High dimensional in situ proteomics imaging system

高维原位蛋白质组成像系统

• 批准号: 522213027
• 金额: --
• 依托单位: Eberhard Karls Universität Tübingen
• 依托单位国家: 德国
• 项目类别: Major Research Instrumentation
• 财政年份: 2023
• 资助国家: 德国
• 起止时间: 2022-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/522213027?language=en
• 关键词: dimensional; situ; proteomics; imaging; system

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.

多细胞生物体由组织组成，而组织又是具有不同细胞类型的细胞的集合体，具有特征性的高维分子，例如蛋白质组谱。多细胞生物体的功能以及功能障碍-例如癌症的发展-取决于这些细胞在其空间组织环境中的相互作用和调节的平衡。理解和潜在的合理推导干预措施，以调节健康和疾病中的组织功能，最终需要（1）实验技术，以全面分析组织背景下的单细胞，（2）机器学习方法解释产生的复杂数据，以实现简洁和可操作的生物医学发现。组织成像已经成为解决（1）通过允许在其组织环境中监测单细胞的技术选择。技术的发展使得越来越多的分子参数可以在单细胞水平上同时监测。解决（2）的机器学习方法随着后面的成像方法而不断发展。虽然蒂宾根的研究机构/倡议，如大学和马克斯普朗克研究所和三个DFG卓越集群，特别是包括主要申请人之间的桥梁和协同转化和机器学习研究之间的差距，但缺乏高维组织成像能力。为了缩小这一差距，我们的目标是安装一个组织成像系统，该系统可以扩展到技术上无限数量的测量参数（蛋白质标记物），从而能够对一个组织切片中的不同细胞群进行详细和多维的分析。