Mass spectrometry imaging (MSI) has become widely popular because of its potential to map the spatial distribution of thousands of compounds in a single measurement directly from tissue surfaces. With every MSI experiment, it is important to maintain high mass accuracy for correct identification of the observed ions. Many times this can be compromised due to different experimental factors, leading to erroneous assignment of peaks. This makes recalibration a crucial preprocessing step. We describe a lock mass-free mass spectra recalibration method, which enables to significantly reduce these mass shift effects. The recalibration method is applied in three steps: First, we decide on an order to process all the spectra. Herein, we describe three different methods for ordering the spectra-minimum spanning tree (MST), topological greedy (TG), and crystal growth (CG). Second, we construct a reference (consensus) spectrum, from the ordered spectra, and third, all spectra are individually corrected against this consensus spectrum. The performance of the recalibration method is demonstrated on three imaging datasets acquired from matrix-assisted laser desorptionionization (MALDI) and laser desorption/ionization (LDI) mass spectrometry imaging of whole-body Drosophila melanogaster fly. The applied recalibration method is shown to strongly reduce the observed mass shifts in the imaging datasets. Among the three ordering methods, CG and MST perform comparatively better than TG and highly decrease the overall standard deviation of the mass error distribution. Lock mass correction of MSI data is practically difficult, as not all spectra contain the selected lock mass peak. Our method eliminates this need.
Keywords: Data processing; Mass shift correction; Mass spectrometry imaging; Recalibration.