Florian A. Rosenberger

FastaLake constructs compact metaproteomic databases from sequence resources to improve peptide identification without requiring matched metagenomic sequencing.

The paper identifies a conserved viral protein module that displaces host RNA polymerase sigma factors to control transcription in mycobacteria.

This study establishes quality control standards for single-cell proteomics through inter-laboratory benchmarking, identifying software and technical factors affecting data reliability.

The paper constructs a cell type-resolved proteomic atlas of the human body using Deep Visual Proteomics, revealing protein abundance patterns and uncovering cancer-testis antigens in oocytes.

This study shows that mitoSAM-dependent lipoylation is essential for postnatal heart development by enabling metabolic remodeling from glycolysis to oxidative phosphorylation.

This study uses single-cell spatial proteomics to map human liver zonation patterns and shows their disruption in fibrosis.

The paper uses Deep Visual Proteomics to show that human colon organoids switch to an in vivo-like phenotype after xenotransplantation.

The paper shows that mitochondrial SAM is essential for processing mitochondrial ribosomal RNA precursors and ribosome assembly.

nanoPhos improves phosphoproteomics sensitivity and enables cell-type resolved spatial analysis of mouse brain tissue.

This study uses spatial proteomics and machine learning to analyze AATD in human liver tissue, revealing peroxisomal upregulation and a terminal hepatocyte state with distinct proteomic signatures.

AlphaQuant improves proteoform regulation detection in bottom-up proteomics by using tree-based quantification and deep learning, revealing new patterns in mouse tissues.

The paper introduces scDVP, a spatial single-cell proteomics method, and finds that half of the murine hepatocyte proteome varies spatially within liver tissue.

This paper introduces a multiplex-DIA workflow that enhances single-cell proteome depth using dimethyl labeling and a reference channel, achieving reliable quantification of nearly 4,000 proteins per cell.

This paper introduces SILAF, a method for stable-isotope labeling in Drosophila to enable proteomic and phosphorylation site analysis, revealing metabolic regulation and protein turnover.