Ozturk Efe, Venkataraman Abhijeet, Rivera Moctezuma Felix G, Coskun Ahmet F
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA 30332.
bioRxiv. 2024 Oct 24:2024.10.21.619323. doi: 10.1101/2024.10.21.619323.
Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological titissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creang high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.
质谱成像(MSI)是一种用于对生物组织内代谢物和其他生物分子进行空间分辨分析的强大技术。然而,MSI固有的低空间分辨率常常限制其提供详细细胞水平信息的能力。为了解决这一限制,我们提出了一种引导式超分辨率(GSR)方法,该方法利用高分辨率成像质谱流式细胞术(IMC)图像来提高低分辨率MSI数据的空间分辨率。通过将这些详细的IMC图像用作引导,我们提高了MSI图像的分辨率,创建了高分辨率代谢物图谱。这种增强有助于对细胞结构和组织结构进行更精确的分析,为单细胞水平的超分辨空间代谢组学提供更深入的见解。