Abstract

Translating Big Data from HR Imaging MS Data into Molecular Knowledge

Theodore Alexandrov, Team Leader, EMBL Heidelberg

Spatial metabolomics is emerging as a powerful approach to localize hundreds of metabolites directly from sections of biological samples with the grand challenge to be in the molecular annotation of big data generated. Existing bioinformatics tools cannot be applied directly because of the sheer data size and high complexity of spectra. We developed algorithms for molecular annotation for High Resolution Imaging Mass Spectrometry that integrate both spectral and spatial filters and map the results onto metabolic pathways. We will present our efficient implementation using modern big data technologies and apply it to 3D cell spheroids, microbial agar plates, and biological tissues. We will discuss the mass spectrometry as well as algorithmic challenges. One major challenge that we addressed is the formulation of a False Discovery Rate that would be applicable in the spatial metabolomics setting. We also present the evaluation of the proposed FDR. Finally, we present how these algorithms and online engine will be integrated into METASPACE, a novel European project on Bioinformatics for Spatial Metabolomics.