High dimensional in situ proteomics imaging system

点击翻译按钮获取中文摘要

Multicellular organisms are composed of tissues, which in turn are assemblies of cells with varying cell types with characteristic high dimensional molecular, e.g. proteomic profiles. Function, as well as dysfunction in multicellular organisms – as for instance development of cancer – depends on the balance of interactions and regulation between these cells in their spatial tissue context. Understanding and potential rational derivation of interventions for modulation of tissue function in health and disease ultimately require (1) experimental techniques to comprehensively profile single cells in their tissue context and (2) machine learning approaches interpret the resulting complex data to the end of achieving concise and actionable biomedical findings. Tissue imaging has been the technique of choice to address (1) by allowing monitoring of single cells in their tissue context. Technological developments are allowing an ever increasing number of molecular parameters to be monitored simultaneously at the single-cell level. Machine learning approaches to address (2) are constantly evolving with latter imaging approaches. While research institutions/initiatives in Tübingen such as the university and Max-Planck institutes and three DFG Excellence Clusters, in particular including the main applicant bridge the gap between and synergize translational and machine learning research, there is a lack in high dimensional tissue imaging capacity. To close this gap, we aim to install a tissue imaging system that scales to a technically unlimited number of measurement parameters (protein marker), thereby enabling a detailed and multidimensional analysis of different cell populations in one tissue section.