Ulrika Axelsson

Generative machine learning unlocks the first proteome-wide image of human cells

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.

Dissecting autonomous enzyme variability in single cells

The paper uses single-cell spatial proteomics to show that metabolic enzyme expression and localization vary between cells, revealing autonomous metabolic heterogeneity.

Intrinsic Heterogeneity of Primary Cilia Revealed Through Spatial Proteomics

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.

Proximity proteomics of primary cilia in human hypothalamic neurons

The paper identifies ciliary proteins in human hypothalamic neurons and finds new therapeutic targets for obesity, including LPAR1, which mediates ciliary shortening.

A Morpho-Proteomic Atlas of Mitosis at Sub-Minute Resolution

This study creates a high-resolution morpho-proteomic atlas of mitosis by decomposing it into 40 subsections and quantifying 4,350 proteins, revealing dynamic changes and links to cancer mutations.

SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology

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.