Ensuring sample and data quality is crucial for reliable results in clinical metabolomics. However, strict sample stability protocols often conflict with practical clinical considerations. Additionally, integrating datasets with varying nomenclature, selectivity, or quantification strategies poses significant challenges. In this talk, I will highlight common pitfalls in the clinical metabolomics workflow and introduce useful tools to address these issues. This presentation is aimed at scientists involved in clinical projects who seek guidance on pre-analytical and dataset integration challenges.

Although they can be measured with similar analytical methods, lipidomics and metabolomics datasets are often analyzed differently when it comes to statistics. These differences are primarily justified by the biology behind the molecules. In this talk, I will discuss specifically how the nature and structure of standardized lipidomic datasets influences data interpretation and sharing down the line. I’ll introduce tools and approaches to explore lipidomics, and discuss tips and tricks to make the most of this unique type of data. This talk will be especially aimed at scientists who are unsure how to analyze lipidomics and are looking for guidance.