Alzheimer’s disease (AD) is the most prevalent cause of dementia. While there is no effective treatment for AD, a growing body of evidence points to passive immunotherapy with monoclonal antibodies against amyloid beta (Aβ) as a promising therapeutic strategy. Meningeal lymphatic drainage plays an important role in Aβ accumulation in the brain, yet it is unknown if or how modulating meningeal lymphatic function can influence the outcome of anti-Aβ immunotherapy in AD. Analysis of the meninges of middle-aged AD transgenic 5xFAD mice revealed an accelerated deterioration of lymphatic vasculature. Ablation of meningeal lymphatic vessels in adult 5xFAD mice exacerbated Aβ deposition, microgliosis and affected neurovascular activation, increasing the amount of Aβ load and aggravating behavioral deficits following passive immunotherapy. On the contrary, therapeutic delivery of vascular endothelial growth factor C improved Aβ clearance by monoclonal antibodies and tuned hippocampal function in aged AD transgenic mice. Furthermore, we present a set of AD-associated genes that are highly expressed by meningeal lymphatic endothelial cells and correlate with altered levels of Aβ42 in the cerebrospinal fluid as well. We sought to investigate the microglial pathways affected in AD in human brains. To do so, we generated single-nuclei RNA-seq (snRNA-seq) from the parietal cortex from the Knight ADRC. We generated snRNAseq (10X chemistry v3) for AD, presymptomatic (ATN+ with cognition intact) and controls with none or neglectable AD pathology. After data cleaning and quality control,11,166 microglia nuclei remained for clustering and downstream analyses. A significant overlap was found between the gene signature of microglia from 5xFAD mice with dysfunctional meningeal lymphatics and the transcriptional profile of activated microglia from the human AD brain. Overall, our data demonstrates that impaired meningeal lymphatic drainage impacts the microglial inflammatory response in AD and that enhancement of meningeal lymphatics, alone or combined with potential passive immunotherapies, could lead to better clinical outcomes.
Single nuclei RNA-seq (10X chemistry v3) was generated from the parietal lobe as described in https://pubmed.ncbi.nlm.nih.gov/33911285/. Brain autopsied samples from Knight-ADRC. After data cleaning and quality control, 11,166 microglia nuclei remained for downstream analyses.
The study is published in the manuscript “Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy”, By Da Mesquita et al, Nature 2021 (https://pubmed.ncbi.nlm.nih.gov/33911285/).
1. The first release (June 1, 2021) includes sample meta data, study-wide unique barcodes of nuclei, gene expression data, and sample consent information.
2. The second release (November 23, 2021) has added single-nuclei RNA-seq sequence files, code files, genotypes for EQTL analysis, and phenotype files.
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|Microglia expression profiles in AD||snd10021||Single Cell RNA Sequencing||n = 44|
|Microglia expression profiles in AD||fsa000007||NG00108.v2||Microglia profile, subject, and phenotype data|
|Microglia expression profiles in AD - GWAS||fsa000015||NG00108.v2||GWAS data|
|Microglia expression profiles in AD - Single-nuclei RNA-seq Sequence||fsa000016||NG00108.v2||Single-nuclei RNA-seq Sequence data|
View the File Manifest for a full list of files released in this dataset.
The microglia profile of the Parietal lobe of 44 donors from the Knight ADRC were sequenced using 10X genomics 3’ Chemistry v3 (10,000 nuclei per donor, 50,000 reads per nuclei), and aligned to the precursor mRNA reference (build GRCh38).
|Sample Set||Accession||Number of Subjects|
|Microglia expression profiles in AD||snd10021||n = 44|
|Consent Level||Number of Subjects|
|DS-ADRD-IRB-PUB||n = 44|
Visit the Data Use Limitations page for definitions of the consent levels above.
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 2, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studiesNon-Technical Research Use Statement:Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.
- Investigator:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Approval:June 8, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have conﬁrmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.Non-Technical Research Use Statement:We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially signiﬁcant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.
- Investigator:Pendergrass, RionInstitution:GenentechProject Title:Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studiesDate of Approval:July 16, 2021Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The purpose of our study is to identify novel genetic factors associated with Alzheimer’s Disease, corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP). This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. We will also evaluate genetic associations with sub-phenotypes individuals have within these broad disease categories, such as their Braak staging results which provide insights into the level of severity of Alzheimer’s. Thus we are requesting access to the set of genomic Whole Exome and Whole Genome Sequences (WES and WGS) have just been released through the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (DSS NIAGADS). The findings from our genetic association testing have the potential for identification of new therapeutic targets for Alzheimer's Disease, CBD, and PSP. The findings from our studies also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways. We will use standard genetic epidemiological methods to handle the WGS and WES data. We will also analyze cell type-specific expression differences in AD to identify biomarkers and disease pathways using standard gene expression analysis methods currently in use.All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the DSS NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.Non-Technical Research Use Statement:Genetic variation and gene expression data allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype, disease traits, gene expression, and outcomes, through these datasets will allow us to pursue important new findings for disease treatment.
- Investigator:Yang, JingjingInstitution:Emory UniversityProject Title:Novel Bayesian methods for integrating transcriptomic data in GWASDate of Approval:February 16, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The objective of the proposed project is to derive novel Bayesian methods to integrate multi-omics data in genome-wide association studies (GWAS) for studying complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. First, we will model the expression quantitative trait loci (eQTL) and other molecular QTL information in GWAS by an adapted Bayesian variable selection model, such that the model can quantify the enrichment of associated genetic variants with respect to each annotation such as eQTL and prioritize genetic variants that are of the enriched annotation. Second, we will be conducting transcriptome-wide association studies (TWAS) by a Bayesian approach to identify potentially causal genes. Third, we will use our Bayesian GWAS results to evaluate a Bayesian polygenic risk score for the complex phenotype of interest.We will first learn molecular QTL information by using external transcriptomics data set such as GTEx V8 and external molecular QTL from TCGA, and then integrate this information with the whole genome sequence data from ADSP to prioritize genetic variants associated with complex phenotypes of interest and conduct TWAS to identify risk genes. We are interested in studying all complex phenotypes that were profiled for the ADSP samples, especially Alzheimer’s disease (AD) and AD-related complex phenotypes. Especially, our lab has access to the ROS/MAP multi-omics data shared by the Rush Alzheimer’s disease center (http://www.radc.rush.edu/). All samples in the ROS/MAP study are well-characterized with extensive complex phenotypes profiled, including clinical diagnosis of AD, AD-related complex phenotypes, and psychological phenotypes. We will combine the whole genome sequence data from both ADSP and ROS/MAP samples to increase the total sample size in our study, thus improving the mapping power.The purpose of using ADSP data is to increase the sample size for testing our derived methods for functional genetic association studies of complex phenotypes. We are not limited to studying AD only. We are flexible to study any complex phenotypes that are profiled for both ADSP and ROS/MAP samples.Non-Technical Research Use Statement:This proposed project is to develop novel Bayesian methods to integrate multi-omics data such as transcriptomic in genome-wide association studies (GWAS) of complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. i) We will model molecular quantitative trait loci information in GWAS, such that the model can quantify the enrichment for associated genetic variants with respect to each annotation and prioritize genetic variants that are of the enriched annotation. ii) We will derive a novel Bayesian model to use the eQTL effect-sizes as weights to conduct gene-based association tests. iii) We will use the Bayesian results from the proposed two methods to calculate Bayesian polygenic risk scores. We propose to test our proposed methods on the applied genomic analysis data and ROS/MAP multi-omics data to study complex phenotypes that are profiled for both ADSP and ROS/MAP samples, including AD, AD-related pathology traits, and related psychological disorders.
Acknowledgment statement for any data distributed by NIAGADS:
Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.
Use the study-specific acknowledgement statements below (as applicable):
For investigators using any data from this dataset:
Please cite/reference the use of NIAGADS data by including the accession NG00108.
For investigators using KnightADRC (sa000008) data:
This work was supported by grants from the National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine
Da Mesquita, Sandro et al. “Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy.” Nature vol. 593,7858 (2021): 255-260. doi:10.1038/s41586-021-03489-0
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