2024-W10: Improving Data Analysis for Ambient Ionisation and Direct Infusion MS-based Metabolomics

Presenters: ▪ Dr. Nicholas Birse (Queen’s University Belfast, UK) ▪ Dr. Vera Plekhova (Ghent University, Belgium) ▪ Ir. Nicolas Defooz (Ghent University, Belgium) ▪ Prof. Lynn Vanhaecke (Ghent University, Belgium) Description: The speed and simplicity of ambient ionization and direct infusion mass spectrometry (AIMS-DIMS) platforms have made them increasingly popular methods for rapid sample screening. However, data handling techniques have become inconsistent as vendor-specific tools are replaced by custom processing pipelines that best fit specific workflows. While tailored approaches aim to leverage the speed and versatility of AIMS-DIMS, they complicate data reproducibility and validation, which is already challenging throughout various instrumental configurations. The workshop will host an expert panel, well-versed in both vendor and non-vendor data processing tools and pathways. They will discuss the features and utility of their toolset, as well as strategies for improving the reproducibility of their workflows. The level will be appropriate for a broad range of expertise, from graduate students to experienced investigators. To target emerging topics of interest the workshop will include lightning talks from speakers and posters presenters participating in the conference and representatives from both academia and instrument and software vendors. There will be a Question-and-Answer session with the panel. The workshop will conclude with a set of practical outcomes and recommendations to mass spectrometry users, instrument and software developers, on how to advance the application of AIMS-DIMS within the metabolomics area. Workshop Objectives: ▪ Quality control for AIMS-DIMS-based metabolomics: developing strategies to ensure data acquisition is robust and comparable across instruments and platforms ▪ Data processing: investigating ways to ensure data processing is directly comparable when using different software packages and programs, including strategies relying on direct extraction from raw files (no peak picking) ▪ Reproducibility: assessing strategies for making data reproducible both within the same laboratory and across multiple laboratories considering the prior 2 objectives and other considerations necessary for AIMS-DIMS-based metabolomics platforms Learning Outcomes: ▪ System setup and acquisition: participants are expected to understand existing and emerging strategies for evaluating and ensuring appropriate setup, calibration and stability of AIMS-DIMS platforms throughout the analysis as a quality assurance measure for the acquired metabolomic data ▪ Data analysis: participants are expected to understand ways of processing AIMS-DIMS data with different software packages, aiming for consistent results and outcomes. Similarly they should develop a greater understanding of where software output may diverge and the challenges related to maintaining reproducibility ▪ Reproducibility: participants are anticipated to comprehend the concept of reproducibility and how to obtain high levels of reproducibility during both AIMS-DIMS metabolomics data acquisition and processing and how to maintain reproducible results across different laboratories and data sets where relevant