Sonja Fritzsche

The paper develops an ultrasensitive spatial tissue proteomics workflow capable of processing over 100 proteomes per day, demonstrating its utility in analyzing rare cancers.

The paper introduces openDVP, a framework for deep visual proteomics, demonstrating its application in three tissue studies to integrate spatial single-cell phenotypes with proteomic data.

This study uses spatial proteomics to analyze ovarian cancer precursor lesions, identifying early molecular changes and therapeutic targets.

This paper introduces a workflow for ultra-low input spatial tissue proteomics, demonstrating high protein quantification from single cells and microregions in murine and human tissues.

This paper presents a 3D spatial atlas of a human lung carcinoma, revealing tumor microenvironment niches and their associated immune interactions and survival outcomes.