Adaptive pixel mass recalibration for mass spectrometry imaging based on locally endogenous biological signals.

Abstract

<p>Mass spectrometry imaging (MSI) is a powerful and convenient method to reveal the spatial chemical composition of different biological samples. The molecular annotation of the detected signals is only possible when high mass accuracy is maintained across the entire image and the <i>m/z</i> range. However, the heterogeneous molecular composition of biological samples could result in fluctuations in the detected <i>m/z</i>-values, called mass shift. Mass shifts impact the interpretability of the detected signals by decreasing the number of annotations and by affecting the spatial consistency and accuracy of ion images. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. The selection of internal calibrating signals for a global MSI acquisition is not trivial, prone to false positive detection of calibrating signals 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 internal calibrating ions generated automatically in a data-adaptive manner. The method exploits RANSAC (<i>Random Sample Consensus</i>) algorithm, to select, in a robust manner, the experimental signal corresponding to internal calibrating signals by filtering out calibration points with infrequent mass errors and by using the remaining points to estimate a linear model of the mass shifts. We applied the method to a zebrafish whole body section acquired at high mass resolution to demonstrate the impact of mass shift on data analysis and the capacity of our algorithm to recalibrate MSI data. We illustrate the broad applicability of the method by recalibrating 31 different public MSI datasets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations, especially the high-confident annotations at a low false discovery rate.</p>

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

“…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.…”

“…When using endogenous signals as a reference, lock masses can be spectrum-specific ( µ = µ i ) (La Rocca et al, 2021) or be defined globally. Our method uses the latter approach.…”

“…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).…”

“…(Boskamp et al, 2020) Recently, La Rocca et al have presented an algorithm to recalibrate MSI datasets using a linear fit on the observed mass errors from endogenous biological peaks. (La Rocca et al, 2021) In their method, they treated each spectrum as independent.…”