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.

在MS病变中生成细胞间相互作用的空间和功能图单核RNA测序（SNUC-SEQ）研究大大提出了我们对多发性硬化症（MS）病变中的细胞异质性。但是，当前的CNS snuc-seq 协议选择性地样本的星形胶质细胞和小胶质细胞核，导致低分辨率的分辨率低人口异质性。此外，snuc-seq不会捕获空间信息，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来缝制MS病变组织的障碍，富集通过流动的无代表性/稀有核，例如星形胶质细胞，小胶质细胞和浸润的免疫细胞排序。我们将将这种方法与用于降级的高级计算工具相结合，插定和批化校正以改善生物学信号的恢复和细胞分辨率急性脱髓鞘，慢性活性和再髓病变的异质性。我们将利用亚群特异性转录组来预测可能基于受体配体对的表达，细胞种群之间的相互作用。我们将进一步使用高度多重的组织学成像方法来定位细胞亚群并确认MS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。我们的研究将确定成分的亚群和受体配体相互作用体是与急性脱髓鞘，慢性低度炎症和透明相关。这些可以针对相互作用或相互作用的组合来减少炎症或增强 MS中的神经台。此外，我们的数据集将提供丰富的资源，以指导更多疾病机制的功能研究。