A Step-by-Step Protocol From METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, its multidimensional nature and large data outputs make it challenging to extract meaningful biological insights. Advancements such as the METASPACE platform allow researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and a cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and translate them into practical biological insights, particularly in complex systems. This approach has broad potential applications, including diagnostics, drug discovery, environmental, and ecological research. We envision this pipeline will be particularly useful for newcomers to MSI and encourage experienced users to customize and extend it to meet more advanced analytical needs.