Tandem Mass Spectrometry across Platforms.

PubChem serves as a comprehensive repository, housing over 100 million unique chemical structures representing the breadth of our chemical knowledge across numerous fields including metabolism, pharmaceuticals, toxicology, cosmetics, agriculture, and many more. Rapid identification of these small molecules increasingly relies on electrospray ionization (ESI) paired with tandem mass spectrometry (MS/MS), particularly by comparison to genuine standard MS/MS data sets. Despite its widespread application, achieving consistency in MS/MS data across various analytical platforms remains an unaddressed concern. This study evaluated MS/MS data derived from one hundred molecular standards utilizing instruments from five manufacturers, inclusive of quadrupole time-of-flight (QTOF) and quadrupole orbitrap "exactive" (QE) mass spectrometers by Agilent (QTOF), Bruker (QTOF), SCIEX (QTOF), Waters (QTOF), and Thermo QE. We assessed fragment ion variations at multiple collisional energies (0, 10, 20, and 40 eV) using the cosine scoring algorithm for comparisons and the number of fragments observed. A parallel visual analysis of the MS/MS spectra across instruments was conducted, consistent with a standard procedure that is used to circumvent the still prevalent issue of mischaracterizations as shown for dimethyl sphingosine and C20 sphingosine. Our analysis revealed a notable consistency in MS/MS data and identifications, with fragment ions' m/z values exhibiting the highest concordance between instrument platforms at 20 eV, the other collisional energies (0, 10, and 40 eV) were significantly lower. While moving toward a standardized ESI MS/MS protocol is required for dependable molecular characterization, our results also underscore the continued importance of corroborating MS/MS data against standards to ensure accurate identifications. Our findings suggest that ESI MS/MS manufacturers, akin to the established norms for gas chromatography mass spectrometry instruments, should standardize the collision energy at 20 eV across different instrument platforms.

Microbial Metabolite 3-Indolepropionic Acid Mediates Immunosuppression.

The microbial-derived metabolite, 3-indolepropionic acid (3-IPA), has been intensely studied since its origins were discovered in 2009; however, 3-IPA's role in immunosuppression has had limited attention. Untargeted metabolomic analyses of T-cell exhaustion and immunosuppression, represented by dysfunctional under-responsive CD8+ T cells, reveal a potential role of 3-IPA in these responses. T-cell exhaustion was examined via infection of two genetically related mouse strains, DBA/1J and DBA/2J, with lymphocytic choriomeningitis virus (LCMV) Clone 13 (Cl13). The different mouse strains produced disparate outcomes driven by their T-cell responses. Infected DBA/2J presented with exhausted T cells and persistent infection, and DBA/1J mice died one week after infection from cytotoxic T lymphocytes (CTLs)-mediated pulmonary failure. Metabolomics revealed over 70 metabolites were altered between the DBA/1J and DBA/2J models over the course of the infection, most of them in mice with a fatal outcome. Cognitive-driven prioritization combined with statistical significance and fold change were used to prioritize the metabolites. 3-IPA, a tryptophan-derived metabolite, was identified as a high-priority candidate for testing. To test its activity 3-IPA was added to the drinking water of the mouse models during LCMV Cl13 infection, with the results showing that 3-IPA allowed the mice to survive longer. This negative immune-modulation effect might be of interest for the modulation of CTL responses in events such as autoimmune diseases, type I diabetes or even COVID-19. Moreover, 3-IPA's bacterial origin raises the possibility of targeting the microbiome to enhance CTL responses in diseases such as cancer and chronic infection.

Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry.

Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91-110% for the amino acids, 76-129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.

Cognitive analysis of metabolomics data for systems biology.

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.