William D. Leineweber

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 analyzes how cell shape correlates with proteomic features across multiple cell lines using image-based spatial proteomics data.

This study uses holotomographic microscopy to visualize endothelial cell dynamics during endothelialization, revealing insights into lipid droplet and mitochondrial behavior.

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.