Charting the molecular structure of complex tissues using spatial covariance tensors

使用空间协方差张量绘制复杂组织的分子结构图

• 批准号: 442489549
• 负责人: Dr. Simon Mages
• 金额: --
• 依托单位: Broad Institute of MIT and Harvard
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/442489549?language=en
• 关键词: Charting; molecular; structure; complex; tissues

Developments in molecular biology during the last decade made it possible to investigate many properties of single cells in tissues, e.g. their genetic information (genetics), its working copies in the cells (transcriptomics) and their protein equipment (proteomics). These "omics" can be used e.g. to characterize all human cells and to group them into cell types, like it is already being done in the international Human Cell Atlas project. The results serve as basis for a better understanding of healthy tissues and diseases as well as for devising treatments. Until recently, the tissue to be analysed had to be dissolved into separated cells in order to acquire the molecular profiles of isolated cells. However, in this process one loses information about the spatial relationships between cells, which are of paramount importance for growth, structure, and function of tissues. In fact, there are also certain diseases like auto-immune diseases and cancer, in which malfunctioning or mislead local communication between cells plays a potentially important role. Recently, experimental methods are increasingly being developed which enable omics on cells within the intact tissue. With the spatial relationships, the resulting data contain a brand-new quality of information, which should be utilized most efficiently to leverage its full potential. However, the methods for the analysis of these data are at the moment far less developed than for pure single cell data without spatial component. In this project, methods from a seemingly completely different field of science shall be recruited for these analyses: from lattice quantum field theory, a subdomain of theoretical physics. There, data with spatial dependencies have been commonplace for decades and many methods to make optimal use of them have been developed there. The data from both areas have an important aspect in common: spatial correlations constitute the basis of the observed phenomena. In lattice quantum field theory these are states of elementary particles and in spatially resolved omics these are states or programs of cells which exert influence across the boundaries of cells and thereby crucially determine the overall appearance of a tissue. To decipher these spatial cell programs, this project will focus on the extension of single cell covariance matrices to spatial covariance tensors, including their computation, modelling and analysis. Complementarily, a method will be developed to simulate spatially resolved omics data for testing data-analytic methods and to aid the design of experiments e.g. for the above-mentioned atlas projects.

在过去十年中，分子生物学的发展使得研究组织中单个细胞的许多特性成为可能，例如其遗传信息（遗传学），其在细胞中的工作拷贝（转录组学）和其蛋白质设备（蛋白质组学）。这些“组学”可以用来描述所有人类细胞的特征，并将它们分为细胞类型，就像国际人类细胞图谱项目中已经完成的那样。这些结果为更好地了解健康组织和疾病以及设计治疗方法奠定了基础。直到最近，要分析的组织必须溶解成分离的细胞，以获得分离细胞的分子谱。然而，在这个过程中，人们丢失了关于细胞之间空间关系的信息，这些信息对组织的生长，结构和功能至关重要。事实上，也有某些疾病，如自身免疫性疾病和癌症，其中细胞之间的局部通信功能障碍或误导起着潜在的重要作用。最近，实验方法越来越多地被开发，其能够在完整组织内的细胞上进行组学。有了空间关系，产生的数据包含了全新的信息质量，应该最有效地利用这些信息，以充分发挥其潜力。然而，这些数据的分析方法目前远不如没有空间成分的纯单细胞数据发达。在这个项目中，来自一个看似完全不同的科学领域的方法将被招募来进行这些分析：从晶格量子场论，理论物理学的一个子域。在那里，具有空间依赖性的数据几十年来一直很常见，并且已经开发了许多优化利用它们的方法。来自这两个地区的数据有一个重要的共同点：空间相关性构成了观察到的现象的基础。在晶格量子场论中，这些是基本粒子的状态，而在空间分辨组学中，这些是细胞的状态或程序，它们跨越细胞的边界施加影响，从而决定了组织的整体外观。为了破译这些空间细胞程序，该项目将专注于将单细胞协方差矩阵扩展到空间协方差张量，包括它们的计算，建模和分析。作为补充，将开发一种方法，模拟空间分辨组学数据，以测试数据分析方法，并帮助设计实验，例如上述图谱项目。