Mass Spectrometry & Spectroscopy


Unlocking the complexity of metabolomics: Pushing the frontiers of targeted and untargeted methodologies


Jason Causon, SCIEX


Metabolomics is vital for understanding disease and enabling precision medicine by identifying biomarkers for diagnosis, prognosis, and predicting patient-specifi c treatment responses [1,2]. It also aids drug discovery by elucidating metabolic pathways, reducing toxicology costs, and improving trial design and patient selection [2].


Metabolites are numerous, highly diverse, and span a wide range of concentrations [1,3], creating analytical challenges. Capturing only abundant metabolites risks missing subtle but critical disease signatures. This complexity is key to understanding health and disease [3]. For instance, bladder cancer shows altered TCA cycle and fatty acid metabolism [1]; liver cancer exhibits changes in amino acid, bile acid, choline, fatty acid metabolism, and glycolysis [1]; obesity involves disruptions in glycolysis, TCA cycle, urea cycle, and glutathione metabolism [1]; and Alzheimer’s disease shows alterations in amino acid, fatty acid, linoleic acid, cholesterol, glycine, serine, aspartate, glycerophospholipid, and polyamine metabolism [1].


Mass spectrometry powers metabolomics


Liquid chromatography tandem mass spectrometry (LC-MS/MS) is indispensable for quantifying and characterising metabolites, but challenges remain in fragmenting and capturing enough MS2 ions for confi dent annotation [3]. The choice between data- dependent acquisition (DDA) or data-independent acquisition (DIA) can also be limited by scarce samples.


Collision-induced dissociation (CID) remains the backbone of metabolomics and lipidomics [4,5,6,7,8]. In CID, precursor ions collide with neutral gas, breaking weaker bonds (e.g., C–O) [6], yielding spectra useful for identifying functional groups like ethanolamine, phosphatidic, or glucuronic conjugates and for classifying metabolites such as lipids [6]. CID can provide limited structural data, such as fatty acyl composition [7,6,9], its strengths are speed, sensitivity and versatility [6].


However, CID cannot usually cleave stronger C–C bonds, leaving positional isomers, stereochemistry, and double bond locations unresolved [7,6,9]. This restricts its ability to distinguish regiospecifi c fatty acyls or structural subtleties, and in complex mixtures it biases toward intense ions, masking low-abundance but important metabolites [5,6,9,10].


Electron-activated dissociation (EAD) overcomes these limits, revealing stereochemistry and double bond positions critical to metabolomics and lipidomics [1,5,6,9,10,11]. By using tuneable energy electrons instead of collisions, EAD generates complementary cleavages [6,9], producing spectra rich in structural information [6,9]. It can identify subtle features in minor metabolites that CID misses, offering signifi cant advances for structural characterisation [3,11,10].


Historically, the drawbacks to electron fragmentation have been speed and sensitivity: scan times of up to hundreds of milliseconds were required, limiting high-throughput use [3]. Multiple double bonds needed longer acquisition or greater sensitivity before decay [3]. Even so, EAD provides an orthogonal, high-resolution complement to CID, enabling the structural detail necessary to connect chemistry and biology [3,11].


Data acquisition for a complete picture of metabolomics


But fragmentation is only part of the story: equally important is how ions are selected— via DDA or DIA. In DDA, the instrument fragments the most intense MS1 ions without prior hypotheses, producing high-quality spectra and excelling for abundant metabolites or targeted workfl ows [3,8,12]. Yet with advances in DDA algorithms, it typically misses lower-intensity ions, suffers from stochastic selection and run-to-run variability, and produces incomplete datasets biased toward higher-abundant species [3,8,12].


DIA instead fragments all detectable ions across sequential m/z windows, producing reproducible, comprehensive datasets that include low-abundance metabolites [3,13]. However, DIA data are voluminous and noisy in complex samples, which limits the dynamic range [14]. To address this, SWATH DIA introduced overlapping Q1 windows, correlating fragments to precursors at each time point [14,15]. Still, conventional DIA suffered from low duty cycles and broad Q1 windows, with only 5–25% ion utilisation [16]. The Zeno trap when enabled raised this above 90%, boosting sensitivity four- to twenty-fold while preserving precision [16,17].


Yet DIA in metabolomics faces redundancy: many small molecules fragment to the same ions. For instance, choline-containing lipids, such as phosphatidylcholine and sphingomyelin, generate the highly effi cient diagnostic phosphocholine product ion at m/z 184 (i.e., the protonated phosphocholine headgroup) in positive-ion MS/MS. In DDA, pairing this fragment with an isolated precursor m/z cleanly confi rms the choline- lipid class for that specifi c precursor. But in DIA, multiple co-isolated precursors within a window can simultaneously contribute to the same m/z 184 signal, obscuring which phosphatidylcholine or sphingomyelin species—and which fatty-acyl composition or isomer—produced it [3,18,19]. This redundancy complicates unambiguous identifi cation and quantitation, underscoring the need for narrow Q1 windows and high scan speed in metabolomics DIA [3]. DIA thus requires both high speed and narrow Q1 windows [3]. Ultimately, DDA and DIA are complementary: DDA provides clean spectra for abundant metabolites, while DIA ensures broad coverage of scarce but signifi cant molecules, though running both remains time-consuming and sample-intensive [3].


A transformative new platform for high- throughput metabolomics


To address many challenges in metabolomics and other omics analyses, a new MS system has been introduced with radically improved capabilities that promise to transform the study of metabolomics, lipidomics (and proteomics). Prof. Dr. Nicola Zamboni’s group at ETH Zürich studies metabolic regulation, cellular decisions, and pathological states, using cutting-edge MS/MS to reveal metabolite complexity and support personalised health research [4,3,4,20]. Zamboni emphasises that biologically critical metabolites are often of low-abundance, requiring advanced acquisition strategies and instrumentation. In general, the ZenoTOF 8600 system, with innovations like the OptiFlow Pro ion source, DJet ion guide and QJet ion guide, and the Zeno trap, improves ion generation, transmission, and detection, yielding a tenfold sensitivity increase over the ZenoTOF 7600+ system [21]. This enhances peak intensity tenfold and S/N ratio 12.9-fold, detecting more features in complex samples [21]. Indeed, metabolomics experiments showing up to 30-fold sensitivity gains and lipidomics revealing 2–3 times more features (see Figure 1) [3,11]. Spectra are exceptionally clean, enabling precise denoising, deisotoping, and peak detection [3,11].


Figure 1: To verify the impact on detection and identifi cation of complex samples, Zamboni and his team analysed a lipid extract by DDA (positive-ion mode, 15-minute reverse phase LC, top-10, 5 ms accumulation per MS²). They observed a two- to three-fold increase in the number of detected features, and a 50% increase in putatively MS2-annotated lipids ion by matching against a library of theoretical MS²-fragments (LipidOracle), without any apparent loss in match quality [11].


INTERNATIONAL LABMATE - NOVEMBER 2025


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