A sulfatide-centered ultra-high-resolution magnetic resonance MALDI imaging benchmark dataset for MS1-based lipid...

Area of Science:

• Spatial omics
• Mass spectrometry imaging (MSI)
• Lipidomics

Background:

• Spatial omics techniques are vital for understanding biological systems and identifying spatial biomarkers.
• Current matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI) often provides only MS1 level data, limiting confident molecular identification.
• Challenges in assigning molecular identities from MS1 data hinder advancements in computational lipid annotation.

Purpose of the Study:

• To generate well-characterized, biology-driven datasets for benchmarking computational lipid annotation tools.
• To provide high-resolution magnetic resonance MSI (MR-MSI) data for lipid characterization in a mouse model of metachromatic dystrophy.
• To improve the confidence and accuracy of molecular annotations in spatial omics studies.

Main Methods:

• Acquisition of two sulfatide-centered MR-MSI datasets at varying mass resolving powers.
• Utilized ultra-high-resolution (R ∼1,230,000) quantum cascade laser mid-infrared imaging-guided MR-MSI for isotopic fine structure analysis.
• Compared manual sulfatide annotations with decoy database-controlled annotations in Metaspace.

Main Results:

• Provided two novel MR-MSI datasets characterizing lipids in a mouse model of metachromatic dystrophy.
• Demonstrated enhanced confidence in molecular annotations through ultra-high-resolution data and isotopic fine structure analysis.
• Validated the utility of the datasets by comparing manual and computational sulfatide annotations.

Conclusions:

• The generated datasets serve as valuable benchmarks for annotation algorithms.
• These datasets can validate spatial biomarker discovery pipelines.
• They provide a reference for future research on sulfatide metabolism and spatial regulation.