Multi-omic genetic regulatory signatures underlying tissue complexity of diabetes in the pancreas at single-cell spatial resolution

单细胞空间分辨率下胰腺糖尿病组织复杂性的多组学遗传调控特征

• 批准号: 10684817
• 负责人: Marcela Brissova
• 金额: $ 65.22万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-17 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684817
• 关键词: Acinar Cell; Adult; Age; Alpha Cell; American; Architecture; Beta Cell; Biological; Biology; Cell Differentiation process; Cell Nucleus; Cells; Chromatin; Complex; Computer Analysis; Data; Data Analyses; Development; Diabetes Mellitus; Diagnosis; Dimensions; Disease; Ductal Epithelial Cell; Elements; Endocrine; Enhancers; Environmental Risk Factor; Ethnic Population; Genes; Genetic; Genetic Predisposition to Disease; Genetic study; Genomic approach; Genomics; Genotype; Glucose; Growth; Heritability; Heterogeneity; Hormone secretion; Human; Image; In Situ; Individual; Inflammation; Insulin; Interdisciplinary Study; Islets of Langerhans; Knowledge; Location; Machine Learning; Macrophage; Maps; Measures; Medical Care Costs; Molecular; Molecular Analysis; Natural regeneration; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Organ; Pancreas; Pattern; Physiological; Physiology; Play; Prediabetes syndrome; Process; Proteomics; Quantitative Trait Loci; RNA; Regulator Genes; Regulatory Element; Research; Resolution; Resources; Role; Signal Transduction; Single Nucleotide Polymorphism; Specificity; Technology; Text; Tissue imaging; Tissues; Transcriptional Regulation; Translating; Untranslated RNA; Weight; XCL1 gene; cell type; centralized portal; data sharing; diabetes risk; epigenomics; functional genomics; genetic signature; genome wide association study; in situ imaging; insight; islet; multi-ethnic; multimodality; multiple omics; personalized therapeutic; sex; tool; trait; transcriptome; transcriptomics

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Diabetes is a complex disease that results from the cumulative temporal effects of genetic and environmental factors. A hallmark of both common and rare forms of diabetes is genetic dysregulation of insulin-producing β cells which reside in islets of Langerhans and are scattered throughout the larger context of the entire pancreas. Although genome wide association studies (GWAS) of type 2 diabetes (T2D), the most common form of diabetes, identified >600 statistically independent signals, they are difficult to translate into biological mechanisms because of their predominant noncoding location. Therefore, we propose to map the context specificity of T2D and related trait GWAS signals in pancreas across multiple dimensions: cell type, age, sex, developmental stage, and genetic background. Our proposal is based on our exciting single-cell multi-omic spatially-resolved pilot data and the well-supported idea that noncoding GWAS signals percolate upwards through complex and hierarchical molecular networks that influence cellular circuits. The initial layer in this hierarchy is chromatin organization, which propagates genetic predisposition onto subsequent molecular layers, likely starting with the transcriptome. Because islet physiology is influenced by the identity and spatial arrangement of surrounding cells within the pancreas, our multimodal molecular analyses will focus on four major pancreatic lineages; endocrine α and β cells, acinar, and ductal cells. In addition, we will incorporate the analysis of tissue resident macrophages because they play an important role in several processes including islet/pancreatic cell differentiation, growth, regeneration, and inflammation. The proposed studies will establish associations between regulatory elements, genes, cell types, tissue organization, and physiological function. Our multidisciplinary research team with complementary expertise in pancreas and islet biology, sequencing technologies, single cell genomics and epigenomics, image data analysis, and machine learning devised a suite of tools and analyses to discover cell state dynamic changes across diverse conditions, and how these changes influence downstream biology from transcriptional regulation, to cellular spatial organization within the pancreas, and finally to tissue-level physiology. This approach, if successful, will enable mechanistic insights across GWAS loci, which can inform new personalized therapeutic strategies.

在此输入您的申请的新摘要信息的文本。此部分的文本长度不得超过 30 行。 糖尿病是一种复杂的疾病，是由遗传和环境因素的累积时间效应引起的，常见和罕见糖尿病的一个特点是产生胰岛素的 β 细胞的遗传失调，这些细胞存在于胰岛中并分散在更大的范围内。尽管 2 型糖尿病 (T2D)（最常见的糖尿病形式）的全基因组关联研究 (GWAS) 发现了超过 600 个统计上独立的信号，但由于它们占主导地位的非编码，因此很难转化为生物学机制。因此，我们建议在多个维度上绘制胰腺中 T2D 和相关性状 GWAS 信号的背景特异性：细胞类型、年龄、性别、发育阶段和遗传背景。组学空间解析的先导数据和得到充分支持的观点，即非编码 GWAS 信号通过影响细胞回路的复杂且分层的分子网络向上渗透，该层次结构中的初始层是染色质组织，它传播遗传。由于胰岛生理学受到胰腺内周围细胞的特性和空间排列的影响，因此我们的多模式分子分析将重点关注四种主要的胰腺α和β细胞谱系；此外，我们还将对组织驻留巨噬细胞进行分析，因为它们在胰岛/胰腺细胞分化、生长、再生和炎症等多个过程中发挥着重要作用。我们的多学科研究团队在胰腺和胰岛生物学、测序技术、单细胞基因组学和表观基因组学、图像数据分析和机器学习方面具有互补的专业知识，设计了一套工具和分析来发现不同条件下细胞状态的动态变化，以及这些变化如何影响下游生物学，从转录调控到胰腺内的细胞空间组织，最后到组织水平的生理学。如果成功，这种方法将能够获得机械见解。穿过GWAS 位点，可以为新的个性化治疗策略提供信息。