Enabling subcellular DESI-MSI for broad adoption: Acquisition, analysis, and application

Subcellular mass spectrometry imaging (MSI) has traditionally been inaccessible due to specialized equipment and costly software. Researchers have now demonstrated a method to make subcellular MSI more widely available. By modifying a standard commercial DESI source, they increased the pixel resolution from the typical 5-10 microns to routine 2x2 millimeters and even 1x1 millimeter in proof-of-concept experiments.

At a tissue-glass boundary, imaged at 2x2 millimeters, no measurable spillover was observed within a 4.4 millimeter uncertainty of the edge position.

To make the data interpretable, they extended MSI.EAGLE, an open-source, vendor-agnostic application with two new methods. The first preserves tissue architecture alongside subcellular detail, overcoming the limitations of conventional clustering at high resolution. The second performs landmark-free co-registration, recovering synthetic misalignments of up to 8 microns to within one pixel of a reference registration.

Using these enhancements, researchers were able to demonstrate genuine subcellular signals in mouse brain tissue. They resolved mitochondrial cardiolipin into perinuclear puncta and quantified cytoplasmic enrichment across 6,073 cells. By linking cell provenance to metabolism in 2D space directly, they assigned cell identities to MSI pixels using histology-predicted transcriptomics.

Finally, they recovered cell-type-specific metabolomes consistent with the transcriptomic identities, all using stock MSI hardware, open-source GUI-based software, and common histology techniques. This new workflow makes cellular and subcellular spatial metabolomics linked to cell provenance accessible to many researchers and laboratories.