Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples

新兴的多样本空间分辨转录组学的计算方法

• 批准号: 10711312
• 负责人: Stephanie Carinne Hicks
• 金额: $ 40.45万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10711312
• 关键词: Address; Alleles; Atlases; Biological; Cells; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Diagnosis; Discipline; Disease; Environmental Health; Experimental Designs; Face; Gene Expression; Goals; Health; Human; Image; Individual; Knowledge; Malignant Neoplasms; Methods; Molecular; Neurodegenerative Disorders; Prevention; Prognosis; RNA Splicing; Research; Research Personnel; Sampling; Specificity; Technology; Tissues; Variant; Work; cell type; complex data; computerized tools; experience; experimental group; genomic data; improved; mHealth; multidimensional data; open source; precision medicine; programs; tool; transcriptome sequencing; transcriptomics; treatment response; Address; Alleles; Atlases; Biological; Cells; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Diagnosis; Discipline; Disease; Environmental Health; Experimental Designs; Face; Gene Expression; Goals; Health; Human; Image; Individual; Knowledge; Malignant Neoplasms; Methods; Molecular; Neurodegenerative Disorders; Prevention; Prognosis; RNA Splicing; Research; Research Personnel; Sampling; Specificity; Technology; Tissues; Variant; Work; cell type; complex data; computerized tools; experience; experimental group; genomic data; improved; mHealth; multidimensional data; open source; precision medicine; programs; tool; transcriptome sequencing; transcriptomics; treatment response

Project Summary/Abstract Understanding the spatial landscape of gene expression in tissues is a fundamental question for human health and disease. Applications range from identifying the spatial organization of cell types to dysregulation of spatial-dependent gene expression associated with disease. Advances in technologies, such as spatially-resolved transcriptomics (SRT), provide a wealth of data to investigate these questions. Furthermore, SRT combined with advances in long-read RNA-sequencing enable applications such as identifying spatial-dependent splicing variation and allele specificity in healthy and disease states, such as cancer or neurodegenerative disorders. Recent SRT studies are generating datasets across multiple samples (different donors or adjacent tissue sections), but researchers analyze samples independently because there lack computational tools for datasets with multiple samples. In contrast, when samples are jointly analyzed together, the statistical power is increased to detect differences with greater accuracy and precision. The lack of tools to analyze SRT data with multiple samples is a significant knowledge gap that limits are ability to refine the molecular causes and consequences of diseases that can be targeted for prevention and treatment. My research program develops scalable computational methods and open-source software for biomedical data analysis, in particular single-cell and spatial transcriptomics data, leading to an improved understanding of human health and disease. Here, our goal is to focus on developing scalable computational methods and software for data from spatial and long-read technologies with multiple samples and experimental conditions to accurately (1) predict spatial domains of tissues across multiple samples, (2) identify differences in spatial gene expression across experimental conditions or biological groups with multiple samples in each group, and (3) identify differential splicing variation across spatial domains or experimental conditions. The rationale for the proposed work is that the computational tools developed will enable substantial advances in our understanding of the spatial landscape of gene expression on distinct scales from cells to tissues to individuals. The significance of this proposal is substantial with broad impact for researchers increasingly using these imaging and genomic data, such as large-scale consortia generating spatial atlases across multiple samples, but also the proposed methods will be relevant to a wide variety of scientific disciplines that leverage high-dimensional data in a spatial context, such as environmental and mobile health. The project builds on my past experience in developing computational methods and open-source software for scalable clustering and identifying differences in gene expression at the single-cell level. The creation of well-documented, open-source software expands the impact of this work to other researchers aiming to understand the spatial landscape of gene expression in a variety of disease settings.

项目摘要/摘要 了解组织中基因表达的空间景观是人类健康的基本问题 和疾病。应用程序范围从识别细胞类型的空间组织到失调 与疾病相关的空间依赖基因表达。技术的进步，例如 空间分辨的转录组学（SRT）提供了大量数据来研究这些问题。此外， SRT结合了长阅读RNA-seter-sever启用应用程序的进步，例如识别 在健康和疾病状态（例如癌症）或 神经退行性疾病。最近的SRT研究正在跨多个样本生成数据集（不同 捐赠者或相邻组织部分），但是研究人员独立分析样本，因为缺乏 具有多个样本的数据集的计算工具。相反，当将样品共同分析时， 统计能力增加以检测差异，以更高的精度和精度。缺乏工具来 用多个样本分析SRT数据是一个显着的知识差距，限制可以重新确定能力 可以针对预防和治疗的疾病的分子原因和后果。 我的研究计划开发了可扩展的计算方法和开源软件，用于生物医学数据 分析，尤其是单细胞和空间转录组数据，从而提高了对 人类健康和疾病。在这里，我们的目标是专注于开发可扩展的计算方法和 来自具有多个样本和实验条件的空间和长阅读技术的数据软件 准确（1）预测多个样品中组织的空间结构域，（2）确定空间基因的差异 每组中有多个样品的实验条件或生物组的表达，（3） 确定跨空间结构域或实验条件之间的微分剪接变化。 拟议工作的理由是，开发的计算工具将实现重大进步 在我们理解基因表达的空间景观中，从细胞到组织的不同尺度上 个人。该提案的重要性是巨大的，对研究人员越来越多地使用 这些成像和基因组数据，例如大型联盟，在多个 样本，但所提出的方法将与多种利用的科学学科有关 在空间环境中的高维数据，例如环境和移动健康。该项目建立在我的 过去开发计算方法和开源软件的经验，用于可扩展聚类和 识别单细胞水平上基因表达的差异。创建有据可查的开源 软件将这项工作的影响扩展到其他研究人员，以了解 各种疾病环境中的基因表达。