Identifying shared and distinct cell type vulnerability across neurological diseases.
识别神经系统疾病中共有和不同的细胞类型脆弱性。
基本信息
- 批准号:10370858
- 负责人:
- 金额:$ 44.55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-02-01 至 2024-01-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAlzheimer&aposs DiseaseAlzheimer&aposs disease diagnosisAreaAutopsyBrainBrain regionCell NucleusCellsClinicalDataData SetDiseaseFoundationsFrontotemporal DementiaGene ExpressionGene Expression ProfileGene ProteinsGenerationsGoalsHuntington DiseaseIndividualInvestigationKnowledgeLightLinkMolecularNerve DegenerationNeurodegenerative DisordersOverlapping GenesParkinson DiseasePathologicPathologyProteomicsPublishingResearchResearch PersonnelResolutionResourcesSignal TransductionSmall Nuclear RNASpecific qualifier valueStandardizationTechniquesTissuesbrain tissuecell typedata sharingdata visualizationfrontotemporal lobar dementia-amyotrophic lateral sclerosisgenome wide association studygenome-wideinsightnervous system disorderresponsetranscriptome sequencingtranscriptomics
项目摘要
PROJECT SUMMARY
A central question in neurodegeneration research is to identify type-specific changes associated with the onset
and progression of neurological disease. Currently, studying alterations in cell type composition, signatures, and
function in response to pathology are areas of active investigation in Alzheimer's Disease (AD), Parkinson's
Disease (PD), Huntington's Disease (HD), and Amyotrophic Lateral Sclerosis-Frontotemporal Dementia (ALS-
FTD) research. For polygenic diseases such as AD, PD, and ALS-FTD, genome-wide association, bulk tissue
transcriptomics, and bulk proteomics studies have highlighted candidate loci, genes, and proteins with significant
associations to pathological and clinical manifestations of diseases. However, these efforts are focused
separately on each disease; thus, the major gap is a lack of studies that systematically examine tissue from the
same brain regions from individuals with different neurodegenerative diseases. Here, we aim to contextualize
disease-specific bulk tissue findings using cutting-edge techniques at single-cell resolution to characterize
commonalities and differences in cell type-specific changes across different neurodegenerative diseases. Such
cross-disease investigation is likely to shed light on each disease individually, by linking specific cell type
changes to pathologies that are hallmarks for a particular neurodegenerative disease. In parallel with data
generation and analysis, we also aim to create a standardized, cross-disease interactive portal for investigators
to explore not only cell type-specific signatures that are associated with individual neurodegenerative diseases
like AD, but also signatures that are shared between AD and other diseases. This approach will not only further
our understanding of AD-specific cell type vulnerability, but also distinguish between specific and general
signatures of neurodegeneration in multiple other diseases. Thus, our overall goals are not only to create a
foundational data set to refine our understanding of cell type vulnerability and alteration within AD and across
other neurodegenerative diseases, but also to establish a robust framework to allow external researchers to
derive new disease-specific insights into cell type composition changes in neurodegeneration.
项目概要
神经退行性变研究的一个中心问题是识别与发病相关的类型特异性变化
和神经系统疾病的进展。目前,研究细胞类型组成、特征和
病理反应的功能是阿尔茨海默病 (AD)、帕金森病的积极研究领域
疾病 (PD)、亨廷顿舞蹈病 (HD) 和肌萎缩侧索硬化症-额颞叶痴呆 (ALS-
FTD)研究。对于 AD、PD 和 ALS-FTD 等多基因疾病,全基因组关联、大块组织
转录组学和大量蛋白质组学研究强调了候选位点、基因和蛋白质,具有显着的意义
与疾病的病理和临床表现的关联。然而,这些努力的重点是
分别针对每种疾病;因此,主要的差距是缺乏系统地检查组织的研究
来自患有不同神经退行性疾病的个体的相同大脑区域。在这里,我们的目标是结合实际情况
使用单细胞分辨率的尖端技术来表征疾病特异性的大块组织发现
不同神经退行性疾病的细胞类型特异性变化的共性和差异。这样的
通过将特定的细胞类型联系起来,跨疾病研究可能会单独阐明每种疾病
作为特定神经退行性疾病标志的病理变化。与数据并行
生成和分析,我们还旨在为研究人员创建一个标准化的跨疾病互动门户
不仅探索与个体神经退行性疾病相关的细胞类型特异性特征
像 AD 一样,但也有 AD 和其他疾病之间共有的特征。这一做法不仅将进一步
我们对 AD 特定细胞类型脆弱性的理解,但也区分特定和一般
多种其他疾病中神经退行性变的特征。因此,我们的总体目标不仅仅是创建一个
基础数据集,以加深我们对 AD 内和跨细胞类型脆弱性和改变的理解
其他神经退行性疾病,同时还建立了一个强大的框架,让外部研究人员能够
获得关于神经退行性变中细胞类型组成变化的新的疾病特异性见解。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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