Dissecting autoimmune cellular and molecular networks in vitiligo

翻译后摘要：白癜风是一种毁容器官特异性自身免疫性疾病，影响世界人口的1%， 包括美国的300万人。目前还没有FDA批准的治疗白癜风的药物， 目前的一线治疗具有边际功效。 白癜风的危害有哪些？为什么它的进展速度因患者而异？这些 是一个知之甚少的问题。在这个项目中，我们试图提供分子机制， 这些基本问题。我们的范式转变假设是，炎症和炎症之间的平衡， 直接在皮肤内的致耐受性途径决定了损伤的位置及其进展速率。 因此，皮肤的局部变化是白癜风进展的核心。 我们建议使用技术进步来测试和扩展我们的假设，直到现在才使这成为可能。 首先，我们开发了一种抽吸起泡方法，该方法提供了对组织浸润免疫细胞的稳健采样 而不会混淆炎症和水泡本身。其次，我们建立了单细胞RNA-Seq （scRNA-Seq）方法，并已成功地将其应用于水泡液样品。第三，我们建议 开发gridRNA-Seq，我们使用激光显微切割白癜风病变的切口活检， 每个包含10-20个细胞的小立方体组织，代表三维组织结构。然后我们 使用低输入RNA-Seq方案来分析每个立方体的表达水平。 这三种技术的结合使我们处于一个独特的位置，从根本上改变我们的方法 研究白癜风。使用scRNA-Seq，我们将发现白癜风病变内存在的细胞类型，并编译基因。 这些细胞的表达特征。使用配体/受体分析，我们将建立细胞信号传导 支持疾病进展模型的网络。通过比较病变与非病变水疱样本， 将识别白癜风中的异常信号，特别是致耐受性和炎症信号的差异 病变和非病变皮肤之间的关系。虽然功能强大，scRNA-Seq序列是一种细胞混合物， 细胞丢失了。为了解决这一限制，我们将开发gridRNA-Seq来分析10-20个细胞的“区块”， 从皮肤上一个已知的、划定的区域。网格中的每个块都有一个很好的表征表型， 由组织块位置处的组织学外观确定。通过整合细胞类型特异性 签名与来自gridRNA-Seq的批量RNA-Seq数据，我们建议“去卷积”这种混合表达数据 以估计每个块的细胞组成以及其表达。我们将使用这些信息， 将特异性表达和细胞类型组成变化与疾病表型相关联。总的来说，我们的 数据将直接评估单个细胞的确切致耐受性和炎症平衡及其相关性 与疾病表型，因此都测试我们原来的假设，以及提供一个公正的看法， 白癜风的发病机制在个体细胞水平。

Abstract: Vitiligo is a disfiguring organ-specific autoimmune disease that affects 1% of the world population, including 3 million individuals in the U.S. alone. There are no FDA-approved medical treatments for vitiligo, and current first-line therapies have marginal efficacy. What controls the spreading of vitiligo? Why is its rate of progression different from patient to patient? These are poorly understood problems. In this project we seek to provide the molecular mechanism that explains these fundamental questions. Our paradigm-shifting hypothesis is that the balance between inflammatory and tolerogenic pathways directly within the skin determines the location of lesions and their rate of progression. Thus, local changes in skin are at the core of vitiligo progression. We propose to test and expand our hypothesis using technological advances that only now make this possible. First, we developed a suction blistering method that provides robust sampling of tissue-infiltrating immune cells without confounding inflammation from blistering itself. Second, we have established a single cell RNA-Seq (scRNA-Seq) methodology and have successfully applied it to blister fluid samples. Third, we propose to develop gridRNA-Seq where we use laser microdissection of incisional biopsies of vitiligo lesions into grids of small cubes of tissue containing 10-20 cells each, representing the three dimensional tissue structure. We then use a low input RNA-Seq protocol to profile the expression level of each cube. The combination of these three technologies put us in a unique position to fundamentally change our approach to study vitiligo. With scRNA-Seq we will find the cell types present within vitiligo lesions and compile gene expression signatures for each of these cells. Using ligand/receptor analysis we will build cell signaling networks to support models of disease progression. By comparing lesional with nonlesional blister samples we will identify aberrant signaling in vitiligo, and in particular how tolerogenic and inflammatory signals differ between lesions and nonlesional skin. Although powerful, scRNA-Seq sequences a cell mixture, and the origin of the cells is lost. To address this limitation, we will develop gridRNA-Seq to profile a “block” of 10-20 cells from a known, demarcated region in the skin. Each block in the grid has a well-characterized phenotype as established by the histological appearance at the location of the block. By integrating cell type specific signatures with bulk RNA-Seq data from gridRNA-Seq we propose to “deconvolve” this mixed expression data to estimate the cell composition of each block as well as its expression. We will then use this information to correlate specific expression and cell type composition changes with disease phenotype. Taken together, our data will directly assess the exact tolerogenic and inflammatory balance of individual cells and its association with disease phenotype and hence both test our original hypothesis as well as provide an unbiased view of vitiligo pathogenesis at the individual cell level.