A Novel Peak-Shape Aware Approach for Mass Alignment in Mass Spectrometry.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Biotechnology

Background:

• Mass spectrometry data can suffer from mass shifts due to instrumental drift and calibration errors, impacting downstream analysis.
• Accurate mass alignment is crucial for reliable interpretation of spectral data, especially in complex biological samples.

Purpose of the Study:

• To develop and validate a novel, label-free algorithm for improving relative mass alignment between mass spectra.
• To address the challenge of gradual, nonlinear mass shifts in mass spectrometry data.

Main Methods:

• The algorithm models the warping function using natural cubic splines for accurate representation of mass shifts.
• Validation was performed on diverse datasets, including human glioblastoma multiforme samples analyzed by MALDI-TOF MSI and REIMS, as well as public datasets from MALDI-TOF and DESI Orbitrap instruments.

Main Results:

• The algorithm significantly reduces mass dispersion and enhances spectral similarity to reference spectra across various mass spectrometry techniques (MALDI-TOF, REIMS, DESI-Orbitrap).
• Demonstrated reliable correction of severe mass shifts, outperforming existing methods like MSIWarp in speed and effectiveness.
• Utilizes peak shape information from profile data for robust warping function computation.

Conclusions:

• A novel algorithm effectively reduces relative mass misalignment in mass spectrometry data.
• The open-source Python implementation, available on GitHub, facilitates wider adoption and application of the methodology.
• The method's robustness and efficiency make it a valuable tool for mass spectrometry data analysis.