CCS-aware SpatialOMx® enables highly confident and automatic lipid annotations with regiospecific context

Importance of the Topic

The reliable identification and localization of lipids within biological tissues is critical for understanding their roles in health and disease. Traditional MALDI imaging offers spatial resolution but lacks automated and high-confidence structural annotation. By integrating ion mobility and liquid chromatography data, the CCS-aware SpatialOMx® workflow greatly enhances lipid annotation accuracy and spatial context, enabling advanced lipidomics studies in neuroscience, pathology, and pharmaceutical research.

Objectives and Study Overview

This study introduces and evaluates the CCS-aware SpatialOMx® workflow, which merges four-dimensional lipidomics annotations with spatial MALDI-TIMS imaging data. Major goals include:

• Developing an automated pipeline for matching LC-PASEF® lipid annotations to MALDI-TIMS imaging spectra based on exact mass, isotopic pattern, MS/MS fragments, retention time, and CCS values
• Demonstrating reproducible CCS measurements across ESI and MALDI ionization
• Visualizing tissue-specific lipid distributions in mouse brain sections with high confidence and regiospecific context

Methodology and Instrumentation

The workflow consists of two parallel experiments on a timsTOF fleX instrument:

• MALDI-TIMS Imaging
  • Fresh frozen mouse brain sections (10 µm) sprayed with ZSA matrix
  • Negative-ion mode acquisition at 20–50 µm pixel size, m/z 300–1200, mobility 1/K0 0.6–1.8 V∙s/cm², 800 ms ramp
  • Spectra and unsupervised segmentation in SCiLS™ Lab 2021a
• 4D-Lipidomics™ by LC-PASEF®-MS/MS
  • Lipid extraction from brain hemisphere
  • UHPLC on C18 column (100×2.1 mm, 1.9 µm; 55 °C; 0.4 mL/min; acetonitrile/water and isopropanol/acetonitrile gradients)
  • ESI(+) and ESI(–) PASEF® acquisitions m/z 100–1500, mobility 1/K0 0.55–1.90 V∙s/cm²
  • MetaboScape® 2021b feature extraction (T-ReX® 4D) and rule-based lipid annotation (41 subclasses)

Main Results and Discussion

• 4D-Lipidomics™ annotated 292 lipids in negative mode, 323 in positive, yielding 446 unique species after merging. Rule-based classification spanned glycerolipids, glycerophospholipids, sphingolipids, and sterols.
• Kendrick Mass Defect (KMD) plots colored by retention time and sized by CCS distinguished lipid series and exposed outliers, validating annotation consistency.
• CCS values measured by ESI and MALDI for representative lipids varied by <1 %, confirming cross-technique reproducibility and supporting CCS as a robust matching criterion.
• Automated CCS-aware annotation in MetaboScape® matched 82 lipids between ESI and MALDI datasets. Annotations imported into SCiLS™ Lab enabled direct visualization of MS/MS-confirmed lipid identities instead of mere m/z labels.
• High resolving power of TIMS separated isobaric lipids PE 20:1_22:6 and PS 38:1 (Δm/z 23.7 mDa). Mobility-resolved images revealed distinct tissue localization: PE 20:1_22:6 in cerebellar gray matter and PS 38:1 in white matter structures.

Benefits and Practical Applications

• Enhances confidence in spatial lipidomics by combining orthogonal separation dimensions (LC, mass, mobility)
• Automates cross-platform data fusion, reducing manual annotation and potential errors
• Enables regiospecific lipid mapping in complex tissues, facilitating biomarker discovery and pathology studies

Future Trends and Applications

Emerging directions include:

• Integration of in-silico CCS prediction tools to expand lipid coverage and detect novel species
• Single-cell and subcellular resolution imaging leveraging faster TIMS ramps and laser scanning modes
• Multi-omics fusion combining proteomics, metabolomics, and lipidomics spatial data for comprehensive tissue profiling
• Application to clinical samples for diagnostics and treatment monitoring in neurodegenerative and metabolic diseases

Conclusion

The CCS-aware SpatialOMx® workflow on the timsTOF fleX platform provides a seamless and highly confident approach for lipid annotation in MALDI imaging. By leveraging 4D-Lipidomics™ data and ion mobility separation, it overcomes traditional limitations of MALDI-MS/MS, enabling accurate, automated, and spatially resolved lipidomics suitable for advanced research and quality control applications.

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