Charting the molecular structure of complex tissues using spatial covariance tensors

• Applicant: Dr. Simon Mages
• Subject Area: Bioinformatics and Theoretical Biology ; Nuclear and Elementary Particle Physics, Quantum Mechanics, Relativity, Fields
• Term: from 2020 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442489549

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.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professorin Dr. Aviv Regev, Ph.D.