The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease
Open accessAbstract
Alzheimer’s disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.
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AI-driven multi-omics approaches are increasingly utilized to elucidate disease-associated mechanisms, evolving from early integrative pipelines into sophisticated deep learning frameworks.
“In particular, AI-driven multi-omics approaches are increasingly utilized to elucidate disease-associated mechanisms, evolving from early integrative pipelines into sophisticated deep learning frameworks353.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [353]
The Usage Notes (‘Use case 3: Gene and protein integrative network analysis’; page not provided) describe growing interest in integrative network analysis of multi-omics data to identify biological processes implicated in Alzheimer’s disease. They do not describe these approaches as AI-driven or trace an evolution from early pipelines to deep learning frameworks. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe MSBB cohort includes 915 samples from four brain regions: BM10, BM22, BM36, and BM44.
“The MSBB cohort includes 915 samples from four brain regions: the anterior prefrontal cortex (BM10), superior temporal gyrus (BM22), parahippocampal gyrus (BM36), and inferior frontal gyrus (BM44);”
From Comprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains (2025) · cites this paper as [38]
The Methods section, “Human brain tissue preparation” (page unavailable), identifies the four sampled regions as BM10, BM22, BM36 and BM44, with the corresponding anatomical names; it calls BM10 the frontal pole. Our copy does not substantiate the stated total of 915 samples, so the regional description is supported but the sample count is not verified. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDiscrepancies in RNA-seq library preparation methods, sequencing techniques, sequencing depth, and AD definition between the MSBB and Mayo cohorts could introduce biases in data interpretation.
“Discrepancies in RNA-seq library preparation methods, sequencing techniques, sequencing depth, and AD definition between the MSBB and Mayo cohorts could introduce biases in data interpretation (38, 39).”
From Comprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains (2025) · cites this paper as [38]
The Methods section describes MSBB RNA-seq library preparation and sequencing, and the Usage Notes describe how AD severity is characterized in MSBB. The cited paper does not establish differences from Mayo in those methods, sequencing depth, or AD definition, or say that such differences could bias interpretation. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe KND approach has been successfully employed to identify novel pathways and key driver genes of complex human diseases such as AD, Parkinson's disease, melanoma and gastric cancer, and the top drivers predicted for these diseases were experimentally validated in vitro or in vivo.
“This approach has been successfully employed to identify novel pathways and key driver genes of complex human diseases such as AD [22], Parkinson’s disease (PD) [43], melanoma [44] and gastric cancer [45] and notably the top drivers predicted for the abovementioned diseases were experimentally validated in vitro or in vivo.”
From Sex specific molecular networks and key drivers of Alzheimer’s disease (2023) · cites this paper as [22]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe two bulk RNA-seq studies used AD samples and controls from the same patient cohorts but took unique approaches for co-expression network analysis.
“These studies both used AD samples and controls from the same patient cohorts (Religious Orders Study and Memory and Aging Project, Mayo Clinic, Mount Sinai School of Medicine),64,65,66 but they took unique approaches for co-expression network analysis.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [66]
For human AD gene network analysis, multi-Omics data previously generated from a set of 364 well-characterized AD and control brains spanning the full spectrum of LOAD-related cognitive and neuropathological disease severities represented in the Mount Sinai Brain Bank AD cohort was used.
“For human AD gene network analysis, we used the multi-Omics data we previously generated from a set of 364 well-characterized AD and control brains spanning the full spectrum of LOAD-related cognitive and neuropathological disease severities represented in the Mount Sinai Brain Bank AD cohort.”
From Microglial <i>INPP5D</i> limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease (2023) · cites this paper as Wang et al.69
AMP-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 [39]
PLCG2 expression significantly predicts TREM2 expression in multiple brain regions in human brain bulk RNA-seq data from the MSBB.
“examining the human brain bulk RNA‐seq data collected by the MSBB, 46 this finding was recapitulated because PLCG2 expression significantly predicts TREM2 expression in multiple brain regions.”
From PLCG2 modulates TREM2 expression and signaling in response to Alzheimer's disease pathology (2025) · cites this paper as [46]
The authors' analyses of clinical Alzheimer's disease data confirm that widespread inflammation in 3xTg-AD males is not simply a consequence of transgenic mouse model development.
“Our analyses of clinical Alzheimer’s disease data confirm that widespread inflammation in 3xTg-AD males is not simply a consequence of transgenic mouse model development”
The authors' analyses of clinical data from patients with sporadic late onset Alzheimer's disease further validated the translational relevance of and lessened the limitations associated with the 3xTg-AD model in the context of this study.
“our analyses of clinical data from patients with sporadic late onset Alzheimer’s disease further validated the translational relevance of and lessened the limitations associated with the 3xTg-AD model in the context of this study.”
Plexin-B1 (PLXNB1) is a hub gene in a coregulated gene subnetwork underlying late-onset AD.
“This identified Plexin-B1 (gene symbol PLXNB1), an axon guidance receptor, as a hub in a coregulated gene subnetwork underlying late-onset AD (Fig. 1A).”
From Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease (2024) · cites this paper as [13]
The Usage Notes, under “Use case 3: Gene and protein integrative network analysis,” discuss how network analysis can identify hub regulators in AD, but do not identify Plexin-B1 as one. Searches for PLXNB1, Plexin-B1, and an axon-guidance hub found no passage in our copy establishing the specific claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredIn the female Alzheimer's parahippocampal gyrus, there is a female-specific primary disease phenotype associated with impaired synaptic function.
“We again observed a female-specific primary disease phenotype associated with impaired synaptic function”
From Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients (2024) · cites this paper as [39]
The Methods (Human brain tissue preparation; page unavailable) identify the parahippocampal gyrus as a region selected for profiling, and the study-population section reports female participants. Neither those passages nor the Usage Notes report a female-specific disease phenotype or impaired synaptic function; searches for those terms found no passage establishing the claimed finding. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe authors found overwhelming similarities between the mouse and human DEG data.
“We found overwhelming similarities between the mouse and human data.”
From Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients (2024) · cites this paper as [39]
The Usage Notes discuss differential expression in human Alzheimer’s disease brain samples, but do not compare mouse and human DEGs. Searches for mouse–human DEG similarities and cross-species comparisons found no passage reporting the claimed finding. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThe leading predicted upstream regulator of differential gene expression in the male Alzheimer's parahippocampal gyrus was LPS.
“the leading predicted upstream regulator of differential gene expression in the male Alzheimer’s parahippocampal gyrus was LPS”
From Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients (2024) · cites this paper as [39]
The Usage Notes section, “Use case 1: Differential gene and protein expression in relation to AD” (page unavailable), describes data that could be used to study region-specific expression, but reports no upstream-regulator ranking. Searches for LPS, lipopolysaccharide, and upstream regulator found no passage identifying LPS as the leading predicted regulator in male Alzheimer’s parahippocampal gyrus. (LLM, conf=0.75)
Experimental — accuracy not yet measuredGene expression analyses revealed significant upregulation of complement system genes in male and female Alzheimer's parahippocampal gyri.
“detailed gene expression analyses revealed significant upregulation of complement system genes in male and female Alzheimer’s parahippocampal gyri (Fig. 5h)”
From Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients (2024) · cites this paper as [39]
The Methods (Human brain tissue preparation; page unavailable) identify the parahippocampal gyrus as a profiled region, and Usage Notes (Use case 1; page unavailable) describe differential expression analysis as a possible use of the data. Neither passage reports significant upregulation of complement-system genes in male and female Alzheimer’s samples. A search for complement genes and sex-specific differential expression found no such result in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe top altered canonical pathway in the male Alzheimer's parahippocampal gyrus was neutrophil extracellular trap signaling.
“the top altered canonical pathway in the male Alzheimer’s parahippocampal gyrus was neutrophil extracellular trap signaling”
From Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients (2024) · cites this paper as [39]
The Methods section (Human brain tissue preparation; page not provided) identifies the parahippocampal gyrus as a region selected for profiling, but does not rank canonical pathways in male Alzheimer’s cases. Searches for “neutrophil extracellular trap signaling” and “male Alzheimer’s parahippocampal gyrus canonical pathway” found no passage in our copy reporting the claimed result. (LLM, conf=0.74)
Experimental — accuracy not yet measured
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“In addition, the other independent cohort (80 cases, in which 62 cases were used in the final dataset; Data S4A) was provided by the Alzheimer's Disease Research Center at Icahn School of Medicine at Mount Sinai, with well characterized criteria for clinical and pathological diagnoses (Wang et al., 2018).”
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Sex differences in brain protein expression and disease
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Self-citationTsai, A. P., Dong, C., Lin, P. B.-C., Messenger, E. J., Casali, B. T., Moutinho, M., Liu, Y., Oblak, A. L., Lamb, B. T., Landreth, G. E., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01022-0 · Cited by 92 · see citing works
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Self-citationThe Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease
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Ca2+ Dyshomeostasis Disrupts Neuronal and Synaptic Function in Alzheimer’s Disease
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“32,69 The cluster signatures and cluster-specific DEGs from the spatial transcriptomics analysis were intersected with the network neighborhood within a path length of up to six steps from INPP5D in the human AD BN.”
Self-citationStojakovic, A., Trushin, S., Sheu, A., Khalili, L., Chang, S.-Y., Li, X., Christensen, T., Salisbury, J. L., Geroux, R. E., Gateno, B., et al. (2021) · Communications Biology · doi:10.1038/s42003-020-01584-y · Cited by 72 · see citing works
The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease
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Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer’s disease
Eteleeb, A. M., Novotny, B. C., Tarraga, C. S., Sohn, C., Dhungel, E., Brase, L., Nallapu, A., Buss, J., Farias, F., Bergmann, K., et al. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002607 · Cited by 64 · see citing works
Impact of increased <i>APP</i> gene dose in Down syndrome and the Dp16 mouse model
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Sex specific molecular networks and key drivers of Alzheimer’s disease
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“To uncover new molecular players underlying AD pathophysiology, we have previously conducted multiscale gene network analyses of transcriptomic and genomic data of individuals with AD from the MSBB 12,13 .”
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“org/ hippocampus due to their relevance to LOAD neuropathology across independent human cohorts (ROS/MAP, Mount Sinai Brain Bank, and Mayo Clinic) and stratified patients into different molecular subtypes based on gene co-expression profiles using iterative WGCNA (Allen et al., 2016; De Jager et al., 2018; Wang et al.…”
BackgroundMethodPLCG2 modulates TREM2 expression and signaling in response to Alzheimer's disease pathology
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“46 In four different brain regions, simple linear regression analyses were used to test if PLCG2 expression levels could predict the expression of TREM2 .”
An interpretable deep learning framework identifies proteomic drivers of Alzheimer’s disease
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PLCG2 as a Risk Factor for Alzheimer's Disease.
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Comprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains
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“1) ( 38 , 39 ).”
InfluentialEstablishing the relationship between brain cellular senescence and brain structure
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Self-citationThe transcriptional and cellular landscape of cognitive resilience to Alzheimer’s disease
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Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease
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- Authors
- Wang, M., Beckmann, N. D., Roussos, P., Wang, E., Zhou, X., Wang, Q., Ming, C., Neff, R., Ma, W., Fullard, J. F., Hauberg, M. E., Bendl, J., Peters, M. A., Logsdon, B., Wang, P., Mahajan, M., Mangravite, L. M., Dammer, E. B., Duong, D. M., Lah, J. J., Seyfried, N. T., Levey, A. I., Buxbaum, J. D., Ehrlich, M., Gandy, S., Katsel, P., Haroutunian, V., Schadt, E., & Zhang, B.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- James J. Peters VA Medical Center (US)
- Aarhus University (DK)
- Sage Bionetworks (US)
- Emory University (US)
- Corresponding author
- Haroutunian, Vahram
- Schadt, Eric
- Zhang, Bin
- ORCID
- Wang, Minghui
- Beckmann, Noam D.
- Roussos, Panos
- Wang, Erming
- Zhou, Xianxiao
- Wang, Qian
- Neff, Ryan
- Ma, Weiping
- Fullard, John F.
- Hauberg, Mads E.
- Bendl, Jaroslav
- Peters, Mette A.
- Logsdon, Ben
- Wang, Pei
- Mangravite, Lara M.
- Dammer, Eric B.
- Duong, Duc M.
- Lah, James J.
- Seyfried, Nicholas T.
- Levey, Allan I.
- Buxbaum, Joseph D.
- Ehrlich, Michelle
- Gandy, Sam
- Katsel, Pavel
- Haroutunian, Vahram
- Schadt, Eric
- Zhang, Bin
- Venue
- Scientific Data
- Published
- 2018-09-11
- Volume
- 5
- Issue
- 1
- Pages
- 180185-180185
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC6132187
- OpenAlex
- W2889790826
- Cited by
- 502
- References
- 10
- Citations by year
- 2026: 51 · 2025: 72 · 2024: 92 · 2023: 65 · 2022: 62 · 2021: 75 · 2020: 56 · 2019: 27 · 2018: 1 · 2012: 1
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Scopus (Elsevier) (submittedVersion, other-oa)
- Funders
- Lundbeckfonden (R248‐2017‐2003)
- National Institutes of Health (1s10od018522-01, HHSN271201300031C)
- National Institute on Aging (R01AG057907, R01AG046170, RF1AG058469, U01AG046170, RF1AG054014, RF1AG057440, RF1AG059319, U01AG052411, R01AG050986)
- National Institute of Mental Health (HHSN271201300031C)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, multi-omics, transcriptomics, proteomics, genomics, mild cognitive impairment, postmortem brain, late-onset Alzheimer's disease, genetic susceptibility loci
- MeSH terms
- Aged, 80 and over, Alzheimer Disease, Humans, Cohort Studies, Genetic Predisposition to Disease, Proteome, Genomics, Proteomics, Genome-Wide Association Study, Transcriptome, Cognitive Dysfunction
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