ArticleNature metabolism2025
AI-driven framework to map the brain metabolome in three dimensions.
Article in Nature metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Hyperglycosylation is a metabolic driver of Alzheimer's disease.Nature metabolism · 2026Article
- Mass Spectrometry-Based Spatial Imaging of the Cochlea.Journal of the American Society for Mass Spectrometry · 2026Article
- Decoding cancer across scales with metabolomics.Nature reviews. Cancer · 2026Review
- Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Applications and technical challenges of MALDI mass spectrometry imaging in microbial ecosystems and metabolic network research.Archives of microbiology · 2026Review
- A respiratory signature of disease progression in the Pompe rat.Journal of neurophysiology · 2026Article
- Anesthesia, perimortem conditions, and brain harvest method govern the measured metabolome: analysis of substandard samples generates flawed metabolite levels.Oxford open neuroscience · 2026Article
- Spatial Molecular Imaging of the Glycome Using Mass Spectrometry.Journal of visualized experiments : JoVE · 2025Article
- SpaBatch: Deep Learning-Based Cross-Slice Integration and 3D Spatial Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional (2D) space and is poised to be extended into the three-dimensional (3D) world of biology. Here we introduce MetaVision3D, a pipeline driven by computer vision, a branch of artificial intelligence focusing on image workflow, for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework uses advanced algorithms for image registration, normalization and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at submesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas of normal and diseased animals (available at https://metavision3d.rc.ufl.edu ) as an interactive online database and web server to further advance brain metabolism and related research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.