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：
10365983
负责人：
David Pitt
金额：
$ 59.1万
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/10365983
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 - Cell Movement Spatial Distribution Structure Techniques Testing Tissues Tumor-infiltrating immune cells base cell preparation cell type computerized tools data visualization denoising genomic platform histological image histological stains imaging approach imaging modality improved innovation insight multiplexed imaging novel strategies oligodendrocyte precursor population based precursor cell receptor receptor expression remyelination therapeutic target tissue repair tool transcriptome transcriptome sequencing 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.

生成多发性硬化症病变中细胞间相互作用的空间和功能图单核 RNA 测序 (sNuc-Seq) 研究极大地增进了我们对多发性硬化症（MS）病变中的细胞异质性。然而，目前的 CNS sNuc-Seq 协议选择性地对星形胶质细胞和小胶质细胞核进行采样不足，导致分辨率低人口异质性。此外，sNuc-Seq 不捕获空间信息，例如 MS 病变环境中细胞群的分布和相互作用。在这里，我们建议使用一种新的方法对 MS 病变组织进行 sNuc-Seq，富集通过 Flow 检测未代表性/稀有的细胞核，例如星形胶质细胞、小胶质细胞和浸润性免疫细胞排序。我们将把这种方法与先进的去噪计算工具结合起来，插补和批量校正，以提高生物信号的恢复和细胞的分辨率急性脱髓鞘、慢性活动性和髓鞘再生多发性硬化症病变的异质性。我们将利用亚群特异性转录组通过计算预测可能的情况基于受体-配体对的表达，细胞群之间的相互作用。我们将进一步使用高度多重组织学成像方法来定位细胞亚群并确认多发性硬化症病变中受体-配体对的共定位。我们最终将在体外进行研究阐明所选受体-配体相互作用的功能影响。我们的研究将确定构成亚群和受体-配体相互作用组与急性脱髓鞘、慢性低度炎症和髓鞘再生有关。这些相互作用或相互作用的组合可以有针对性地减少炎症或增强多发性硬化症的神经修复。此外，我们的数据集将提供丰富的资源，指导更多疾病机制的功能研究。