Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based on Locally Endogenous Biological Signals

Abstract

Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the change in the number of ions from pixel-to-pixel of the biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their “a priori” selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel-specific internal calibrating ions, automatically generated in a data-adaptive manner (). Through a practical example, we applied the methodology to a zebrafish whole-body section acquired at a high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI data sets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.

Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions

“…To evaluate the proposed recalibration method's efficacy, we looked at two main effects: a) the mass error and the spatial distributions of selected ions, b) the number of putative molecular annotations. The number of putative annotations was tested similarly to La Rocca et al (La Rocca et al, 2021). We submitted the original and recalibrated peaks to METASPACE and evaluated the number of annotated formulas in each dataset.…”

“…The lock masses can be defined in each spectrum (La Rocca et al, 2021) or globally (same for all spectra). For our models, we used the latter approach.…”

“…Boskamp et al (Boskamp et al, 2020) showed that endogenous signal (chemical noise) could improve the mass accuracy in MALDI-MSI of peptide datasets. Recently, La Rocca et al (La Rocca et al, 2021) have presented an algorithm to recalibrate MSI datasets using a linear fit on the observed mass errors from endogenous biological peaks. The candidate reference ions were chosen among those most often detected in a given sample type, based on the publicly available MSI datasets on METASPACE (Palmer et al, 2016).…”

Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

“…Processed datasets (negative and positive modes) were exported into processed (centroided) imzML 26 files via MALDIquantForeign. Data-adaptive pixel-wise recalibration based on endogenous biological signals was conducted using the MSI-recalibration tool 27 . The centroided imzML data was uploaded into the METASPACE annotation platform 10 and lipid search was performed against the SwissLipid database 13 .…”

“…This serves as a protection against possible proximal background signals by down-weighting them relative to 𝑚 𝑀𝑂𝐼 ; the more the measured peak signal deviates from 𝑚 𝑀𝑂𝐼 , the lower the weight it receives in the SPP representation. It is important to note, however, that this does not protect against mis-calibrated/misaligned data and in such cases we recommend applying the dataadaptive MSI recalibration method of La Rocca et al 27 which was also performed for this study.…”