Marvin Thielert

The paper introduces the ZenoTOF 8600 system, which enhances proteomics by increasing ion flux and improving precursor identification and quantification in DIA mode.

The paper quantifies proteins and transcripts in the same cell, finding that the proteome defines stable cell states while the transcriptome reflects transitions.

The paper introduces QuantSelect, a deep learning method for fragment ion selection in DIA proteomics, improving quantification accuracy and differential expression sensitivity.

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

This paper introduces mxDVP, a method combining high-plex imaging and mass spectrometry to profile pancreatic islets, identifying 12 endocrine subtypes and revealing spatial heterogeneity and rare cell states.

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

AlphaDIA enables end-to-end transfer learning for feature-free proteomics, improving identification and quantification of post-translational modifications.

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.

AlphaViz validates proteomics results by visualizing raw data and comparing with automated analysis outputs.

The paper develops a high-sensitivity mass spectrometry workflow to quantify single-cell proteome changes upon perturbation, revealing a stable core proteome despite transcriptomic variability.