A statistical approach to system suitability testing for mass spectrometry imaging.

Area of Science:

• Analytical Chemistry
• Chemical Imaging
• Spectroscopy

Background:

• Mass spectrometry imaging (MSI) provides spatial chemical information but lacks standardized quality control (QC) and system suitability testing (SST).
• Inconsistent data acquisition in MSI can compromise experimental validity.
• There is a critical need for robust QC/SST protocols to ensure reliable MSI results.

Purpose of the Study:

• To develop an adaptable QC/SST approach for various ionization MSI platforms.
• To establish a method for monitoring instrument performance and data quality during MSI acquisition.
• To address the community's demand for standardized QC/SST in MSI.

Main Methods:

• A novel QC mix was applied to glass slides for creating regions-of-interest (ROIs).
• Principal component analysis (PCA) models were constructed and refined using data from optimal and compromised instrument conditions.
• Key metrics including mass accuracy and spectral accuracy were evaluated to generate individual compound suitability scores.

Main Results:

• A PCA-based SST model was successfully applied to data from a compromised instrument.
• A statistically significant threshold of 0.93 was defined for IR-MALDESI-MSI analyses, minimizing type-I errors.
• The study demonstrated the importance of QC and regular instrument maintenance for reliable MSI data.

Conclusions:

• This work presents a proof-of-concept for universal QC/SST protocols in MSI.
• The developed approach is adaptable to various MSI platforms, including DESI, MALDI, MALDI-2, and IR-MALDESI.
• Further development will focus on enhancing the efficiency of this QC/SST protocol for broader implementation.