The article presents a novel framework called MEISTER, which integrates deep-learning-based reconstruction, high-throughput mass spectrometry imaging (MSI), and multimodal registration to achieve comprehensive and multiscale biochemical mapping of the brain. MEISTER enables the imaging of detailed lipid profiles in tissues with millions of pixels and in large single-cell populations from the rat brain. The framework includes a deep-learning model that enhances high-mass-resolving Fourier-transform MS (FTMS) acquisition by 15-fold, a multimodal image registration technique for creating 3D molecular distributions, and a data integration method that fits cell-specific mass spectra to 3D datasets. Using MEISTER, the authors imaged lipid profiles in 11 brain regions and identified region-specific lipid contents and cell-specific localizations of lipids. They also integrated single-cell MS (SCMS) data to map cell-type-specific chemical profiles onto tissue images, providing spatially resolved cell type distributions across the brain. The framework was validated through computational simulations and experimental tissue MSI and SCMS data, demonstrating its potential for multiscale biochemical characterization of the brain and other tissues.The article presents a novel framework called MEISTER, which integrates deep-learning-based reconstruction, high-throughput mass spectrometry imaging (MSI), and multimodal registration to achieve comprehensive and multiscale biochemical mapping of the brain. MEISTER enables the imaging of detailed lipid profiles in tissues with millions of pixels and in large single-cell populations from the rat brain. The framework includes a deep-learning model that enhances high-mass-resolving Fourier-transform MS (FTMS) acquisition by 15-fold, a multimodal image registration technique for creating 3D molecular distributions, and a data integration method that fits cell-specific mass spectra to 3D datasets. Using MEISTER, the authors imaged lipid profiles in 11 brain regions and identified region-specific lipid contents and cell-specific localizations of lipids. They also integrated single-cell MS (SCMS) data to map cell-type-specific chemical profiles onto tissue images, providing spatially resolved cell type distributions across the brain. The framework was validated through computational simulations and experimental tissue MSI and SCMS data, demonstrating its potential for multiscale biochemical characterization of the brain and other tissues.