FT-ICR Mass Spectrometry Imaging at Extreme Mass Resolving Power Using a Dynamically Harmonized ICR Cell with 1ω or 2ω Detection.

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.

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

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 (https://github.com/LaRoccaRaphael/MSI_recalibration). 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 shift in mass spectrometry imaging: comprehensive analysis and practical corrective workflow.

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.

Rapid visualization of lipopeptides and potential bioactive groups of compounds by combining ion mobility and MALDI imaging mass spectrometry.

Mass spectrometry imaging (MSI) has become a powerful method for mapping metabolite distribution in a tissue. Applied to bacterial colonies, MSI has a bright future, both for the discovery of new bioactive compounds and for a better understanding of bacterial antibiotic resistance mechanisms. Coupled with separation techniques such as ion mobility mass spectrometry (IM-MS), the identification of metabolites directly on the image is now possible and does not require additional analysis such as HPLC-MS/MS. In this article, we propose to apply a semi-targeted workflow for rapid IM-MSI data analysis focused on the search for bioactive compounds. First, chemically-related compounds showing a repetitive mass unit (i.e. lipids and lipopeptides) were targeted based on the Kendrick mass defect analysis. The detected groups of potentially bioactive compounds were then confirmed by fitting their measured ion moibilites to their measured m/z values. Using both their m/z and ion mobility values, the selected groups of compounds were identified using the available databases and finally their distribution was observed on the image. Using this workflow on a co-culture of bacteria, we were able to detect and localize bioactive compounds involved in the microbial interaction.

Multi-Enzymatic Limited Digestion: The Next-Generation Sequencing for Proteomics?

Over the past 40 years, proteomics, generically defined as the field dedicated to the identification and analysis of proteins, has tremendously gained in popularity and potency through advancements in genome sequencing, separative techniques, mass spectrometry, and bioinformatics algorithms. As a consequence, its scope of application has gradually enlarged and diversified to meet specialized topical biomedical subjects. Although the tryptic bottom-up approach is widely regarded as the gold standard for rapid screening of complex samples, its application for precise and confident mapping of protein modifications is often hindered due to partial sequence coverage, poor redundancy in indicative peptides, and lack of method flexibility. We here show how the synergic and time-limited action of a properly diluted mix of multiple enzymes can be exploited in a versatile yet straightforward protocol to alleviate present-day drawbacks. Merging bottom-up and middle-down ideologies, our results highlight broad assemblies of overlapping peptides that enable refined and reliable characterizations of proteins, including variant identification, and their carried modifications, including post-translational modifications, truncations, and cleavages. Beyond this boost in performance, our methodology also offers efficient de novo sequencing capabilities, in view of which we here present a dedicated custom assembly algorithm.

Exploiting Differential Signal Filtering (DSF) and Image Structure Filtering (ISF) Methods for Untargeted Mass Spectrometry Imaging of Bacterial Metabolites.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a label-free technique, producing images where pixels contain mass spectra. The technique allows the visualization of the spatial distribution of (bio)molecules from metabolites to proteins, on surfaces such as tissues sections or bacteria culture media. One particularly exciting example of MALDI-MSI use rests on its potential to localize ionized compounds produced during microbial interactions and chemical communication, offering a molecular snapshot of metabolomes at a given time. The huge size and the complexity of generated MSI data make the processing of the data challenging, which requires the use of computational methods. Despite recent advances, currently available commercial software relies mainly on statistical tools to identify patterns, similarities, and differences within data sets. However, grouping m/z values unique to a given data set according to microbiological contexts, such as coculture experiments, still requires tedious manual analysis. Here we propose a nontargeted method exploiting the differential signals between negative controls and tested experimental conditions, i.e., differential signal filtering (DSF), and a scoring of the ion images using image structure filtering (ISF) coupled with a fold change score between the controls and the conditions of interest. These methods were first applied to coculture experiments involving Escherichia coli and Streptomyces coelicolor, revealing specific MS signals during bacterial interaction. Two case studies were also investigated: (i) cellobiose-mediated induction for the pathogenicity of Streptomyces scabiei, the causative agent of common scab on root and tuber crops, and (ii) iron-repressed production of siderophores of S. scabiei. This report proposes guidelines for MALDI-MSI data treatment applied in the case of microbiology contexts, with enhanced ion peak annotation in specific culture conditions. The strengths and weaknesses of the methods are discussed.