Roberto A. Ribas Zunino / Research / MetaVision3D: mapping the brain metabolome in 3D
Co-first author · Nature Metabolism · 2025
MetaVision3D: mapping the brain metabolome in 3D
A co-first-author Nature Metabolism paper that moves spatial metabolomics from flat sections to a 3D brain metabolome atlas.
The problem
Spatial metabolomics shows striking complexity in a single 2D section, but tissues are three-dimensional. Stacking serial sections into a faithful 3D model is hard: each slice is cut, mounted and measured slightly differently.
What the pipeline does
MetaVision3D uses computer vision to transform serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. It aligns sections automatically (MetaAlign3D), normalizes slice-to-slice signal variation (MetaNorm3D), fills gaps in damaged sections (MetaImpute3D), and interpolates between sections (MetaInterp3D) to render a continuous 3D model.
As proof of principle the team built a 3D metabolome atlas of the mouse brain in normal and diseased animals and released it as a public, interactive web server.
Why it matters
It gives brain-metabolism researchers a way to see where molecules sit across whole structures, not just one plane, and an open atlas to query. UF Health covered the work at publication.
My role
One of four co-first authors (equal contribution). Building on this work I developed Vision (in-house, not yet published), an all-in-one platform that combines Sami (Spatial Augmented Multiomics Interface, Nat Commun 2025) and MetaVision3D for analysis, visualization and spatial UMAP clustering of high-dimensional spatial omics data.