50 Million euro investment for LUMC spin-off RougeTx advances therapies for inherited bleeding disorders.
Researchers from the LUMC Center for Proteomics and Metabolomics (CPM) at reNEW Leiden have published four scientific articles on improved methods and analysis for the study of metabolites. The articles were published in Nature Methods, Nature Metabolism (1 & 2) and Molecular Systems Biology and provide valuable tools and resources for researchers in the field of metabolomics.
Metabolites are crucial regulators of cellular biology and their comprehensive study through metabolomics is essential to uncover novel insights into cellular function during health and disease. Mass spectrometry – a powerful technique that measures the mass to charge ratio of ions – plays a pivotal role in these studies. However, identifying and characterizing metabolites has remained a persistent challenge. “Identifying metabolites from mass spectrometry data is sometimes like a forensic investigation. Experiments provide clues, but connecting them can be challenging – especially when dealing with poorly characterized or previously unknown compounds,” says Martin Giera, reNEW facility head and group leader at the CPM. The center is a leading technological innovator, offering advanced services and expertise in the field of omics-analysis.
Improving METLIN
To advance the study of metabolites, several tools have previously been developed that allow their identification. One of the most notable is METLIN: the largest mass spectrometry database currently available, holding experimental data of approximately 1 million molecules. To maximize METLIN’s potential for metabolite identification, researchers from the reNEW facility, in close collaboration with the Metabolomics Center at the Scripps Research Institute have integrated the database with the XCMS informatics platform (now accessible through a unified interface – here). Giera: “By combining the strengths of these two technologies, we significantly improve the accuracy of molecular identifications. This is a critical step in advancing metabolomics into the realm of digital biology—similar to genomics and transcriptomics—and unlocking its full potential”
In a further effort to improve metabolite identification, the researchers expanded the comprehensive METLIN database by incorporating new data types. “We added two datatypes – collision cross section (CCS) values and Ion mobility spectrometry (IMS) data – to METLIN for 750 unique lipid standards. These datatypes are increasingly popular and are paramount to distinguish between different lipids that have the same molecular weight,” says Giera. He continues: “We thereby provide a reliable dataset that will not only strengthen lipid annotation using traditional technologies but will also be crucial for on-tissue lipid annotation during mass spectrometry imaging analysis.”
Unknown or misleading?
Despite advances in annotation and analysis – such as those provided by the CPM mentioned in the studies above – other issues remain in the identification of metabolites. Giera: “Over the years, mass spectrometry experiments have uncovered a substantial number of unidentified spectral features, sparking debate about how many metabolites exist in nature. Data currently suggest thousands to millions of metabolites potentially remain to be discovered and annotated.” In a recent Nature Metabolism report, Giera and colleagues provide new insights into this issue. “We found that many of these ‘ghost’ features result from a technological artifact called in-source fragmentation (ISF) rather than representing actual biochemical phenomena. ISF relates to the fragmentation of analytes during the initial ionization process, before they reach the collision cell. ISF basically generates a forest from a tree, meaning a single analyte can be presented as a molecular ion and one or many extra fragments,” says Giera. He warns that these results have strong implications for metabolomics research: “Do not forget to consider in-source fragmentation if you are hunting for seemingly unknown metabolites. You could be chasing a technical artifact rather than a real metabolite!”
Metabolism in context
In a fourth effort, the CPM published a new method in Nature Methods to push boundaries in spatial metabolomics. The method allows the integration of two spatial analysis techniques on a single tissue section. Giera: “We developed a new method for performing mass cytometry and mass spectrometry imaging on the same tissue section. This allows for the spatial analysis of both cellular markers and metabolic profiles on-tissue at near single cell level.” Illustrating the potential of the new method, the researchers applied their new method to perform metabolic analysis of 22 different cell types in the tumor microenvironment. “Tumor tissue and specifically the tumor microenvironment is very diverse comprising several different cell types with wide-ranging metabolic wiring. To dissect such intricacies, new technologies such as the method we developed are key in enhancing our understanding of cellular interactions on a metabolic level. Our development is not constrained to tumor tissue but can be readily applied to other biological settings as for example neurodegeneration.”
About the CPM
The CPM is a technological core innovator and service provider specializing in the analysis of proteins, metabolites, lipids, and glycans from diverse biological materials, including cells and tissues. Mass spectrometry serves as the primary technology at CPM, enhanced by nuclear magnetic resonance (NMR) and advanced miniaturized separation systems. The facility is based at the Leiden University Medical Center and is one of reNEW’s research facilities. Read more about the CPM here.
Read more
Phantom Metabolites? | Science | AAAS
XCMS-METLIN: data-driven metabolite, lipid and chemical analysis
