Kendrick mass defect (KMD) analysis is widely used for helping the detection and identification of chemically related compounds based on exact mass measurements. We report here the use of KMD as a criterion for filtering complex mass spectrometry dataset. The method enables an automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of family of homologous compounds from MSI images. We show that the KMD filtering, based on an in-house software, is suitable and robust for high resolution (full width at half-maximum, FWHM, at m/z 410 of 20 000) and very high-resolution (FWHM, at m/z 410 of 160 000) MSI data. This method has been successfully applied to two different types of samples, bacteria co-cultures and brain tissue section.

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.

MALDI mass spectrometry imaging (MALDI MSI) is a powerful analytical method for achieving 2D localization of compounds from thin sections of typically but not exclusively biological samples. The dynamically harmonized ICR cell (ParaCell) was recently introduced to achieve extreme spectral resolution capable of providing the isotopic fine structure of ions detected in complex samples. The latest improvement in the ICR technology also includes 2ω detection, which significantly reduces the transient time while preserving the nominal mass resolving power of the ICR cell. High-resolution MS images acquired on FT-ICR instruments equipped with 7T and 9.4T superconducting magnets and the dynamically harmonized ICR cell operating at suboptimal parameters suffered severely from the pixel-to-pixel shifting of m / z peaks due to space-charge effects. The resulting profile average mass spectra have depreciated mass measurement accuracy and mass resolving power under the instrument specifications that affect the confidence level of the identified ions. Here, we propose an analytical workflow based on the monitoring of the total ion current to restrain the pixel-to-pixel m / z shift. Adjustment of the laser parameters is proposed to maintain high spectral resolution and mass accuracy measurement within the instrument specifications during MSI analyses. The optimized method has been successfully employed in replicates to perform high-quality MALDI MS images at resolving power (FWHM) above 1,000,000 in the lipid mass range across the whole image for superconducting magnets of 7T and 9.4T using 1 and 2ω detection. Our data also compare favorably with MALDI MSI experiments performed on higher-magnetic-field superconducting magnets, including the 21T MALDI FT-ICR prototype instrument of the NHMFL group at Tallahassee, Florida.

MALDI mass spectrometry imaging (MSI) allows the mapping and the tentative identification of compounds based on their m/z value. In typical MSI, a spectrum is taken at incremental 2D coordinates (pixels) across a sample surface. Single pixel mass spectra show the resolving power of the mass analyzer. Mass shift, i.e., variations of the m/z of the same ion(s), may occur from one pixel to another. The superposition of shifted masses from individual pixels peaks apparently degrades the resolution and the mass accuracy in the average spectrum. This leads to low confidence annotations and biased localization in the image. Besides the intrinsic performances of the analyzer, the sample properties (local composition, thickness, matrix deposition) and the calibration method are sources of mass shift. Here, we report a critical analysis and recommendations to mitigate these sources of mass shift. Mass shift 2D distributions were mapped to illustrate its effect and explore systematically its origin. Adapting the sample preparation, carefully selecting the data acquisition settings, and wisely applying post-processing methods (i.e., m/z realignment or individual m/z recalibration pixel by pixel) are key factors to lower the mass shift and to improve image quality and annotations. A recommended workflow, resulting from a comprehensive analysis, was successfully applied to several complex samples acquired on both MALDI ToF and MALDI FT-ICR instruments.

<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>