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.

项目总结/文摘