RSR-MSI: Reference-Based Super-Resolution for Mass Spectrometry Imaging of Tissues and Single Cells

High-spatial-resolution mass spectrometry imaging (MSI) visualizes molecular distributions in tissues and cells. However, achieving higher spatial resolution typically necessitates smaller pixel dimensions and an increased number of pixels, leading to longer data acquisition times and diminished analytical throughput. Although deep learning approaches have demonstrated significant potential in MSI, they typically require large training data sets or paired images, which are often unavailable. Herein, we propose the reference-based super-resolution for mass spectrometry imaging (RSR-MSI) method, with optical microscopy images as reference frames to extract abundant texture information. By integrating this with ion intensity data from the original MS images, we develop an image-specific super-resolution network. Employing solely a single low-resolution MS image coupled with a reference optical image, we successfully reconstruct high-resolution MS images for biological tissues and single cells, producing results with rich chemical and textural details. This approach significantly decreases the routine pixel-by-pixel scanning time by an order of magnitude while achieving high spatial resolution using existing mass spectrometry instruments without any customized modifications. Overall, our work introduces and validates the application of image super-resolution methods within the realm of single-cell MSI at subcellular resolution, paving the way for the development of high-spatial-resolution and high-throughput MSI for cellular biology research.