Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Sep 16:18:1443865.
doi: 10.3389/fninf.2024.1443865. eCollection 2024.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences

Alejandra P Pérez-González  1   2   3 , Aidee Lashmi García-Kroepfly  1 , Keila Adonai Pérez-Fuentes  1 , Roberto Isaac García-Reyes  1 , Fryda Fernanda Solis-Roldan  1 , Jennifer Alejandra Alba-González  1 , Enrique Hernández-Lemus  1   4 , Guillermo de Anda-Jáuregui  1   4   5
Affiliations
Review

The ROSMAP project: aging and neurodegenerative diseases through omic sciences

Alejandra P Pérez-González et al. Front Neuroinform. .

Abstract

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Keywords: Alzheimer's disease; Religious Order Study Memory and Aging Project; aging; genomics; metabolomics; omics; proteomics; transcriptomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Methodological convergences in the ROSMAP cohort omic data. (Top) Venn Diagram showcasing the percentage of convergence in omic analysis made to ROSMAP participants. (Bottom) Upsetplot describing the specific technologies used for the ROSMAP database, the number of samples for each technology and the convergence and divergence of participants in one or more technologies.
Figure 2
Figure 2
Number of articles published annually from 2007 to 2024, classified by different omics technologies within a specific cohort. The categories of omics technologies include genomics, transcriptomics, epigenetics, proteomics, metabolomics, and multi-omics. A notable growth in the use of multi-omics approaches in recent years is highlighted, reflected by an increase in the number of integrative studies spanning multiple omics disciplines. This increase suggests a trend toward the adoption of more integrated and multidimensional analyses in biomedical research.
Figure 3
Figure 3
Genomic data metrics in ROSMAP. A subset of the ROSMAP samples (n = 1,200, representing 1,179 unique deceased participants) underwent whole genome sequencing (WGS). DNA was extracted from brain tissue (n = 806), whole blood (n = 389), or EBV-transformed lymphocytes (n = 5). The WGS libraries were prepared and sequenced on an Illumina HiSeq X sequencer (v2.5 chemistry) using 2 × 150 bp cycles. Variants were annotated with population frequencies from established variant databases, including dbSNP, 1,000 Genomes, and the Exome Aggregation Consortium (ExAC) (De Jager et al., 2018). Only 1,196 bam and bai files are available in https://www.synapse.org/Synapse:syn20068543. Also, the WGS Harmonization study is available in https://www.synapse.org/Synapse:syn22264775. For genotyping, the majority of samples were genotyped on the Affymetrix GeneChip 6.0 platform at the Broad Institute's Center for Genotyping (n = 1,204) or the Translational Genomics Research Institute (n = 674). Additionally, 566 participants were genotyped on the Illumina OmniQuad Express platform at Children's Hospital of Philadelphia (De Jager et al., 2018). SNP Array data can be accessed via https://doi.org/10.7303/syn3157325.
Figure 4
Figure 4
Transcriptomic data metrics in ROSMAP. For RNA-seq, sequencing was carried out using the Illumina HiSeq2000 with 101 bp paired end reads for a targeted coverage of 5 0M paired reads. Fastq files were re-aligned to the GENCODE24 (GRCh38) reference genome using STAR with twopassMode set as Basic. The RNA samples used to generate the RNAseq data were also submitted to the Broad Institute's Genomics Platform for processing on the Nanostring nCounter platform to generate miRNA profiles for 800 miRNAs using the Human V2 miRNA codeset (De Jager et al., 2018). RNA-seq data can be accessed via https://www.synapse.org/Synapse:syn26720676. For sc-RNA-seq, libraries were prepared using the Chromium Single Cell 3′ Reagent Kits v2 and the generated libraries were sequenced using NextSeq 500/550 High Output v2 kits (150 cycles). Gene counts were obtained by aligning reads to the hg38 genome (Mathys et al., 2019).
Figure 5
Figure 5
Distribution of individuals with RNA-seq sequencing data by brain tissue in ROSMAP. The Dorsolateral Prefrontal Cortex (DLPFC) is the most extensively studied region, with 1,141 specimens, followed by the Head of the Caudate Nucleus (HCN) (749 samples), the Posterior Cingulate Cortex (PCC) with 671 samples, the Temporal Cortex (TC) with 125 samples, and the Frontal Cortex (FC) with 123 samples in more than 60,607 features.
Figure 6
Figure 6
Proteomic data metrics in ROSMAP. LC-SRM and TMT-MS proteomics was performed using frozen tissue from dorsolateral prefrontal cortex (DLPFC). In LC-SRM, the abundance of endogenous peptides was quantified as a ratio to spiked-in synthetic peptides containing stable heavy isotopes. For TMT, MS2 spectra were searched against the UniProtKB human proteome database containing both Swiss-Prot and TrEMBL human reference protein sequences (90,411 target sequences), plus 245 contaminant proteins. Both TMT quantitation and LC-SRM data can be accessed via https://www.synapse.org/Synapse:syn17008935.
Figure 7
Figure 7
Epigenetic data metrics in ROSMAP. For H3K9Ac ChIP-Seq, the Millipore anti-H3K9Ac mAb was used for chromatin immunoprecipitation experiment can be found in Synapse:syn4896408. H3K9Ac ChIP-Seq Methylation data can be found in Synapse:syn3157275.
Figure 8
Figure 8
Metabolomics data metrics for ROSMAP. The Biocrates AbsoluteIDQ p180 platform (Biocrates AG, Innsbruck, Austria) was used for this Biocrates p180 assay. It is a multiplexed targeted metabolomic assay covering 188 metabolites, including hexoses, amino acids, biogenic amines, acylcarnitines, glycerophospholipids and sphinoglipids. An ultra-performance liquid chromatography couple to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA) was used to quantitate bile acids. Metabolon assay methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. Metabolomics data can be accessed via https://www.synapse.org/Synapse:syn10235592.
Figure 9
Figure 9
Each of the omics layers provides a distinctive set of data that, when integrated and analyzed together, allows a deep understanding of biological processes. This integrated approach facilitates the identification of new interactions between different omics levels, the discovery of endophenotypes at multiple levels, the elucidation of complex traits, and the study of interactomics. Thus, multi-omics analyses provide a holistic and comprehensive view of brain biology, opening new avenues for biomedical research.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Abdelnour C., Agosta F., Bozzali M., Fougère B., Iwata A., Nilforooshan R., et al. . (2022). Perspectives and challenges in patient stratification in Alzheimer's disease. Alzheimers Res. Ther. 14:112. 10.1186/s13195-022-01055-y - DOI - PMC - PubMed
    1. Akgun B., Hamilton-Nelson K. L., Kushch N. A., Adams L. D., Starks T. D., Martinez I., et al. . (2022). African-ancestry based polygenic risk scores improve Alzheimer disease risk prediction in individuals of African Ancestry. Alzheimers Dement. 18:e067457. 10.1002/alz.067457 - DOI - PubMed
    1. Al-Amrani S., Al-Jabri Z., Al-Zaabi A., Alshekaili J., Al-Khabori M. (2021). Proteomics: concepts and applications in human medicine. World J. Biol. Chem. 12, 57–69. 10.4331/wjbc.v12.i5.57 - DOI - PMC - PubMed
    1. Allen M., Carrasquillo M. M., Funk C., Heavner B. D., Zou F., Younkin C. S., et al. . (2016). Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases. Sci. Data 3:160089. 10.1038/sdata.2016.89 - DOI - PMC - PubMed
    1. Andrade-Guerrero J., Santiago-Balmaseda A., Jeronimo-Aguilar P., Vargas-Rodríguez I., Cadena-Suárez A. R., Sánchez-Garibay C., et al. . (2023). Alzheimer's disease: an updated overview of its genetics. Int. J. Mol. Sci. 24:3754. 10.3390/ijms24043754 - DOI - PMC - PubMed

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AP-G is a doctoral student from Programa de Doctorado en Ciencias Biomédicas (PDCB), Universidad Nacional Autónoma de México (UNAM), and received fellowship 904078 from CONAHCYT. 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; genomics and RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation, ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNAseq), R01AG48015 (monocyte RNAseq) RF1AG57473 (single nucleus RNAseq), U01AG32984 (genomic and whole exome sequencing), U01AG46152 (ROSMAP AMP-AD, targeted proteomics), U01AG46161(TMT proteomics), U01AG61356 (whole genome sequencing, targeted proteomics, ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP), and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at http://www.radc.rush.edu.

LinkOut - more resources