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[Preprint]. 2023 Nov 28:2023.11.27.568931. [Version 1] doi: 10.1101/2023.11.27.568931

MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D

Xin Ma, Cameron J Shedlock, Terrymar Medina, Roberto A Ribas, Harrison A Clarke, Tara R Hawkinson, Praveen K Dande, Lei Wu, Sara N Burke, Matthew E Merritt, Craig W Vander Kooi, Matthew S Gentry, Nirbhay N Yadav, Li Chen, Ramon C Sun
PMCID: PMC10705265  PMID: 38077043

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 space and is poised to be extended into the three-dimensional world of biology. Here, we introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs 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 mesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas (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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