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<p>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 <a href="https://metavision3d.rc.ufl.edu">https://metavision3d.rc.ufl.edu</a>) as an interactive online database and web server to further advance brain metabolism and related research.</p>

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AI-driven framework to map the brain metabolome in three dimensions

  • 期刊论文

  • 发表日期：

    2025年3月18日

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  • Xin Ma,
  • Cameron J. Shedlock,
  • Terrymar Medina,
  • Roberto A. Ribas,
  • Harrison A. Clarke,
  • Tara R. Hawkinson,
  • Praveen K. Dande,
  • Hari K. R. Golamari,
  • Lei Wu,
  • Borhane EC. Ziani,
  • Sara N. Burke,
  • Matthew E. Merritt,
  • Craig W. Vander Kooi,
  • Matthew S. Gentry,
  • Nirbhay N. Yadav,
  • Li Chen,
  • Ramon C. Sun

摘要

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.