Tissue Heterogeneity-Driven Parallel Acquisition for High-Coverage MS/MS Imaging of Lipids

Elucidating tissue architecture and function necessitates lipid analysis that is both spatially comprehensive and achieved at high resolution while maintaining spatial context. Conventional mass spectrometry imaging (MSI) techniques typically perform MS/MS analysis sequentially for individual lipids, creating a significant trade-off between spatial resolution and analytical depth. To circumvent this limitation, we introduce a deconvolution-based structure-specific spatial lipidomics (DeconS²L) method for coupling with per-pixel and broadband MS/MS sampling. DeconS²L capitalizes on the spatial and compositional heterogeneity inherent to all biological tissues for large-scale lipid annotation and lipidome imaging. To facilitate rapid imaging throughput and improved sample usage, lipids per pixel are co-fragmented to generate convolved MS/MS spectra. DeconS²L analysis of ∼4000 pixels in a mouse cerebellum tissue yielded 100 annotated lipids and allowed multiplexed MS/MS imaging of the tissue lipidome. Furthermore, applying DeconS²L to human hepatocellular carcinoma (HCC) tissues effectively resolved isobaric interferences that obscured tumor margins in conventional imaging. It successfully revealed the tumor-specific enrichment of odd-chain lipid PC 33:1 and distinct spatial heterogeneity in triglyceride saturation, correlating with metabolic reprogramming in HCC. DeconS²L represents a versatile methodology that effectively integrates molecular annotation with spatial lipidomics, demonstrating significant potential for in-depth biomarker discovery in clinical pathology.