MetaVision3D: Automated framework for the Generation of Spatial Metabolome Atlas in 3D
Xin MaiD, Cameron J. ShedlockiD, Terrymar Medina, Roberto A. RibasiD, Harrison A. ClarkeiD, Tara R. HawkinsoniD, Praveen K. Dande, Lei Wu, Sara N. BurkeiD, Matthew E. MerrittiD, Craig W. Vander KooiiD, Matthew S. GentryiD, Nirbhay Narayan YadaviD, Li CheniD, Ramon C. SuniD
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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