Automatic data exploration of mass spectrometry imaging using high resolving power

The dynamically harmonized FT-ICR cell (ParaCell©) is a mass analyzer used to achieve extreme resolving power (typically >400,000 @ m/z 800). However, when the FT-ICR analyzer is not carefully operated, abnormal shifts in the mass measurement accuracy are observed in-between MALDI acquisitions due to variation in the space charge in the analyzer. For MALDI mass spectrometry imaging, the inherent heterogeneity of biological sample results in great variation of the total ion current (TIC) from pixel to pixel, enhancing the observed mass shift phenomenon. To avoid this, an acquisition methodology focusing on the stabilization of the TIC during MALDI-MSI has been developed. Although this approach is successful, acquisition constraints may not allow the observation of compounds needing stronger desorption conditions. This work aims at developing a post-processing procedure to keep the extreme mass accuracy, even if the usual relatively strong desorption conditions are used for the acquisition of FT-ICR images. We propose a Python-based post-processing approach to recalibrate mass spectrometry images (MSI), without a priori specific peak knowledge, based on database identification. A comparison of the acquisition methods is performed on zebrafish sections using structural analysis of their ionic images and non-negative matrix factorization (NMF).