Datasets

Data availability

Data obtained from the AD Knowledge Portal. The results published here are in whole or in part based on data obtained from the AD Knowledge Portal.

The Religious Orders Study and Memory and Aging Project (ROSMAP) Study. The data available in the AD Knowledge Portal would not be possible without the participation of research volunteers and the contribution of data by collaborating researchers. Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36836 (RNAseq), U01AG46161 (TMT proteomics), U01AG61356 (ROSMAP AMP-AD), P30AG072975, and the Illinois Department of Public Health (ROSMAP). Additional phenotypic data can be requested at www.radc.rush.edu.

ROSMAP TMT Proteomics. Study data were provided through the Accelerating Medicine Partnership for AD (U01AG046161 and U01AG061357) based on samples provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute.

MIT ROSMAP Single-Nucleus Multiomics. Study data were generated from postmortem brain tissue provided by the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) cohort at Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. This work was supported in part by the Cure Alzheimer’s Fund, NIH grants AG058002, AG062377, NS110453, NS115064, AG062335, AG074003, NS127187, MH119509, HG008155 (M.K.), RF1AG062377, RF1 AG054321, RO1 AG054012 (L.-H.T.), and the NIH training grant GM087237 (to C.A.B.). ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, and U01AG61356.

Bulk brain RNA-seq (syn12104384) and brain miRNA array data (syn3387325) from ROSMAP¹; brain proteomics from the Emory cohorts (syn20824864)^2,3; cerebrospinal fluid proteomics (syn21441787)⁴; brain proteomics across aging from the Johns Hopkins cohort (syn21441775)⁴; brain parenchyma snRNA-seq from ROSMAP (syn52383412; http://compbio.mit.edu/ad_multiregion/)⁵; and paired single-nucleus ATAC-seq and RNA-seq from ROSMAP (syn52293417; http://compbio.mit.edu/ad_epigenome/)⁶ are available via the AD Knowledge Portal (https://adknowledgeportal.org). The AD Knowledge Portal is a platform for accessing data, analyses, and tools generated by the Accelerating Medicines Partnership (AMP-AD) Target Discovery Program and other National Institute on Aging (NIA)-supported programs to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data is available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.synapse.org/Data%20Access).

Data obtained from the Gene Expression Omnibus. snRNA-seq of human brain vasculature was obtained under accession GSE163577⁷ and scRNA-seq of cerebrospinal fluid immune cells under accession GSE200164⁸, both openly available without restriction at https://www.ncbi.nlm.nih.gov/geo/.

Data obtained from published supplementary material. The following processed datasets were obtained from the supplementary information of their original publications, which is openly available from the publisher in each case: neuropathology-associated and case-control differential splicing results (Supplementary Tables 2 and 8 of Raj et al.⁹); m6A RNA methylation changes (Supplementary Table 24 of Castro-Hernández et al.¹⁰); plasma proteomic profiles across age (Supplementary Table 16 of Lehallier et al.¹¹); plasma protein age associations (Supplementary Table 25 of Oh et al.¹²); brain phosphoproteomics (Supplementary Table 2 of Morshed et al.¹³); brain glycoproteomics (Supplementary Table 2 of Zhang et al., 2020¹⁴ and Zhang et al., 2024¹⁵); brain and plasma metabolomics (supplementary material of Kalecky et al.¹⁶); tau interactomes (Supplementary Tables 1, 3 and 5 of Tracy et al.¹⁷); PSEN1 and APP interactomes (Supplementary Table 1 of Hosp et al.¹⁸); γ-secretase substrates (Supplementary Table 1 of Hou et al.¹⁹); microRNA meta-analysis results of Takousis et al.²⁰); CRISPRi screen results (Tian et al.²¹, Leng et al.²², Dräger et al.²³, Parra Bravo et al.²⁴, Samelson et al.²⁵); and GWAS results (Kunkle et al.²⁶, Wightman et al.²⁷, Bellenguez et al.²⁸).

Databases and knowledge resources. Alzheimer's disease gene associations were retrieved from the Comparative Toxicogenomics Database (https://ctdbase.org, MeSH identifier D000544)²⁹ and from DisGeNET (https://www.disgenet.org, disease identifier C0002395)³⁰. GWAS results were retrieved from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas, MONDO identifier 0004975)³¹. MicroRNA target predictions were obtained from miRDB (https://mirdb.org)³² and TargetScan (https://www.targetscan.org)³³. Clinical trial and drug-target information was obtained from DrugBank (https://go.drugbank.com)³⁴, ClinicalTrials.gov (https://clinicaltrials.gov) and Alzforum (https://www.alzforum.org).

1 De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research. Sci Data 5, 180142, doi:10.1038/sdata.2018.142 (2018).

2 Higginbotham, L. et al. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease. Sci Adv 6, eaaz9360, doi:10.1126/sciadv.aaz9360 (2020).

3 Ping, L. et al. Global quantitative analysis of the human brain proteome in Alzheimer's and Parkinson's Disease. Sci Data 5, 180036, doi:10.1038/sdata.2018.36 (2018).

4 Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med 26, 769-780, doi:10.1038/s41591-020-0815-6 (2020).

5 Mathys, H. et al. Single-cell multiregion dissection of Alzheimer's disease. Nature 632, 858-868, doi:10.1038/s41586-024-07606-7 (2024).

6 Xiong, X. et al. Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion. Cell 186, 4422-4437 e4421, doi:10.1016/j.cell.2023.08.040 (2023).

7 Yang, A. C. et al. A human brain vascular atlas reveals diverse mediators of Alzheimer's risk. Nature 603, 885-892, doi:10.1038/s41586-021-04369-3 (2022).

8 Piehl, N. et al. Cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment. Cell 185, 5028-5039 e5013, doi:10.1016/j.cell.2022.11.019 (2022).

9 Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility. Nat Genet 50, 1584-1592, doi:10.1038/s41588-018-0238-1 (2018).

10 Castro-Hernandez, R. et al. Conserved reduction of m(6)A RNA modifications during aging and neurodegeneration is linked to changes in synaptic transcripts. Proc Natl Acad Sci U S A 120, e2204933120, doi:10.1073/pnas.2204933120 (2023).

11 Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25, 1843-1850, doi:10.1038/s41591-019-0673-2 (2019).

12 Oh, H. S. et al. Organ aging signatures in the plasma proteome track health and disease. Nature 624, 164-172, doi:10.1038/s41586-023-06802-1 (2023).

13 Morshed, N. et al. Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer's disease. Nat Aging 1, 550-565, doi:10.1038/s43587-021-00071-1 (2021).

14 Zhang, Q., Ma, C., Chin, L. S. & Li, L. Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer's disease. Sci Adv 6, doi:10.1126/sciadv.abc5802 (2020).

15 Zhang, Q., Ma, C., Chin, L. S., Pan, S. & Li, L. Human brain glycoform coregulation network and glycan modification alterations in Alzheimer's disease. Sci Adv 10, eadk6911, doi:10.1126/sciadv.adk6911 (2024).

16 Kalecky, K., German, D. C., Montillo, A. A. & Bottiglieri, T. Targeted Metabolomic Analysis in Alzheimer's Disease Plasma and Brain Tissue in Non-Hispanic Whites. J Alzheimers Dis 86, 1875-1895, doi:10.3233/JAD-215448 (2022).

17 Tracy, T. E. et al. Tau interactome maps synaptic and mitochondrial processes associated with neurodegeneration. Cell 185, 712-728 e714, doi:10.1016/j.cell.2021.12.041 (2022).

18 Hosp, F. et al. Quantitative interaction proteomics of neurodegenerative disease proteins. Cell Rep 11, 1134-1146, doi:10.1016/j.celrep.2015.04.030 (2015).

19 Hou, P. et al. The gamma-secretase substrate proteome and its role in cell signaling regulation. Mol Cell 83, 4106-4122 e4110, doi:10.1016/j.molcel.2023.10.029 (2023).

20 Takousis, P. et al. Differential expression of microRNAs in Alzheimer's disease brain, blood, and cerebrospinal fluid. Alzheimers Dement 15, 1468-1477, doi:10.1016/j.jalz.2019.06.4952 (2019).

21 Tian, R. et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat Neurosci 24, 1020-1034, doi:10.1038/s41593-021-00862-0 (2021).

22 Leng, K. et al. CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states. Nat Neurosci 25, 1528-1542, doi:10.1038/s41593-022-01180-9 (2022).

23 Drager, N. M. et al. A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states. Nat Neurosci 25, 1149-1162, doi:10.1038/s41593-022-01131-4 (2022).

24 Parra Bravo, C. et al. Human iPSC 4R tauopathy model uncovers modifiers of tau propagation. Cell 187, 2446-2464 e2422, doi:10.1016/j.cell.2024.03.015 (2024).

25 Samelson, A. J. et al. CRISPR screens in iPSC-derived neurons reveal principles of tau proteostasis. Cell 189, 1517-1534 e1519, doi:10.1016/j.cell.2025.12.038 (2026).

26 Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet 51, 414-430, doi:10.1038/s41588-019-0358-2 (2019).

27 Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet 53, 1276-1282, doi:10.1038/s41588-021-00921-z (2021).

28 Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet 54, 412-436, doi:10.1038/s41588-022-01024-z (2022).

29 Davis, A. P. et al. Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res 51, D1257-D1262, doi:10.1093/nar/gkac833 (2023).

30 Pinero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 48, D845-D855, doi:10.1093/nar/gkz1021 (2020).

31 Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res 51, D977-D985, doi:10.1093/nar/gkac1010 (2023).

32 Chen, Y. & Wang, X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48, D127-D131, doi:10.1093/nar/gkz757 (2020).

33 McGeary, S. E. et al. The biochemical basis of microRNA targeting efficacy. Science 366, doi:10.1126/science.aav1741 (2019).

34 Knox, C. et al. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res 52, D1265-D1275, doi:10.1093/nar/gkad976 (2024).