Inferring cell state tumor microenvironment maps by integrating single-cell and spatial transcriptomics

通过整合单细胞和空间转录组学推断细胞状态肿瘤微环境图

基本信息

批准号：
10305360
负责人：
ITAI YANAI
金额：
$ 23.77万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10305360
关键词：
Algorithms Architecture Atlas of Cancer Mortality in the United States Biological Cancer Biology Catalogs Cell Count Cells Communities Complex Computing Methodologies Consensus Data Data Analyses Dissociation Elements Generations Genes Genetic Programming Genetic Transcription Glass Heterogeneity Immunofluorescence Immunologic Individual Intuition Knowledge Link Localized Malignant Neoplasm Location Maintenance Malignant Neoplasms Maps Measures Methodology Methods Modality Molecular Neighborhoods Neoplasm Metastasis Pattern Portraits Reporting Research Personnel Role Slide Spottings Technology Time Tissues Treatment Failure Trees Work cancer cell cancer type cell type innovation insight neoplastic cell new technology novel novel strategies single cell technology single molecule single-cell RNA sequencing transcriptome transcriptomics tumor tumor microenvironment tumor progression

项目摘要

SUMMARY Single-cell RNA-Sequencing (scRNA-Seq) has proved to be a transformative technology for cancer biology, enabling the unbiased transcriptomic profiling of individual tumor cells and revealing a striking amount of transcriptional heterogeneity in malignant cells. Many reports in recent years have identified a range of cancer cell states in diverse cancer types suggesting that these are stable and functional tumor units, with roles in tumor maintenance and progression. However, a major shortcoming of scRNA-Seq analysis is the loss of spatial information which follows from the dissociation of the tumor prior to sequencing. Lacking knowledge of the general location of each cell within the tissue, as well as its local neighborhood, scRNA-Seq cannot alone inform us about the complex set of relationships among cancer cell states, together with their interactions with the elements of the tumor microenvironment. Spatial transcriptomics is a disruptive new technology that for the first time is able to measure whole transcriptomes in a robust fashion throughout a tissue. While spatial transcriptomics maps the expression of all genes simultaneously – enabling systematic and unbiased transcriptome analysis – it is not itself a single-cell technology and thus also cannot alone inform us on the patterning of cancer cell states together with states of the tumor microenvironment. Sensitive and robust algorithms are thus required to harness the full power implicit in an integration of these technologies. Here we propose to develop a new computational method called SNAP (Single-cell Neighborhood Map) which uses matched scRNA-Seq and spatial transcriptomics data from the same tumor to infer the spatial location of each scRNA-Seq-identified cell by reference to the spatial transcriptomics data, and produces a neighborhood transcriptome for each scRNA-Seq cell. To analyze these novel neighborhood transcriptomes we propose an approach to cluster cells with common patterns of neighbors, thereby identifying sets of colocalizing cell states. SNAP promises to exploit the complementary aspects of single-cell and spatial transcriptomics to link co- localizing cancer cell states and states of the tumor microenvironment. The methodology presented here includes several novel algorithms, all of which will be made freely available to the community, where we expect them to be broadly applicable across cancer biology.

概括事实证明，单细胞RNA - 序列（SCRNA-SEQ）是癌症生物学的一种变革性技术，实现单个肿瘤细胞的无偏转录分析和启发量的含量恶性细胞中的转录异质性。各种癌症类型中的细胞状态表明它们是稳定且功能性肿瘤单元，在但是，肿瘤维持和进展。在测序之前遵循肿瘤解离的空间信息每个细胞在组织中的一般位置以及当地的邻居引擎盖，scrna-seq不能孤单告知我们有关癌细胞状态之间复杂的关系以及与之相互作用的复杂关系。肿瘤微环境的元素。第一次能够以强大的快速方式测量整个转录转录组学映射所有同时基因的表达 - 启用系统和无偏见转录组分析 - 它是诺言是一种单细胞技术，因此在癌细胞态的模式以及肿瘤微环境的状态。算法是利用这些技术集成的全部力量含义所需的。建议开发一种名为SNAP（单细胞邻域图）的新计算机方法匹配的SCRNA-SEQ和空间转录数据来自相同肿瘤以推断每个肿瘤的空间位置通过参考空间转录组学数据，SCRNA-Seq识别的单元格，并产生一个邻域每个SCRNA-SEQ细胞的转录组。与邻居共同模式的聚类细胞的方法，鉴定共定位细胞态的集合。 SNAP纸张来利用单细胞转录组学的兼容，以将共同协调联系起来将癌细胞状态和肿瘤微环境的状态定位。包括几种新颖的算法，所有这些算法将提供给社区，我们希望它们在癌症生物学中广泛适用。