Past-in-the-Future. Peak detection improves targeted mass spectrometry imaging

Area of Science:

• Pharmacology
• Analytical Chemistry
• Biotechnology

Background:

• Accurate drug distribution analysis in tumors is vital for cancer pharmacology.
• Treatment failure can result from poor drug penetration within tumors.
• Mass spectrometry imaging (MSI) offers visualization of drug localization but requires robust preprocessing.

Purpose of the Study:

• To introduce a simple, quantitative preprocessing and quantification pipeline for MSI data.
• To enable reliable ion peak identification and integration for drug distribution analysis.
• To evaluate the distribution of the anticancer drug paclitaxel in tumor tissues.

Main Methods:

• A novel preprocessing approach using a fixed mass difference between analyte and its derivatives to create a mass range gate.
• Application of classical peak integration methods adapted for MSI.
• Quantification of paclitaxel concentration per pixel in tumor sections.

Main Results:

• Achieved a limit of detection below 0.1 pmol mm⁻² or 10 μg g⁻¹ for paclitaxel.
• Generated quantitative images of paclitaxel distribution in various tumor models.
• Demonstrated <20% difference between MSI quantification and HPLC measures in the same specimens.

Conclusions:

• The proposed pipeline provides accurate and sensitive quantification of drug distribution using MSI.
• This method is valuable for understanding drug penetration in tumors, potentially improving cancer therapy.
• The developed Python scripts are publicly available for broader research application.