Detecting Stable Isotope Labeled Metabolites in Untargeted Analysis Using a Benchtop Orbitrap Mass Spectrometer and a Single Processing Software Platform

Significance of the Topic

A robust, untargeted metabolomics workflow capable of detecting stable isotope labeled metabolites in a single experiment addresses critical needs in pathway mapping and flux analysis. By integrating high-resolution mass spectrometry and advanced data processing, researchers can uncover unexpected metabolic routes and improve confidence in compound identification.

Objectives and Overview

The study aimed to develop an end-to-end workflow for stable isotope labeling (SIL) in untargeted metabolomics. Key goals included:

• Simultaneous data acquisition and analysis of unlabeled and 13C-labeled metabolites.
• Confident assignment of elemental formulas via accurate mass measurements.
• Detection of both expected and unanticipated labeled metabolites in Escherichia coli extracts.

Methodology

Sample Preparation involved combining dried E. coli extracts from Cambridge Isotope Laboratories in defined ratios (0%, 10%, 25%, 50%, 100% 13C). Extracts were reconstituted in 0.1% formic acid prior to analysis.

Liquid Chromatography–Mass Spectrometry separation used a Thermo Scientific Hypersil GOLD column (1.9 μm, 150×2.1 mm) on a Vanquish UHPLC system with a 15 min gradient (water/acetonitrile, both with 0.1% formic acid) at 45 °C.

Data Acquisition employed an Orbitrap Exploris 240 mass spectrometer in positive mode across m/z 67–1000, at resolutions of 30K, 60K, and 120K. Internal calibration (Easy-IC) and AcquireX intelligent acquisition provided full scan and MS/MS spectra (stepped HCD at 30/50/150%).

Used Instrumentation

• Thermo Scientific Vanquish UHPLC
• Hypersil GOLD column (150×2.1 mm, 1.9 μm)
• Thermo Scientific Orbitrap Exploris 240 MS
• Thermo Scientific Compound Discoverer software
• ChemSpider and mzCloud libraries for annotation

Main Results and Discussion

High-resolution settings (<1 ppm mass error) enabled confident formula predictions (e.g., adenosine C10H13N5O4 at m/z 268.10385).

Isotopologue Detection showed clear separation of M+0 to M+n peaks in uracil, tryptophan, and other metabolites across defined 13C ratios, with relative exchanges matching expected values and low %CV across triplicate injections.

Unknown Annotation capabilities identified unexpected 13C incorporation in cytosine, uracil, and a putative tyrosine methyl ester, demonstrating the workflow’s power to reveal unanticipated metabolic products.

Benefits and Practical Applications

• Comprehensive tracer and flux analysis without preselection of target pathways.
• Enhanced confidence in metabolite identification through accurate mass and isotopic fine structure.
• Ability to discover novel or unexpected labeled compounds in complex samples.

Future Trends and Potential Applications

Anticipated developments include integrating multiple isotope labels (15N, 18O), coupling with machine-learning for automated pathway inference, expanding to higher-throughput platforms, and combining with multi-omics datasets for holistic systems biology studies.

Conclusion

This work established a streamlined, untargeted SIL metabolomics workflow using benchtop Orbitrap technology and a unified processing platform. It delivers reliable isotopologue detection, formula assignment, and discovery of novel labeled metabolites in a single experiment.