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.

项目摘要/摘要 了解肿瘤微环境(TME)的异质性和结构是癌症分层的关键 对免疫治疗有反应的患者和临床结果。单细胞RNA测序(ScRNAseq)是一种 然而，在单细胞水平上研究TME的有力工具是单细胞之间的空间信息 没有保存在这项技术中，这对研究TME至关重要。相比之下，空间分辨率的使用 转录组学在理解TME的空间背景方面拥有希望，因为它 捕捉单个细胞在较大组织架构中的位置的能力。最近， 空间转录(ST)技术的商业化使研究人员能够在 TME的空间架构达到了前所未有的水平。与其他“组学”技术类似，新奇 迫切需要计算工具来破译和推断这些高维生物的生物学意义 ST数据。除了需要以当前形式分析和可视化ST数据的方法外，还有 需要为ST的未来状态开发方法，从而使细胞分辨率水平大大提高 减少到单细胞水平。此外，对一个组织样本的多个检测也得到了应用 产生在同一样本上测量的多个模式(例如，scRNAseq、基于图像的细胞术 多重免疫荧光等方法)，这需要有效的数据集成方法。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 临床结果，因此需要计算工具来解开空间架构的影响 TME的临床反应。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，解释和利用存在于 ST数据，以了解TME。我们将开发创新的方法来评估TME的成分 (目标1)和研究共同局部化和空间异质性(目标2)，以及加强分析 用于科技数据分析和可视化的软件spatialGE(目标3)。统计学和生物信息学 在spatialGE中实施的分析方法将使癌症研究人员能够轻松地利用这些 方法对TME进行了研究。这些分析方法，以及开发的软件工具 (spatialGE R/BioConductor包以及基于网络的软件)，将与 临床、翻译和基础科学癌症研究人员，以确保 已开发的方法。