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

Area of Science:

• Biochemistry
• Analytical Chemistry
• Molecular Imaging

Background:

• Tissue lipid analysis requires high resolution and spatial context.
• Conventional mass spectrometry imaging (MSI) limits analytical depth versus spatial resolution.

Purpose of the Study:

• Introduce Deconvolution-based Structure-Specific Spatial Lipidomics (DeconS2L) for advanced lipidomics.
• Overcome limitations of conventional MSI for comprehensive tissue lipid analysis.

Main Methods:

• DeconS2L couples per-pixel and broadband MS/MS sampling with deconvolution algorithms.
• Lipids are co-fragmented for convolved MS/MS spectra to improve throughput.
• Utilizes tissue heterogeneity for large-scale lipid annotation and imaging.

Main Results:

• Analyzed mouse cerebellum tissue, identifying 100 lipids and enabling multiplexed imaging.
• Resolved isobaric interferences in human hepatocellular carcinoma (HCC) tissues.
• Revealed tumor-specific enrichment of PC 33:1 and triglyceride saturation heterogeneity in HCC.

Conclusions:

• DeconS2L integrates molecular annotation with spatial lipidomics.
• Demonstrates potential for in-depth biomarker discovery in clinical pathology.
• Offers a versatile methodology for complex biological samples.