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

revised on 01.12.2020, 22:26 and posted on 03.12.2020, 06:14 by Raphaël La Rocca, Christopher Kune, Mathieu Tiquet, Lachlan Stuart, Gauthier Eppe, Theodore Alexandrov, Edwin De Pauw, Loïc Quinton

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 heterogeneous molecular composition of 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 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 datasets 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.


European Union’s Horizon 2020 research and innovation program No. 731077 (EU FT-ICR MS project, INFRAIA-02-2017)

European Union and Wallonia program FEDER BIOMED HUB Technolohy Support No. 2.2.1/996

European Union’s Horizon 2020 program (EURLipids Interreg Eurogio Meuse-Rhine project supported by the European Regional Development Fund (FEDER))


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University of Liège ULiège



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Declaration of Conflict of Interest

no conflict of interest