Enhancing Mass Spectrometry Imaging Accuracy via Spatially Informed Mass Recalibration

Mass spectrometry imaging (MSI) is a powerful analytical technique that allows for visualizing the spatial distribution of molecules directly on biological samples. MSI data acquired using MALDI-TOF spectrometers can suffer from significant mass shifts of up to 1 Dalton, jeopardizing data interpretation. Despite efforts in sample preparation, practical limitations tied to the specific mass spectrometer and sample may persist.

Our work introduces a computational pipeline developed in the Python language that recalibrates proteomics MSI data. It estimates these shifts, uses the spatial structure to smooth the problem, and uses the estimates to mitigate the effect of these shifts effectively.

We study two scenarios: targeted, where known target masses serve as calibration references, and untargeted, where we create a reference mass list using an average peptide mass model.

We applied the pipeline to several example datasets and developed and implemented visualizations to study the effects of the calibration. We compared the newly developed error smoothing method with previously published methods for mass calibration of proteomics MALDI-TOF data.

Correcting mass errors can yield high-quality images and facilitate MSI data interpretation. We also show some of the current limitations of the approach. The newly developed Python package supports several methods for mass calibrating proteomics MSI datasets and visualization methods to assess calibration quality.