Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples

项目摘要/摘要 了解组织中基因表达的空间格局是关系到人类健康的一个基本问题。 和疾病。应用范围从识别细胞类型的空间组织到细胞类型的失调 与疾病相关的空间相关基因表达。技术进步，例如 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， SRT与长读RNA测序的进步相结合，使诸如识别 健康和疾病状态下的空间相关剪接变异和等位基因fi城市，如癌症或 神经退行性疾病。最近的SRT研究正在跨多个样本(不同)生成数据集 供体或邻近组织切片)，但研究人员独立分析样本，因为没有 包含多个样本的数据集的计算工具。相比之下，当样本被联合分析在一起时， 增加了统计能力，以更高的准确性和精确度检测差异。缺乏工具来 用多个样本分析SRT数据是一个显著的知识空白，因为它限制了fifi的能力 可用于预防和治疗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了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数据，有助于更好地理解 人类的健康和疾病。在这里，我们的目标是专注于开发可伸缩的计算方法和 空间和长读技术的数据软件，具有多个样本和实验条件 准确地(1)跨多个样本预测组织的空间域，(2)识别空间基因的差异 跨实验条件或生物组的表达，每组有多个样本，以及(3) 确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，所开发的计算工具将使实质性的进展成为可能 在我们对从细胞到组织的不同尺度上基因表达的空间格局的理解中 个人。这一建议的意义是巨大的，对越来越多的研究人员使用fi 这些成像和基因组数据，如大型联合体生成的跨多个 样例，而且所建议的方法也将与利用 空间环境中的高维数据，例如环境和移动健康。这个项目是建立在我的 过去开发计算方法和开源软件以实现可伸缩的集群和 在单细胞水平上识别基因表达的差异。创建文档齐全、开放源代码的 软件将这项工作的影响扩大到其他旨在了解 基因在各种疾病环境中的表达。

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.