Luca Rappez

This study induces skeletal muscle, neuronal, and endothelial cells from bovine embryonic stem cells and demonstrates their self-organization into vascularized tissue structures.

MetaLens uses deep learning to enhance spatial metabolomics resolution from microscopy, identifying subcellular metabolic domains without labels.

This paper introduces VascuMap, a deep-learning tool for label-free microvessel segmentation and spatial analysis, achieving high accuracy and enabling comprehensive examination of vascular networks.

The paper evaluates pixel-cell deconvolution methods in spatial single-cell metabolomics using fluorescein as ground truth, showing that weighted average outperforms linear inverse modeling and demonstrating the impact of ion suppression.

SpaceM integrates MALDI-imaging mass spectrometry with pre- and post-MALDI microscopy to measure >100 metabolites in >10000 individual cultured cells alongside fluorescence, morphology and spatial context.

An open-source cloud engine annotates imaging mass spectrometry data at controlled false discovery rate; publicly shared results reached over 3200 datasets and 6 million metabolite images by January 2019.