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：
10478987
负责人：
ITAI YANAI
金额：
$ 19.41万
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 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10478987
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 tissue mapping 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.

概括单细胞 RNA 测序 (scRNA-Seq) 已被证明是癌症生物学的一项变革性技术，能够对单个肿瘤细胞进行公正的转录组分析，并揭示大量的近年来的许多报告已经确定了一系列癌症的转录异质性。不同癌症类型中的细胞状态表明这些是稳定且有功能的肿瘤单位，在然而，scRNA-Seq 分析的一个主要缺点是丢失了缺乏测序前肿瘤解离产生的空间信息。 scRNA-Seq 无法单独确定组织内每个细胞的大致位置及其局部邻域告诉我们癌细胞状态之间复杂的关系，以及它们与空间转录组学是一项颠覆性的新技术。第一次能够在整个组织中以稳健的方式测量整个转录组。转录组学同时绘制所有基因的表达图谱——实现系统性和公正性转录组分析——它本身不是单细胞技术，因此也不能单独告诉我们癌细胞状态的模式以及肿瘤微环境的状态敏感和稳健。因此，需要算法来充分利用这些技术集成中隐含的全部功能。建议开发一种称为 SNAP（单细胞邻域图）的新计算方法，该方法使用匹配来自同一肿瘤的 scRNA-Seq 和空间转录组数据，以推断每个肿瘤的空间位置 scRNA-Seq 参考空间转录组数据识别细胞，并生成邻域每个 scRNA-Seq 细胞的转录组为了分析这些新的邻近转录组，我们提出了一个方法对具有共同邻居模式的细胞进行聚类，从而识别一组共定位细胞状态。 SNAP 承诺利用单细胞和空间转录组学的互补性来将共同的定位癌细胞状态和肿瘤微环境的状态此处介绍的方法。包括几种新颖的算法，所有这些算法都将免费提供给社区，我们期望它们广泛适用于癌症生物学。