Mass Spectrometry Laboratory, MolSys Research Unit, University of Liège, 4000 Liège, Belgium.
InBioS - Center for Protein Engineering, University of Liège, 4000 Liège, Belgium.
J Am Soc Mass Spectrom. 2024 Aug 7;35(8):1743-1755. doi: 10.1021/jasms.4c00129. Epub 2024 Jul 15.
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 / 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 and , revealing specific MS signals during bacterial interaction. Two case studies were also investigated: (i) cellobiose-mediated induction for the pathogenicity of , the causative agent of common scab on root and tuber crops, and (ii) iron-repressed production of siderophores of . 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.
基质辅助激光解吸/电离（MALDI）质谱成像（MSI）是一种无标记技术，可生成包含质谱的像素图像。该技术允许可视化（生物）分子在表面（如组织切片或细菌培养基）上的空间分布，从代谢物到蛋白质。MALDI-MSI 的一个特别令人兴奋的应用例子在于它能够定位微生物相互作用和化学通讯过程中产生的离子化化合物，提供特定时间代谢组的分子快照。生成的 MSI 数据的巨大规模和复杂性使得数据处理具有挑战性，这需要使用计算方法。尽管最近取得了进展，但当前可用的商业软件主要依赖于统计工具来识别数据集内的模式、相似性和差异。然而，根据微生物学背景（如共培养实验）对数据集进行分组/值的独特性，仍然需要繁琐的手动分析。在这里，我们提出了一种非靶向方法，利用负对照和测试实验条件之间的差异信号，即差异信号过滤（DSF），以及使用图像结构过滤（ISF）对离子图像进行评分，并结合对照和感兴趣条件之间的倍数变化评分。这些方法首先应用于涉及 和 的共培养实验，揭示了细菌相互作用过程中的特定 MS 信号。还研究了两个案例研究：（i）纤维二糖介导的致病性诱导，是根茎作物普通疮痂病的病原体，和（ii）铁抑制产铁载体。本报告提出了在微生物学背景下应用 MALDI-MSI 数据处理的指南，在特定培养条件下增强了离子峰注释。讨论了这些方法的优缺点。
