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Generating spatial and functional maps of cell-to-cell interactions in MS lesions

生成多发性硬化症病变中细胞间相互作用的空间和功能图

基本信息

批准号：
10579301
负责人：
David Pitt
金额：
$ 59.93万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-15 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10579301
关键词：
Accounting Acute Antibodies Astrocytes Atlases Biological Cell Communication Cell Nucleus Cells Chronic Complex Computing Methodologies Data Data Analyses Data Set Demyelinations Disease Environment Evaluation Exhibits Frequencies Gene Expression Profile Genetic Transcription Heterogeneity Histology Image Imaging Techniques In Situ In Vitro Indirect Immunofluorescence Inflammation Inflammatory Lesion Ligands Lymphocyte Maps Microglia Multiple Sclerosis Multiple Sclerosis Lesions Myeloid Cells Neurons Nuclear Oligodendroglia Organ Pathologic Pathology Population Population Heterogeneity Preparation Protocols documentation Recovery Relapse Resolution Resources Sampling Signal Transduction Sorting Spatial Distribution Structure Techniques Testing Tissues cell preparation cell type computerized tools data visualization denoising genomic platform histological image histological stains imaging approach imaging modality immune cell infiltrate improved innovation insight multiplexed imaging novel strategies oligodendrocyte precursor population based precursor cell receptor receptor expression remyelination single nucleus RNA-sequencing therapeutic target tissue repair tool transcriptome transcriptomics white matter

项目摘要

Generating spatial and functional maps of cell-to-cell interactions in MS lesions Single nucleus RNA sequencing (sNuc-Seq) studies have greatly advanced our understanding of cellular heterogeneity in multiple sclerosis (MS) lesions. However, current CNS sNuc-Seq protocols selectively under-sample astrocyte and microglia nuclei, resulting in low resolution of population heterogeneity. Moreover, sNuc-Seq does not capture spatial information, such as distribution and interactions of cell populations within the MS lesion environment. Here, we propose to use a novel approach to sNuc-Seq of MS lesion tissue, enrichment for unrepresented/rare nuclei such as astrocytes, microglia and infiltrating immune cells through Flow sorting. We will combine this approach with advanced computational tools for denoising, imputation and batch correction to improve recovery of biological signal and resolution of cellular heterogeneity in acutely demyelinating, chronic active and remyelinating MS lesions. We will leverage the subpopulation-specific transcriptomes to computationally predict possible interactions between cell populations, based on expression of receptor-ligand pairs. We will further use a highly multiplexed histology imaging approach to localize cellular subpopulations and confirm co-localization of receptor-ligand pairs in MS lesions. We will finally conduct in vitro studies to elucidate the functional impact of selected receptor-ligand interactions. Our study will identify the constituent subpopulations and receptor-ligand interactomes that are associated with acute demyelination, chronic low-grade inflammation and remyelination. These interactions or combinations of interactions can be targeted to reduce inflammation or to enhance neurorepair in MS. Moreover, our datasets will provide a rich resource that will guide more functional studies on disease mechanisms.

生成MS病变中细胞间相互作用的空间和功能图单核RNA测序（sNuc-Seq）研究极大地促进了我们对多发性硬化（MS）病变中的细胞异质性。然而，目前的CNS sNuc-Seq 方案选择性地对星形胶质细胞和小胶质细胞核进行采样不足，导致低分辨率的种群异质性此外，sNuc-Seq不捕获空间信息，诸如 MS病变环境中细胞群的分布和相互作用。在这里，我们提出使用一种新的方法来进行MS病变组织的sNuc-Seq，富集MS病变组织的sNuc-Seq。未代表/罕见的细胞核，如星形胶质细胞、小胶质细胞和浸润性免疫细胞，通过Flow 分类我们将联合收割机与先进的计算工具相结合，用于改善生物信号恢复和细胞信号分辨率的插补和批量校正急性脱髓鞘、慢性活动性和髓鞘再生MS病变的异质性。我们将利用亚群特异性转录组来计算预测细胞群体之间的相互作用，基于受体-配体对的表达。我们将进一步使用高度多重组织学成像方法定位细胞亚群并证实受体-配体对在MS病变中的共定位。我们最终将在体外进行研究阐明选定的受体-配体相互作用的功能影响。我们的研究将确定组成亚群和受体配体相互作用组，与急性脱髓鞘、慢性低度炎症和髓鞘再生相关。这些相互作用或相互作用的组合可以靶向减少炎症或增强炎症反应。此外，我们的数据集将提供丰富的资源，疾病机制的功能研究。