Enabling subcellular DESI-MSI for broad adoption: Acquisition through to analysis

Advances in the acquisition and analysis of spatial proteomics and spatial transcriptomics data have been transformative, leading to their wide adoption by researchers. Due to this popularity, standardized sample preparation, data acquisition, and analysis pipelines are becoming more common and accessible for these techniques. However, spatial metabolomics workflows tend to be more specialized across laboratories, and thus less accessible to the broader research community. The bespoke nature of spatial metabolomics is particularly evident in ultra-high spatial resolution mass spectrometry imaging (MSI), where analysis at the cellular and subcellular level is difficult to interpret, computationally intensive, and often requires specific, expensive hardware additions for acquisition.

Here, we will present the development of hardware and software solutions to democratize the acquisition and analysis of cellular and sub-cellular MSI. Our approach involves 1) acquisition of cellular and sub-cellular MSI data at 1 x 1 and 2 x 2 µm using a minimally adapted desorption electrospray ionization (DESI)-XS source, and 2) end-to-end analysis with our novel, open-source software package, MSI.EAGLE. Starting with an unprocessed mass spectrometry image (MALDI or DESI) in open .imzML format, an H&E stain of the same tissue, and consumer-grade compute, we demonstrate the ability to link cellular and sub-cellular metabolic information to cell provenance with our workflow. The entire analysis can be completed without difficult or expensive hardware modifications, without writing a single line of code, and using only open-source software. By lowering the barrier to entry in this manner, we hope to bring the state of high-resolution spatial metabolomics closer to other spatial omics techniques in its accessibility to researchers.