Frederic Ballllosera Navarro

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.

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.

Streamlining Multiplexed Tissue Image Analysis with PIP{Sigma}X: An Integrated Automated Pipeline for Image Processing and EXploration for Diverse Tissue Types

PIP{Sigma}X provides an automated, user-friendly pipeline for analyzing multiplexed tissue images, enabling robust spatial analysis and integration with visualization tools.

Multiplexed Deep Visual Proteomics Unveils Spatial Heterogeneity and Rare Endocrine States in Human Adult Pancreatic Islets

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.

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.

High-parametric protein maps reveal the spatial organization in early-developing human lung

This study maps the spatial organization of the developing human lung using high-parametric protein imaging, revealing cell type composition and temporal changes across five developmental timepoints.