Integrated annotation pipeline using in-silico prediction for on-target chemical derivatization MALDI Imaging

Importance of the Topic

On-target chemical derivatization (OTCD) enhances ionization efficiency and spatial detection of small, low molecular weight, or nonpolar analytes in MALDI Imaging. It is pivotal for detailed mapping of neurotransmitters and metabolic intermediates, improving sensitivity and contrast in biological tissues.

Objectives and Study Overview

This work describes an integrated pipeline that couples in-silico derivatization prediction with high-resolution MALDI Imaging and advanced annotation software. Two case studies illustrate the approach: FMP-10 for imaging neurotransmitters in rat brain and 4-APEBA for TCA cycle metabolites in poplar root sections.

Methodology

Derivatization parameters were defined using MetaboScape 2025b by specifying reagent structures (InChI/SMILES) and reaction sites via SMARTS strings. Imaging was performed on timsTOF fleX MALDI-2 instruments. SCiLS Lab 2025b imported the data, applied T-ReX Feature Finding, and triggered annotation pipelines for both underivatized and derivatized species. CCS Predict Pro within MetaboScape provided collision cross section scoring to enhance confidence.

Instrumentation Used

• timsTOF fleX MALDI-2 mass spectrometer for Imaging
• MetaboScape 2025b for in-silico derivatization and CCS prediction
• SCiLS Lab 2025b with T-ReX for feature finding and annotation

Key Results and Discussion

Application of FMP-10 in rat brain sections achieved clear localization of dopamine, serotonin, noradrenaline (including demethylated forms), and spermidine at 50 µm resolution with high mass accuracy and isotopic pattern matching. Using 4-APEBA in poplar roots enabled imaging of malic acid, citric acid and related TCA cycle metabolites at 5 µm resolution. In-silico derivatization streamlined target list generation and, combined with CCS scoring, reduced false positives and manual curation.

• Neurotransmitter distributions matched known anatomical regions.
• TCA intermediates were visualized at subcellular resolution in plant tissues.
• Automated derivatization and annotation accelerated data processing.
• CCS and m/z scoring ensured robust structure assignments.

Benefits and Practical Applications

This unified workflow significantly reduces manual effort, improves annotation throughput, and delivers high-confidence spatial metabolite maps. It is applicable to neurochemical studies, plant metabolomics, pharmaceutical research, and clinical investigations where small molecule imaging is essential.

Future Trends and Opportunities

Future developments may include expansion to diverse derivatization chemistries, integration of machine learning for automated interpretation, real-time derivatization feedback, and growth of CCS databases. Potential applications encompass drug distribution profiling, biomarker discovery, and single-cell metabolomics.

Conclusion

The combined in-silico derivatization and MALDI Imaging pipeline offers a streamlined, high-throughput approach for mapping small molecules with enhanced confidence and spatial resolution. It holds promise for widespread adoption in analytical and biomedical research.

Reference

Behrens A et al. Integrated annotation pipeline using in-silico prediction for on-target chemical derivatization MALDI Imaging. Bruker Life Sciences Mass Spectrometry, 2025. Shariatgorji M et al. Neurotransmitter mapping protocols. Boehringer Ingelheim, 2019. Zemaitis K et al. TCA cycle metabolite imaging in plants. PNNL, 2023.