Konstantin Kahnert

A conditional latent diffusion transformer predicts immunofluorescence images of 12,800 human proteins from nucleus, ER and microtubule stains, outperforming PUPS and CELL-Diff, including on an unseen drug-treated cell line.

The paper uses spatial proteomics to analyze primary cilia proteomes across cell lines, revealing cell-type specificity and single-cilia heterogeneity, and identifies novel cilia proteins and potential genetic variants.

SubCell is a deep learning model for fluorescence microscopy that captures cellular morphology and protein organization, outperforming existing methods and enabling a multimodal approach to gene function.

FLEXIQuant-LF quantifies protein modification extent in label-free proteomics data using robust regression, applied to APC/C mitosis data.