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

Journal: Nature metabolism
PMID: 40102678

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.

Authors

  • Xin Ma
    Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
  • Cameron J Shedlock
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Terrymar Medina
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Roberto A Ribas
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Harrison A Clarke
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Tara R Hawkinson
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Praveen K Dande
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Hari K R Golamari
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Lei Wu
    Advanced Photonics Center, Southeast University, Nanjing, 210096, China.
  • Borhane Ec Ziani
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Sara N Burke
    Department of Neuroscience, University of Florida, Gainesville, FL, USA.
  • Matthew E Merritt
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Craig W Vander Kooi
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Matthew S Gentry
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
  • Nirbhay N Yadav
    The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Li Chen
    Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
  • Ramon C Sun
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA. ramonsun@ufl.edu.

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