Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1

通过空间转录组学系统检测细胞间相互作用的计算方法 - 重新提交 - 1

基本信息

批准号：
10299124
负责人：
ITAI YANAI
金额：
$ 36.02万
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：
2021-07-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10299124
关键词：
Adopted Algorithms Atlases Biological Biological Assay Biological Models Biological Process Catalogs Cell Communication Cell Extracts Cells Complex Computing Methodologies Data Data Analyses Data Set Data Sources Detection Dissociation Generations Genes Glass Graph Immunofluorescence Immunologic Individual Ligands Link Location Maps Measures Methods Neighborhoods Organ Organism Pattern Placenta Population Resolution Signal Transduction Slide Spottings System Technology Testing Time Tissues Validation Variant base biological systems cell type experience graph theory innovation insight male multimodality novel receptor response single molecule single-cell RNA sequencing spatial integration technology validation transcriptome transcriptomics

Adopted Algorithms Atlases Biological Biological Assay Biological Models Biological Process Catalogs Cell Communication Cell Extracts Cells Complex Computing Methodologies Data Data Analyses Data Set Data Sources Detection Dissociation Generations Genes Glass Graph Immunofluorescence Immunologic Individual Ligands Link Location Maps Measures Methods Neighborhoods Organ Organism Pattern Placenta Population Resolution Signal Transduction Slide Spottings System Technology Testing Time Tissues Validation Variant base biological systems cell type experience graph theory innovation insight male multimodality novel receptor response single molecule single-cell RNA sequencing spatial integration technology validation transcriptome transcriptomics

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项目摘要

SUMMARY Many biological processes occur not at the level of a cell but at the level of a system, and cell-cell interactions are crucial for tissue function. With the introduction of single-cell RNA-Seq, we have robust measures of cell types and cell states. In this approach however, the tissue under study must be dissociated prior to sequencing resulting in the loss of spatial context. Spatial transcriptomics is a promising new field, in which several methods have been developed to profile the transcriptome of cells in their native context. However, the most widely used implementation of this technology – sequencing-based spatial transcriptomics – has not reached single-cell resolution. Thus, there is a critical need for novel computational approaches integrating spatial transcriptomics and single-cell RNA-Seq in order to infer cell-cell relationships in complex tissues. Our lab has recently developed analyses for multimodal intersection of these two data sources that effectively mitigate the limitations of each technology. Here, we propose to apply this concept to uncover patterns of cell-cell interactions in biological systems. In our first Aim, we present the StateMap approach to infer local cell-cell interactions by spatial transcriptomics-based co-localization and receptor-ligand relationships. A catalog of cell types and cell states is first delineated using single-cell data, and the spatial transcriptomics data is then harnessed to map pairs of co-localizing cell states. StateMap then systematically infers the cell-cell interaction mechanisms among co-localizing cell states by statistically testing for signal/response relationships in the spatial transcriptomics data. In our second Aim, we propose the ST-motif method to conceptualize the locations of cell types and states as a network, allowing for systematic analysis by a wealth of available methods. Our approach thus reframes the problem of finding cell-cell relationships as a network motif problem in this graph. Throughout our proposal, we develop and test the algorithms on two model systems, the male germline and the placenta, with which our lab has considerable experience. Conceptually, our proposal promises to yield novel algorithms for mapping cell-cell interactions that are required for actuating the potential of two powerful transcriptomic technologies.

概括许多生物过程不是出现在细胞的水平，而是在系统的水平上，而细胞 - 细胞相互作用对于组织功能至关重要。随着单细胞RNA-seq的引入，我们对细胞有强大的测量类型和细胞状态。但是，在这种方法中，必须在测序之前分离研究的组织导致空间环境的丧失。空间转录组学是一个承诺的新领域，其中有几个已经开发了在其本地环境中介绍细胞转录组的方法。但是，最多该技术的广泛使用 - 基于测序的空间转录组学 - 尚未达到单细胞分辨率。这是对整合空间的新型计算方法的迫切需求转录组学和单细胞RNA-Seq，以推断复杂组织中的细胞细胞关系。我们的实验室有最近开发了这两个数据源的多模式交集的分析，这些数据源有效地减轻了每种技术的局限性。在这里，我们建议将此概念应用于发现细胞细胞的模式生物系统中的相互作用。在我们的第一个目标中，我们提出了推断局部细胞细胞的statemap方法基于空间转录组学的共同定位和接收器配体关系进行的相互作用。细胞目录首先使用单细胞数据划定类型和单元格状态，然后是空间转录组数据利用映射成对的共定位细胞状态。然后，stapemap系统地渗透了细胞电池的相互作用通过统计测试对信号/响应关系的共定位细胞状态之间的机制空间转录组学数据。在我们的第二个目标中，我们提出了ST-MOTIF方法来概念化细胞类型和状态作为网络的位置，可以通过大量可用的财富进行系统分析方法。因此，我们的方法将查找细胞电池关系作为网络图案问题的问题重新确定在此图中。在我们的整个提案中，我们在两个模型系统上开发和测试算法，即男性生殖线和放血，我们的实验室考虑了经验。从概念上讲，我们的建议承诺产生新的算法，用于绘制绘制电势所需的细胞细胞相互作用两种强大的转录组技术。