FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry

Abstract

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

Identification of novel biomarkers of hepatocellular carcinoma by high‐definition mass spectrometry: Ultrahigh‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry and desorption electrospray ionization mass spectrometry imaging

“…20,21 The metabolites were generally identified by the matching of mass spectra and fragment ions with high accuracy to theoretical values in a database and matching of the retention times of the chromatogram to chemical standards, 22,23 which defined the quality of identification. 24,25 However, there were still isomeric and isobaric species, such as lipids. High-definition mass spectrometry (HDMS) is a technology that relies on the mobility of ions, including small molecules, according to the charge, shape, and size in the gas phase inside the MS instrument.…”

“…The metabolites were generally identified by the matching of mass spectra and fragment ions with high accuracy to theoretical values in a database and matching of the retention times of the chromatogram to chemical standards, which defined the quality of identification . However, there were still isomeric and isobaric species, such as lipids.…”

Metabolism in time and space – exploring the frontier of developmental biology

“…With these goals in mind, the meeting highlighted a number of technological advances that will significantly expand the developmental biologists' metabolic toolkit. For example, Kai Johnsson (EPFL, Lausanne, Switzerland) described fluorescent protein sensors that allow for real-time metabolite measurements, while Theodore Alexandrov (EMBL, Heidelberg, Germany) explained how emerging metabolomic techniques can visualize the spatial distribution of metabolites within developing tissues (Palmer et al, 2017). Such advances in metabolomics, metabolite imaging, and the modeling of metabolic networks are providing new opportunities to study the biochemical forces that shape growth and development.…”

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

“…Rapid development and growing popularity of imaging MS, as well as the high dimensionality and sheer size of generated data, measuring up to hundreds of gigabytes for a tissue section, have stimulated the development of computational methods and software (Alexandrov, 2012) . Various methods have been developed for low-dimensional data representation (based on PCA, NMF, t-SNE, bi-clustering), finding spatial regions of interest with spatial segmentation, search for markers associated with a region of interest, and, recently, for metabolite annotation (Palmer et al , 2017) . Many of these methods use some measure of spatial similarity between ion images, often referred to as spatial co-localization .…”