Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples

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 和疾病其应用范围从鉴定细胞类型的空间组织到细胞周期的失调。 与疾病相关的空间依赖性基因表达。技术的进步，如 空间分辨转录组学（SRT）为研究这些问题提供了丰富的数据。此外，委员会认为， SRT结合长读段RNA测序的进展，可以实现诸如识别 健康和疾病状态（如癌症或其他疾病）中的空间依赖性剪接变异和等位基因特异性 神经退行性疾病最近的SRT研究正在生成跨多个样本（不同 捐赠者或邻近组织切片），但研究人员独立分析样本，因为缺乏 多样本数据集的计算工具。相反，当样品一起联合分析时， 增加了统计功效以更高的准确度和精确度检测差异。缺乏工具， 使用多个样本分析SRT数据是一个重大的知识差距，限制了改进 可以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学数据，从而更好地了解 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， 用于空间和长时间读取技术数据的软件，具有多个样本和实验条件， 准确地（1）预测多个样品中组织的空间域，（2）识别空间基因差异 跨实验条件或生物组的表达，每组中有多个样品，和（3） 鉴定跨空间域或实验条件的差异剪接变异。 拟议工作的理由是，开发的计算工具将使实质性的进展 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， 个体这一建议的重要性是巨大的，对越来越多的研究人员产生了广泛的影响。 这些成像和基因组数据，如大规模的联盟产生的空间地图集跨越多个 样本，而且所提出的方法将与各种各样的科学学科有关， 空间环境中的高维数据，例如环境和移动的健康。该项目建立在我的 过去在开发可扩展集群的计算方法和开源软件方面的经验， 在单细胞水平上鉴定基因表达的差异。创建有据可查的、开源的 软件扩展了这项工作的影响，以其他研究人员旨在了解的空间景观， 基因在各种疾病中的表达。

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.