The DLPFC controls working memory and executive function and is a key neocortical region affected in AD.”
From Unraveling sex differences in Alzheimer's disease and related endophenotypes with brain proteomes (2025) · cites this paper as [28]
Bulk RNA-Seq cannot resolve cell-specific transcriptional changes due to tissue heterogeneity.
“Despite these advances, bulk RNA-Seq cannot resolve cell-specific transcriptional changes due to tissue heterogeneity122,124.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [124]
The Methods sections on RNA Sample Preparation and RNA Sequencing describe extracting RNA from frontal-cortex tissue and measuring gene expression, but do not discuss whether bulk RNA-seq can resolve cell-specific changes. Searches for cell types and tissue heterogeneity found no passage making that claim; the mention of cellular heterogeneity appears only in a reference title about DNA methylation. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Alzheimer's disease subtypes identified from ROS/MAP differ in lipid metabolism and synaptic function.
“These subtypes differ in lipid metabolism and synaptic function, highlighting the utility of combining molecular and clinical data to capture disease heterogeneity124,255,396,397.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [124]
The Background & Summary describes ROSMAP as a source of clinical and multi-omic data and notes heterogeneity in Alzheimer’s disease, but does not identify subtypes or compare their molecular functions. Searches for “lipid metabolism,” “synaptic function,” and subtype-related terms found no passage reporting the claimed differences. Page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCross-referencing the mouse amyloid-associated module with ROS/MAP data revealed that LAPTM5, ITGAM/CD11b and LILRB4 clustered together with many GWAS risk genes for Alzheimer's disease.
“Cross referencing our network with the data from the ROS/MAP project (Bennett et al., 2012a, b; De Jager et al., 2018), revealed that LAPTM5, ITGAM/CD11b and LILRB4 clustered together with many of the GWAS risk genes for Alzheimer’s disease”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as De Jager et al., 2018
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INPP5D regulates inflammasome activation in human microglia
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Self-citationCa2+ Dyshomeostasis Disrupts Neuronal and Synaptic Function in Alzheimer’s Disease
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Sex specific molecular networks and key drivers of Alzheimer’s disease
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Self-citationGenetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology
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Self-citationTREM2 splice isoforms generate soluble TREM2 species that disrupt long-term potentiation
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Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways
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Self-citationINPP5D/SHIP1 regulates inflammasome activation in human microglia
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- Authors
- De Jager, P. L., Ma, Y., McCabe, C., Xu, J., Vardarajan, B. N., Felsky, D., Klein, H.-U., White, C. C., Peters, M. A., Lodgson, B., Nejad, P., Tang, A., Mangravite, L. M., Yu, L., Gaiteri, C., Mostafavi, S., Schneider, J. A., & Bennett, D. A.
- Affiliations
- Columbia University Irving Medical Center (US)
- Broad Institute (US)
- Sage Bionetworks (US)
- Rush University Medical Center (US)
- University of British Columbia (CA)
- Corresponding author
- Bennett, David A.
- ORCID
- Venue
- Scientific Data
- Published
- 2018-08-07
- Volume
- 5
- Issue
- 1
- Pages
- 180142-180142
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC6080491
- OpenAlex
- W2950886245
- Cited by
- 598
- References
- 48
- Citations by year
- 2026: 69 · 2025: 83 · 2024: 125 · 2023: 76 · 2022: 82 · 2021: 74 · 2020: 58 · 2019: 23 · 2018: 4 · 2013: 1 · 2012: 1
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Broad Institute
- National Institutes of Health (K25 AG04190601, R01AG03-6836, AG016574, AG10161, AG15819)
- National Institute on Aging (R01AG017917, K25AG041906, R01AG030146, R01AG042210, R01AG018023, R01AG036836, U01AG046152, R01AG011101, K08AG034290, R01AG036042, R01AG043617, R01AG015819, P30AG010161, RC2AG036547, R01AG032990, RF1AG015819, RF1AG036042, P50AG016574)
- National Institute of Environmental Health Sciences (U01ES017155)
- National Center for Research Resources (KL2RR024151)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- dorsolateral prefrontal cortex, Alzheimer's disease, Religious Orders Study, Rush Memory and Aging Project, DNA methylation, H3K9 acetylation, chromatin accessibility, transcriptome, microRNA, neuropathology
- MeSH terms
- Aged, Aged, 80 and over, Aging, Alzheimer Disease, Female, Frontal Lobe, Humans, Male, Genome, Human, Sequence Analysis, RNA, DNA Methylation, Proteomics, Chromatin Immunoprecipitation, Metabolomics, Metabolome
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