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

项目摘要 肿瘤微环境(TME)在肿瘤进展和治疗反应中起着至关重要的作用。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 不同状态的常见细胞类型，相邻细胞之间的相互作用，以及T细胞功能。然而， TME的这些组件中的每一个都被不同的测量技术捕获。两者都是新的 多平台数据集成的计算方法和软件是描述TME的关键。 因此，我们提出了一个适用于多平台单细胞数据的统一R/BioConductor包TMEMap 整合。AIM 1将整合单细胞RNA测序和单细胞TCR测序数据以区分 T细胞在不同的T细胞亚型和状态下发挥作用。AIM 2将整合单细胞联合RNA测序 和来自CITE-SEQ的蛋白质，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 我也是。AIM 3将通过GenePattern Notebook进一步传播此软件。此工作流程将 与临床研究人员合作开发，以确保可用性和可解释性 使用来自协同研究的临床生物标本的可视化方法。总而言之，该软件将 为今后将这些方法嵌入到多平台单机数据库中的工作奠定了坚实的基础 在大规模NCI分析工作中自动执行全面的TME表征的单元数据，如 作为人类肿瘤图集网络。

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.