Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).多模态质谱成像（Multimodal Mass Spectrometry Imaging, MSI）是一项核心技术，通过集成多种质谱成像平台，以获取单分析技术难以实现的样本全范围分子信息，从而深入解析生物系统。本研究旨在构建一种多模态质谱成像方法：通过联用用于代谢组学质谱成像的红外基质辅助激光解析电喷雾电离（IR-MALDESI）技术，与用于空间分辨蛋白质组分析的单管纳滴处理微量样品（nanoPOTS）液相色谱-串联质谱（LC-MS/MS）技术，从单个生物器官中同步获取代谢组与蛋白质组数据。 研究采用两种平台分别分析大鼠大脑的相邻组织切片，并依托前期优化的分析流程对各组数据进行独立处理。其中，IR-MALDESI数据集通过精确质量匹配与谱图准确性验证，结合HMDB、METLIN及LipidMaps数据库完成分子注释；nanoPOTS-LC-MS/MS数据集则采用Sequest HT算法比对大鼠蛋白质组数据库，并以1%的假发现率（False Discovery Rate, FDR）进行过滤。 整合后的数据集揭示了互补的分子特征，可有效区分胼胝体与大脑其他采样区域。多组学通路整合分析结果表明，在对比各脑区代谢物及其对应酶的平均丰度时，两组数据集呈现出显著相关性。本研究为构建结合两种高灵敏度且互补成像平台的多模态质谱成像技术迈出了关键第一步。原始数据文件可在METASPACE（https://metaspace2020.eu/project/pace-2021）及MassIVE（标识符：MSV000088211）中获取。