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[Preprint]. 2024 Apr 20:2024.04.16.589592.
doi: 10.1101/2024.04.16.589592.

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

Affiliations

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

Joseph S Reddy et al. bioRxiv. .

Update in

  • Bridging the gap: Multi-omics profiling of brain tissue in Alzheimer's disease and older controls in multi-ethnic populations.
    Reddy JS, Heath L, Linden AV, Allen M, Lopes KP, Seifar F, Wang E, Ma Y, Poehlman WL, Quicksall ZS, Runnels A, Wang Y, Duong DM, Yin L, Xu K, Modeste ES, Shantaraman A, Dammer EB, Ping L, Oatman SR, Scanlan J, Ho C, Carrasquillo MM, Atik M, Yepez G, Mitchell AO, Nguyen TT, Chen X, Marquez DX, Reddy H, Xiao H, Seshadri S, Mayeux R, Prokop S, Lee EB, Serrano GE, Beach TG, Teich AF, Haroutunian V, Fox EJ, Gearing M, Wingo A, Wingo T, Lah JJ, Levey AI, Dickson DW, Barnes LL, De Jager P, Zhang B, Bennett D, Seyfried NT, Greenwood AK, Ertekin-Taner N. Reddy JS, et al. Alzheimers Dement. 2024 Oct;20(10):7174-7192. doi: 10.1002/alz.14208. Epub 2024 Aug 30. Alzheimers Dement. 2024. PMID: 39215503 Free PMC article.

Abstract

Introduction: Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD.

Methods: To bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors.

Results: We generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures.

Discussion: Inclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

Keywords: Alzheimer’s disease; data descriptor; multi-omics; precision medicine; proteome; transcriptome; whole genome sequencing.

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Conflict of interest statement

Conflict of interest statement The authors declare no conflicts of interest. Author disclosures are available in the supporting information.

Figures

Figure 1.
Figure 1.. Profiled brain regions.
Approximate location of tissue in brain regions sampled for molecular profiling, including RNAseq, WGS, and proteomics. Tissue from the dorsolateral prefrontal cortex (Brodmann areas 8, 9, and/or 46) and caudate nucleus were contributed by all sites, including Mayo Clinic, Mt. Sinai, Columbia, Rush, and Emory. In contrast, tissue from superior temporal gyrus (Brodmann 22) was provided by all sites except Columbia, which had only the temporal pole available for this lobe.
Figure 2.
Figure 2.. Data types by tissue, site, and individual race and ethnicity.
Bar graph depicting the number of samples profiled by each assay (whole genome sequencing, RNAseq or TMT proteomics). Whole genome sequencing data was generated for 626 donors from various contributing sites (an additional 411 donors had WGS from AMP-AD 1.0 efforts, not shown here). Similarly, 2,140 unique transcriptomics profiles from RNAseq of caudate nucleus (n=602), dorsolateral prefrontal cortex (n=779), superior temporal gyrus (716) and temporal pole (n=43) from 844 donors were generated. Samples sent to other sites for the swap study are not included. A lone superior temporal gyrus RNAseq sample from Columbia was also not included in this summary. 1240 unique TMT-proteomes from dorsolateral prefrontal cortex (n=996) and superior temporal gyrus (n=244) were generated from 1,015 donors. These include the 284 samples from the AMP-AD 1.0 efforts to balance batches, as described in methods. Pie charts on the right show the number of donors profiled by ethnoracial categories (AA=African America, NHW=non-Hispanic White, LA=Latino American, and Other). These categories were defined as follows: donors whose race was encoded as “Black or African American” and ethnicity as ‘isHispanic=FALSE’ in the individual metadata were treated as ‘AA’. Those with race encoded as White and ethnicity as ‘isHispanic=FALSE’ were categorized as ‘NHW’. Remaining donors, for whom ethnicity was encoded as ‘isHispanic=TRUE’ were treated as ‘LA’. All remaining donors from various other races were encoded as ‘Other’.
Figure 3.
Figure 3.. RNAseq sample swaps.
To evaluate the technical variability of RNA sequencing amongst the three sites, RNA tissue from the same brain was sequenced at each site for a small number of samples. The number and region of samples exchanged are illustrated with the grayscale brain image with the exchanged tissue highlighted in color (DLPFC in blue, STG in green). Straight arrows represent tissue exchange; circular arrows represent tissue sequenced at the original site, shown in blue, green, and red circular arrows for Mayo Clinic, Rush, and NYGC, respectively. Samples from MSSM (4 DLPFC, 4 STG) and Columbia (5 DLPFC) were utilized for the swap experiment at NYGC.

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