Analytical tools for studying the tumor microenvironment leveraging spatial transcriptomics

利用空间转录组学研究肿瘤微环境的分析工具

• 批准号: 10524921
• 负责人: Brooke L Fridley
• 金额: $ 41.57万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524921
• 关键词: Architecture; Automobile Driving; Basic Science; Bioconductor; Bioinformatics; Biological; Biological Assay; Cancer Center; Cancer Patient; Cancer Prognosis; Cancer Science; Cells; Clinical; Clinical Sciences; Collaborations; Colorado; Communication; Computer software; Computing Methodologies; Cytometry; DNA Sequence Alteration; Data; Data Scientist; Development; Ensure; Epigenetic Process; Future; Gene Expression; Genomics; Goals; Heterogeneity; Image; Immunofluorescence Immunologic; Immunotherapy; Individual; Infiltration; Location; Malignant Neoplasms; Manuscripts; Measurement; Measures; Methodology; Methods; Modality; Neoplasm Metastasis; Online Systems; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Process; RNA; Research; Research Personnel; Research Project Grants; Resolution; Sampling; Slice; Software Tools; Spottings; Statistical Methods; Technology; Tissue Fixation; Tissue Sample; Tissues; Transcript; Translating; Translational Research; Tumor-infiltrating immune cells; Universities; Visualization; analytical method; analytical tool; base; cell type; commercialization; computerized tools; data integration; data structure; design; experimental study; falls; high dimensionality; immune function; innovation; insight; interest; multidimensional data; novel; patient response; personalized medicine; phenotypic data; prognostication; response; single-cell RNA sequencing; software development; spatial integration; statistics; tool; transcriptome; transcriptome sequencing; transcriptomics; treatment response; tumor; tumor heterogeneity; tumor microenvironment; tumorigenesis; usability; user friendly software; web app

PROJECT SUMMARY/ABSTRACT Understanding the tumor microenvironment (TME) heterogeneity and architecture are key to stratify cancer patients responsive to immunotherapy and clinical outcome. Single-cell RNA sequencing (scRNAseq) is a powerful tool for studying the TME at the single-cell level, however, spatial information between single cells was not preserved in this technology, which is vital in studying the TME. In contrast, use of spatially resolved transcriptomics holds the promise in the understanding of the spatial contexture of the TME because of its power to capture the location of individual cells within the larger tissue architecture. Recently, commercialization of spatial transcriptomic (ST) technologies have allowed researchers to study at an unprecedented level the spatial architecture of the TME. Similar to other “omics” technologies, novel computational tools are urgently needed to decipher and infer biological meaning for these high-dimensional ST data. In addition to the need for methods for analyzing and visualizing ST data in its current form, there is the need to develop methods for the future state of ST, whereby the level of cellular resolution is vastly decreasing to the single cell level. Moreover, the application of multiple assays to one tissue sample is also producing multiple modalities measured on the same sample (e.g., scRNAseq, image-based cytometry methods such as multiplex immunofluorescence) which requires effective methods for data integration. Lastly, many ST studies are completed on multiple samples simultaneous with the goal of correlating TME features with clinical outcome, thus requiring computational tools to unravel the impact of the spatial architecture of the TME on clinical response. In the proposed research, we will tackle these challenges by implementing state-of- the-art statistical and computational methods that account for and leverage the spatial information present in ST data to understanding the TME. We will develop innovative methods for assessing the TME’s composition (Aim 1) and studying co-localization and spatial heterogeneity (Aim 2), along with hardening of the analytical software, spatialGE, for the analysis and visualization of ST data (Aim 3). The statistical and bioinformatics analytical approaches implemented in spatialGE will allow cancer researchers to easily leverage these methods in their studies of the TME. These analytical methods, along with the developed software tools (spatialGE R/Bioconductor package along with web-based software), will be established in collaboration with clinical, translational, and basic science cancer investigators to ensure usability and interpretability of the developed approaches.

项目概要/摘要 了解肿瘤微环境 (TME) 异质性和结构是癌症分层的关键 对免疫治疗和临床结果有反应的患者。单细胞 RNA 测序 (scRNAseq) 是一种 在单细胞水平上研究 TME 的强大工具，但是单细胞之间的空间信息 这项技术中没有保留这一点，这对于研究 TME 至关重要。相比之下，使用空间分辨 转录组学在理解 TME 的空间背景方面具有希望，因为它 能够捕获更大组织结构中单个细胞的位置。最近， 空间转录组 (ST) 技术的商业化使研究人员能够在 TME 的空间建筑达到了前所未有的水平。与其他“组学”技术类似，新颖 迫切需要计算工具来破译和推断这些高维的生物学意义 ST数据。除了需要分析和可视化当前形式的 ST 数据的方法之外，还需要 需要为 ST 的未来状态开发方法，其中细胞分辨率水平将大大提高 降低至单细胞水平。此外，对一个组织样本进行多种测定也很重要。 产生在同一样本上测量的多种模式（例如 scRNAseq、基于图像的细胞计数 方法，如多重免疫荧光），这需要有效的数据整合方法。最后， 许多 ST 研究是在多个样本上同时完成的，目的是关联 TME 特征 与临床结果相关，因此需要计算工具来揭示空间结构的影响 TME 对临床反应的影响。在拟议的研究中，我们将通过实施现状来应对这些挑战 最先进的统计和计算方法，可以解释和利用存在于其中的空间信息 ST数据来理解TME。我们将开发评估 TME 成分的创新方法 （目标 1）并研究共定位和空间异质性（目标 2），以及分析的强化 SpatialGE 软件，用于 ST 数据的分析和可视化（目标 3）。统计和生物信息学 SpatialGE 中实施的分析方法将使癌症研究人员能够轻松利用这些方法 他们的 TME 研究中的方法。这些分析方法以及开发的软件工具 （spatialGE R/Bioconductor 软件包以及基于网络的软件），将与 临床、转化和基础科学癌症研究人员，以确保其可用性和可解释性 开发的方法。