Single-cell and imaging data integration software to spatially resolve the tumor microenvironment

用于空间解析肿瘤微环境的单细胞和成像数据集成软件

基本信息

批准号：
10058504
负责人：
Elana Judith Fertig
金额：
$ 40.59万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10058504
关键词：
Algorithms Antigens Atlases Award Bioconductor Cell Surface Proteins Cell physiology Cell surface Cells Clinical Clinical Investigator Clinical Trials Clonality Collaborations Complement Computer software Computing Methodologies Data Data Analyses Databases Detection Ensure Foundations Future Gene Expression Profile Genes Heterogeneity Human Image Imaging technology Immune Immunologist Immunotherapy Individual Informatics Lesion Maps Measurement Measures Methods Modality Molecular Pathway interactions Patients Pattern Play Proteins Proteomics Psychological Transfer Reproducibility Resistance Resolution Role Source T-Cell Receptor T-Lymphocyte Technology Tumor-infiltrating immune cells Visualization Work cancer cell cell type cellular imaging cohort data integration digital pathology exhaust genetic signature high dimensionality immunogenic immunotherapy clinical trials informatics tool large scale data learning strategy longitudinal analysis member multimodality programmed cell death protein 1 response single cell technology single-cell RNA sequencing software development spatial integration statistics transcriptomics treatment response tumor tumor microenvironment tumor progression usability

项目摘要

Project Summary The tumor microenvironment (TME) plays a critical role in cancer progression and therapeutic response. New single cell and imaging technologies provide unprecedented measurements of cell type composition, subtypes of common cell types in disparate states, interactions between neighboring cells, and T cell function. However, each of these components of the TME are captured by disparate measurement technologies. Both new computational methods and software for multi-platform data integration are essential to characterize the TME. Therefore, we propose a unified R/Bioconductor package TMEMap for multi-platform single cell data integration. Aim 1 will integrate single cell RNA-sequencing and single cell TCR-sequencing data to distinguish T cell function in distinct T cell subtypes and states. Aim 2 will integrate combined single cell RNA-sequencing and protein from CITE-seq with imaging proteomics for digital pathology to map cellular interactions in the TME. Aim 3 will further the disseminate this software with through GenePattern Notebook. This workflow will be developed in collaboration with clinical investigators to ensure usability and interpretability of the visualization methods using clinical biospecimens from synergistic studies. Altogether, this software will provide a strong foundation for future work embedding these methods in a database for multi-platform single cell data to automatically perform comprehensive TME characterization in large scale NCI profiling efforts such as the Human Tumor Atlas Network.

项目概要肿瘤微环境（TME）在癌症进展和治疗反应中发挥着关键作用。新的单细胞和成像技术提供了前所未有的细胞类型组成、亚型测量不同状态下的常见细胞类型、相邻细胞之间的相互作用以及 T 细胞功能。然而， TME 的每个组成部分均由不同的测量技术捕获。都是新的用于多平台数据集成的计算方法和软件对于表征 TME 至关重要。因此，我们提出了一个统一的 R/Bioconductor 包 TMEMap，用于多平台单细胞数据一体化。目标 1 将整合单细胞 RNA 测序和单细胞 TCR 测序数据来区分 T 细胞在不同的 T 细胞亚型和状态下发挥功能。目标 2 将整合组合单细胞 RNA 测序以及来自 CITE-seq 的蛋白质和用于数字病理学的成像蛋白质组学，以绘制细胞相互作用图谱 TME。 Aim 3 将通过 GenePattern Notebook 进一步传播该软件。该工作流程将与临床研究人员合作开发，以确保该方法的可用性和可解释性使用来自协同研究的临床生物样本的可视化方法。总而言之，该软件将为未来的工作提供坚实的基础，将这些方法嵌入到多平台单一数据库中细胞数据可在大规模 NCI 分析工作中自动执行全面的 TME 表征，例如作为人类肿瘤图谱网络。