2024-W3: Untargeted Metabolomics, Mol. Networking/Automatic Spectral Library Generation in MZmine

Presenters: ▪ Tomáš Pluskal (IOCB Prague) ▪ Dr. Robin Schmid (IOCB Prague) ▪ Dr. Corinna Brungs (IOCB Prague) ▪ Dr. Tito Damiani (IOCB Prague) ▪ Dr. Louis-Felix Nothias (Université Côte d'Azur) Description: Untargeted mass spectrometry (MS) experiments produce complex, multi-dimensional data that are practically impossible to investigate manually. Moreover, only a small fraction of the detected metabolites can be annotated, leaving a vast unknown chemical space to explore. This creates the need for flexible and scalable software solutions for MS data processing. MZmine is a modular platform-independent software with a vibrant opensource community. Recent contributions have expanded its capabilities to support various MS platforms, including chromatography-MS and MS imaging, combined with ion mobility spectrometry data. The interactive graphical user interface facilitates data exploration and results validation at each processing step. In addition, the MZmine Processing Wizard (MZwizard) introduces easy-to-configure workflows for metabolomics and lipidomics research that can be scaled to process large studies of thousands of samples. This hands-on workshop will introduce the MZwizard, feature detection, compound annotation and molecular networking in MZmine, and existing bridges to GNPS and SIRIUS. We will also describe a novel mass spectral library generation workflow, including our efforts to expand open reference data. Using high-throughput analytical methods, we acquired multi-stage fragmentation spectra of 20,000 unique compounds in both polarities within 12 days in 4740 injections. Workshop Objectives: ▪ Introducing untargeted metabolomics LC-MS/MS data processing in MZmine 3 with the MZwizard: Feature detection, compound annotation, and molecular networking ▪ Introducing MZmine tools for automatic generation of mass spectral libraries to advance open data sharing ▪ Exporting results from MZmine to interface other tools like SIRIUS and GNPS Learning Outcomes: ▪ Getting an overview of MS data processing in MZmine by processing an example dataset ▪ Optimizing individual steps and understanding the distribution and shape of MS data ▪ Getting a clear workflow to acquire and generate open reference data