Single cell and spatial genomic analyses of specimens from patients with autoimmune diseases (Technology Core)

为了更好地了解驱动自身免疫性疾病的分子和细胞途径，我们建议 深入剖析来自患有类风湿性关节炎（RA）、银屑病（Ps）、银屑病性关节炎 (PsA)、原发性干燥综合征（pSS）和系统性红斑狼疮（SLE）。密切配合 AMP AIM网络，我们将专注于临床医生网络定义的队列和临床问题， 生物学家和计算生物学家以及工业和非营利合作伙伴。为我们的技术 核心，我们选择了领先的多维技术，以深入剖析最终器官以及 自身免疫患者的外周血。我们组建了一个调查小组， 疾病和组织类型，并且已经证明有能力开发和实施高- 用于组织和血液样本分析的输送管道。为了帮助我们设计和解释研究，我们 招募了一组合作者和顾问，他们对每种疾病都有临床专业知识， 在每一种组织方面都有专门知识，在空间分布方法方面有技术专门知识。第一年，我们将 结束试验阶段，以优化管道：i）保存和解聚组织; ii）分析单细胞 使用scRNA-seq/CITE-seq/TCR-BCR-seq; iii）通过snRNA-seq/snATAC-seq分析单个核; iv）制备 两个互补的，领先的空间转录组学（Visium）和转录方法的组织 成像（MERFISH）。我们将改进方案，以优化细胞活力和产量，技术质量指标 特定于每种技术和所有细胞类型的表示。在第二年，我们将运行全套规模- 使用每种组织类型约50个样本的技术管道，并将评估数据质量、研究中心和批次效应 和技术工件，以通知对单细胞流水线的潜在修改。在第3-5年，我们将分析 其余约1000个活组织检查和约1000个血液样本由疾病小组收集。我们将开发一个 用于数据预处理、主要生物学分析和质量指标（包括 基因、细胞和组织尺度上的技术和生物学参数），每个参数具有定制的特征 组织和疾病。我们的调查结果将在我们的技术核心和整个 疾病团队网络，系统生物学核心，监督项目的网络委员会，NIH，FNIH 以及我们的行业和非营利合作伙伴。我们还将在真实的时间作出反应， 技术，并与网络合作，试点和扩大关键方法。建议的结果 研究将是一组多维数据集，将与网络和更大的 社区，并为前沿疾病解构和重建提供基础， 自身免疫性终末器官病理学，从而实现AMP-AIM的愿景。

To better understand the molecular and cellular pathways driving autoimmune diseases, we propose to deeply profile biological samples from patients with rheumatoid arthritis (RA), psoriasis (Ps), psoriatic arthritis (PsA), primary Sjögren’s syndrome (pSS) and systemic lupus erythematosus (SLE). Working closely with the AMP AIM network, we will focus on the cohorts and clinical questions defined by the network of clinicians, biologists and computational biologists together with industry and non-profit partners. For our Technology Core, we selected leading-edge multi-dimensional technologies to deeply profile end organs as well as peripheral blood from patients with autoimmunity. We assembled a team of investigators with expertise in each disease and tissue type, and who have already demonstrated the ability to develop and implement high- throughput pipelines to profile tissue and blood samples. To help us design and interpret the studies, we recruited a team of collaborators and consultants with clinical expertise in each disease, with pathology expertise in each tissue and with technical expertise in spatial profiling methods. In the first year, we will carry out the pilot phase to optimize pipelines for: i) preserving and disaggregating tissues; ii) profiling single cells using scRNA-seq/CITE-seq/TCR-BCR-seq; iii) profiling single nuclei by snRNA-seq/snATAC-seq; iv) preparing tissues for two complementary, leading-edge methods for spatial transcriptomics (Visium) and transcript imaging (MERFISH). We will iterate the protocols to optimize cell viability and yield, technical quality metrics specific to each technology and the representation of all cell types. In year 2, we will run the full set of scaled- up technology pipelines using ~50 samples per tissue type, and will assess data quality, site and batch effects and technical artifacts to inform potential modifications for the single-cell pipeline. In Years 3-5, we will profile the remaining ~1000 biopsies and ~1000 blood samples collected by the disease teams. We will develop a computational pipeline for data pre-processing, primary biological analysis and quality metrics (including technical and biological parameters at the scale of genes, cells and tissues) with customized features for each tissue and disease. Our findings will be rapidly communicated within our Technology Core and across the Network to the Disease Teams, Systems Biology Core, network committees overseeing the project, NIH, FNIH and our industry and non-profit partners. We will also respond in real time to advancing changes in technologies and work with the network to pilot and scale up critical methods. The result of the proposed studies will be a set of multi-dimensional datasets that will be shared with the network and the larger community, and provide a basis for cutting edge disease deconstruction and reconstruction across autoimmune end organ pathologies and thus fulfill the vision of AMP-AIM.