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

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.

项目概要/摘要 了解组织中基因表达的空间景观是人类健康的一个基本问题 应用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性基因表达。 空间分辨转录组学（SRT）提供了大量数据来研究这些问题。此外， SRT 与长读长 RNA 测序技术的进步相结合，可实现诸如识别等应用 健康和疾病状态下的空间依赖性剪接变异和等位基因特异性，例如癌症或 最近的 SRT 研究正在生成多个样本（不同样本）的数据集。 供体或邻近的组织切片），但研究人员独立分析样本，因为缺乏 相比之下，当样本被一起分析时， 统计能力得到提高，可以更准确和精确地检测差异。 分析具有多个样本的 SRT 数据是一个重大的知识差距，限制了改进模型的能力 可作为预防和治疗目标的疾病的分子原因和后果。 我的研究项目为生物医学数据开发可扩展的计算方法和开源软件 分析，特别是单细胞和空间转录组数据，从而提高对 在这里，我们的目标是专注于开发可扩展的计算方法和 软件用于处理来自空间和长读技术的数据，具有多个样本和实验条件 准确地 (1) 预测多个样本中组织的空间域，(2) 识别空间基因的差异 跨实验条件或每组有多个样本的生物组的表达，以及 (3) 识别跨空间域或实验条件的差异剪接变异。 拟议工作的基本原理是开发的计算工具将带来实质性进展 在我们对从细胞到组织的不同尺度上基因表达的空间景观的理解中 该提案的意义重大，对越来越多使用的研究人员产生广泛影响。 这些成像和基因组数据，例如跨多个领域生成空间地图集的大规模联盟 样本，而且所提出的方法将与利用 空间背景下的高维数据，例如环境和移动健康该项目建立在我的基础上。 过去在可扩展集群的计算方法和开源软件方面的开发经验 识别单细胞水平上基因表达的差异 创建有据可查的开源文件。 软件将这项工作的影响扩展到其他旨在了解空间景观的研究人员 各种疾病环境中的基因表达。