It is increasingly recognized that the 'omic analysis of whole blood has applications for precision medicine and disease phenotyping. Despite this realization, whole blood is generally viewed as a challenging analytical matrix in comparison to plasma or serum. Moreover, proteomic analyses of whole blood have almost exclusively focused on (non)targeted analyses of protein abundances and much less on post-translational modifications (PTMs). Here, we developed a streamlined workflow for processing 20 microliters of venous blood collected by volumetric absorptive microsampling that incorporates serial trypsinization and N-glycopeptide and phosphopeptide enrichment and avoids laborious sample dry-down or cleanup steps. As many as 10,000 analytes (reported as protein groups, glycopeptidoforms, and phosphosites) can be quantified by liquid chromatography-tandem mass spectrometry in under 2 h of MS acquisition time. Using these methods, we explored the stability of "dried" and "wet" blood proteomes, as well as the effects of ex vivo inflammatory stimulus or phosphatase inhibition. Multiomics factor analysis enabled facile identification of analytes that contributed to interindividual variability of the blood proteomes, including N-glycopeptides that distinguish immunoglobulin heavy constant alpha 2 allotypes. Collectively, our results help to establish feasibility and best practices for the integrated MS-based quantification of proteins and PTMs from dried blood.

The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof. In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers “identified” and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication. Poor metabolite identification potentially represents an existential threat to the credibility of untargeted “discovery” metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

Multiple reaction monitoring (MRM) enables robust and sensitive quantification but traditionally requires predefined precursor-fragment transitions, limiting its use in discovery-driven studies. Here, we describe untargeted/micro/universal multiple reaction monitoring (uMRM), a workflow that converts high-resolution untargeted liquid chromatography-mass spectrometry/MS (LC-MS/MS) data into scheduled triple-quadrupole MRM transitions. Pooled-sample LC-MS and stepped-energy DDA MS/MS acquisitions (0, 10, 20, and 40 eV) are used to capture precursor and fragment information representative of each experimental set. Detected features undergo automated deisotoping and empirically validated in-source fragment filtering, followed by spline-based modeling of collision-energy-dependent fragmentation to define optimized precursor-fragment transitions. Transitions are scheduled using retention times observed in pooled samples and deployed on triple-quadrupole instruments without requiring nonlinear retention-time alignment or authentic standards. Across representative biological matrices, including urine, brain tissue, and cultured cells, uMRM enabled automated generation of quantitative MRM methods from untargeted discovery data. Benchmarking across seven triple-quadrupole platforms demonstrated strong agreement between uMRM-derived and experimentally optimized collision energies. By converting discovery-scale data sets into compact transition tables suitable for quantitative deployment, uMRM provides a reproducible approach for linking untargeted LC-MS/MS acquisition with targeted quantitation.

The plasma and urinary metabolite profiles of the 2-thiophene-based H1-receptor antagonist methapyrilene (N,N-dimethyl-N'-pyridin-2-yl-N'-(thiophen-2-ylmethyl)ethane-1,2-diamine) were investigated in the rat following the oral administration of 0, 50 or 150 mg/kg/day of the drug for 5 days. The systemic exposure and metabolic fate of methapyrilene were investigated initially using a rapid "fit for purpose" reversed-phase (RP) UHPLC-MS/MS assay followed by a more in-depth characterization of its metabolites using RP-UHPLC coupled to a high resolution multi reflecting time of flight mass spectrometer. No detectable methapyrilene was present in plasma when measured on days 1, 3 and 5, at any dose, using samples obtained 24 h post administration. However, methapyrilene-derived metabolites were detected, in increasing amounts, in these plasma samples on days 3 and 5 of the study. Both methapyrilene and large numbers of metabolites were present in the urines collected 24 h after dosing on days 3 and 5. The profiles of these metabolites were both dose and time dependent. From the urine and plasma profiles obtained using both +ve and -ve ESI HRMS some 24 metabolites were characterized showing functionalization by aliphatic and aromatic hydroxylation, N-oxidation, N-demethylation and loss of the thiophene ring. Glucuronic acid, glycine and glutathione-derived conjugates were also detected.

The effects of the anilinoquinazoline tyrosine kinase inhibitor (TKI) gefitinib on the polar urinary metabolome of mice following oral administration (50 mg/kg) were studied over the 24 h period post dose. Analysis was performed using a hydrophilic interaction liquid chromatographic separation (HILIC) hyphenated to cyclic ion mobility (cIM) and mass spectrometry (MS). This investigation revealed numerous time-dependent changes in the polar urinary metabolic phenotype of gefitinib-dosed mice that mirrored the plasma concentrations and urinary excretion of the drug and its metabolites. These changes showed both relative increases and decreases in the amounts of various endogenous metabolites, including 8-hydroxydeoxyguanosine, thymidine, acetylcarnitine, isobutyrylcarnitine, myristoylglycine, 3-phenylpropionylglycine, cyclic AMP, 19-oxotestosterone, and 3'-asparagine-AMP. These changes were generally seen to be greatest at the time of the highest plasma and urinary concentrations of gefitinib. The concordance of these effects on the urinary metabolome with plasma/urine drug concentrations strongly implies a range of pharmacometabodynamic effects pointing to mechanism-based regulation of a number of endogenous metabolic pathways by gefitinib.

Multiple Reaction Monitoring (MRM) remains the gold standard for quantitative mass spectrometry but continues to be constrained by the limited availability of high-quality transitions and collision energy (CE) values for many biologically and chemically relevant molecules. Here, we present the METLIN 960K MRM library, a 960,000-compound transition resource derived entirely from empirically acquired MS/MS data. MRM transitions were generated in both positive and negative ionization modes using an empirical spline-based pipeline refined by AI BioSync, an XCMS enhancement that provides a framework of AI and machine-learning tools designed to decipher spectral data for biological and analytical relevance. Central to this approach is spline fitting of CE-dependent intensity profiles from experimental MS/MS data collected at four discrete energies (0, 10, 20, and 40 eV), enabling continuous CE modeling and precise prediction of optimal fragmentation conditions. Supervised learning models were used within AI BioSync to refine spline fitting across diverse chemical classes, improving reproducibility and predictive accuracy. Validation across more than 100 authentic compounds, including rare metabolites and diverse small molecules, demonstrated robust detection down to 1 nM, confirming both sensitivity and scalability. This framework also holds immediate applicability for preclinical drug development studies, where authentic metabolite and impurity standards are often unavailable. Unlike prior methods reliant on in silico fragmentation or heuristic rules, all transitions are derived directly from experimental MS/MS data using absolute intensities. The resulting precursor m/z-centric METLIN 960K MRM library (https://metlin.scripps.edu) greatly expands the chemical space accessible to targeted quantitation, providing a scalable, vendor-independent path for sensitive and specific molecular detection across research, clinical, and applied applications.

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC-MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance.

The use of HILIC-based separations for the analysis of polar metabolites in metabolic phenotyping studies is well established. Here, we demonstrate the increased coverage of the polar metabolome obtained by travelling wave (TW) ion mobility (IM) instruments combined with HILIC and mass spectrometry (MS) for metabotyping rat and mouse urine samples. Profiling was performed using either a linear TW IM-MS based instrument with a path length of 40 cm or an instrument with a cyclic travelling wave analyser (cIM) with a path length of 95 cm. Due to the added resolution afforded by using both the linear and cyclic IM geometries with MS detection (IM-MS) significant increases in feature count (m/z-tR pairs) were generally obtained compared to HILIC-MS alone. In addition, the use of both linear and cyclic IM-MS improved the quality of the mass spectra obtained as a result of the separation of co-eluting analytes. As would be expected from the increased path length of the cyclic IM-MS instrument compared to the linear device, the largest gains in feature detection were obtained for the HILIC-cIMMS combination. By increasing the resolution of coeluting components, the cyclic IM-MS instrumentation also provided the largest improvement in the quality of the mass spectral data obtained. When applied to mouse urines obtained from both control and gefitinib-dosed mice, time-related changes were detected in those obtained from the treated animals that were not seen in the controls. Polar metabolites affected by drug administration included, but were not limited to, hypoxanthine, 1,3-dimethyluracil and acetylcarnitine. The changes seen in the relative concentrations of these endogenous metabolites appeared to be related to drug concentrations in the plasma and urine suggesting a pharmacometabodynamic link.

Extra-column band broadening can significantly reduce the performance of rapid ultra-high performance liquid chromatography-MS-based methods (UHPLC-MS). However, as we show here, UHPLC-MS/MS methods on short 2.1 mm i.d. columns can be optimized to reduce band broadening by simple procedures such as dispensing with the solvent divert valves placed between the column and the MS source. Vacuum jacketed columns have previously been shown to provide superior performance to conventional UHPLC-MS/MS by reducing on and post column band broadening. Here we have compared the optimized "direct" UHPLC approach for the high throughput (HT) bioanalysis of drugs and metabolites in biofluids such as urine and blood plasma with vacuum jacketed chromatography (VJC), using columns of the same geometry and packed with the same stationary phases. This study demonstrates that the performance of VJC was still superior to the direct UHPLC-MS/MS methods for rapid "generic" bioanalysis using gradient times of 0.25 to 5 min. Further investigations using microbore VJC-MS/MS, with 1 mm i.d. columns, for bioanalysis of the same biofluid samples showed that this format offers great promise for HT "discovery" drug and metabolite analysis/profiling. In addition the reduction of solvent use, by up to 90 % for methods when using microbore columns, can significantly contribute to improved sustainability and reducing costs per analysis.

The untargeted global profiling of endogenous metabolites and lipids has the potential to increase knowledge and understanding in many areas of biology. LC-MS/MS is a key technology for such analyses however, several different LC methodologies, using different mobile phase compositions, are required to cover the diversity in polarity and analyte structure encountered in biological samples. Most notably many lipid screening methods make use of isopropanol (IPA) as a major component of mobile phases employed for comprehensive lipidomic profiling. In order to increase laboratory efficiency, and minimize opportunities for errors, a suite of methods, based on a single acetonitrile (ACN)-aqueous buffer mobile phase combination, has been developed. This mobile phase can be used for hydrophobic interaction liquid chromatography on an amide stationary phase (for polar analytes), reversed-phase (RP) LC analysis on a C8 stationary phase (for moderately polar-non-polar compounds) and RPLC using a CSH phenyl-hexyl bonded column (for lipids). All of these sub 10 minute separations had good throughput and reproducibility with CV's of analyte response <25 % whilst eliminating the need for complex mobile phase preparation and the use of IPA as an organic modifier for lipidomics. Advantages of removing IPA and replacing it with the ACN-based method were a 58 % increase in peak capacity for lipids, with improved resolution for the di- and triglycerides and cholesterol esters compared to current methods. Compared to the IPA-containing solvent system the ACN-based mobile phase also resulted in a 61 % increase in lipid feature detection. The utility of this "universal" mobile phase approach was demonstrated by its application to a rat toxicology study investigating the consequences of methapyrilene administration through on the endogenous metabolite profiles of plasma and urine. Methapyrilene and its metabolites were also profiled in these samples.

Metabolite identification represents a major bottleneck in contemporary metabolomics research and a step where critical errors may occur and pass unnoticed. This is especially the case for studies employing liquid chromatography-mass spectrometry technology, where there is increased concern on the validity of the proposed identities. In the present perspective article, we describe the issue and categorize the errors into two types: identities that show poor biological plausibility and identities that do not comply with chromatographic data and thus to physicochemical properties (usually hydrophobicity/hydrophilicity) of the proposed molecule. We discuss the problem, present characteristic examples, and propose measures to improve the situation.

The metabolism and pharmacokinetics of fasiglifam (TAK-875, 2-[(3S)-6-[[3-[2,6-dimethyl-4-(3-methylsulfonylpropoxy)phenyl]phenyl]methoxy]-2,3-dihydro-1-benzofuran-3-yl]acetic acid), a selective free fatty acid receptor 1 (FFAR1)/GPR40 agonist, were studied following intravenous (5 mg/kg) and oral administration (10 and 50 mg/kg) to male and female Sprague Dawley rats.Following intravenous dosing at 5 mg/kg, peak observed plasma concentrations of 8.8/9.2 mu g/ml were seen in male and female rats respectively.Following oral dosing, peak plasma concentrations at 1 h of ca. 12.4/12.9 mu g/ml for 10 mg/kg and 76.2/83.7 mu g/ml for 50 mg/kg doses were obtained for male and female rats respectively. Drug concentrations then declined in the plasma of both sexes with t(1/2)'s of 12.4 (male) and 11.2 h (female). Oral bioavailability was estimated to be 85-120% in males and females at both dose levels.Urinary excretion was low, but in a significant sex-related difference, female rats eliminated ca. 10-fold more drug-related material by this route.Fasiglifam was the principal drug-related compound in plasma, with 15 metabolites, including the acyl glucuronide, also detected. In addition to previously identified metabolites, a novel biotransformation, that produced a side-chain shortened metabolite via elimination of CH2 from the acetyl side chain was noted with implications for drug toxicity.

Mobile phase selection is of critical importance in liquid chromatography - mass spectrometry (LC-MS) based studies, since it affects retention, chromatographic selectivity, ionization, limits of detection and quantification, and linear dynamic range. Generalized LC-MS mobile phase selection criteria, suitable for a broad class of chemical compounds, do not exist thus far. Here we have performed a large-scale qualitative assessment of the effect of solvent composition used for reversed-phase LC separations on electrospray ionization (ESI) response for 240 small molecular weight drugs, representing various chemical compound classes. Of these 240 analytes 224 were detectable using ESI. The main chemical structural features affecting ESI response were found to all be surface area or surface charge-related. Mobile phase composition was found to be less differentiating, although for some compounds a pH effect was noted. Unsurprisingly, chemical structure was found to be the dominant factor for ESI response for the majority of the investigated analytes, representing about 85% of the replicating detectable complement of the sample data set. A weak correlation between ESI response and structure complexity was observed. Solvents based on isopropanol, and those containing phosphoric or di- and trifluoracetic acids, performed relatively poorly in terms of chromatographic or ESI response, whilst the best performing 'generic' LC solvents were based on methanol, acetonitrile using formic acid and ammonium acetate as buffer components, consistent with current practice in many laboratories.

There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.

Properly implemented, metabolic and lipidomic profiling can provide a deeper understanding of mammalian, plant and bacterial biology. These omics-tools have developed and matured over the last 40-years and are now being deployed to provide valuable information in epidemiological studies, drug toxicology and pharmacology, disease biology and progression and patient stratification. LC/MS has become the technology of choice for both metabolic and lipid profiling, due to its speed, sensitivity and structural elucidation capabilities. In the preceding two decades there have been many technological and methodological advances in LC/MS that have facilitated the evolution of the technology into a rugged, reliable, and easily deployed tool. These advances include, but are not limited to, improvements in chromatography (phases, columns, and delivery system), instruments for mass spectrometry, optimization of sample preparation, the introduction of ion mobility, data analysis tools, metabolite databases, harmonized protocols, and the more widespread use of quality control methods and reference standards/matrices. Here, recent developments and advances in high throughput liquid chromatography/high resolution mass spectrometry for metabolic phenotyping are described. These advances which may provide improved feature detection, increased laboratory efficiency and data quality, as well as “biomarker” identification, are discussed in relation to their potential application to the analysis of large clinical studies, or biobank collections.

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

Ion mobility (IM) separations, especially when combined with mass spectrometry, offer the opportunity for the rapid analysis and characterization of mixtures. However, the limited resolution afforded by many IM systems means that in practice applications may be limited. Here we have employed an IM separation on a highresolution cyclic IM device with MS/MS to separate and characterize mixtures of sulfated isomers of tyrosine and associated metabolites containing multiple sulfated isoforms present in reaction mixtures. The cIMS device allowed ions, not resolved using a single pass, to be subjected to multiple passes, enabling the resolution of those with similar collision cross sections (CCS). Predicted single pass CCS values calculated for the isomers likely to be present in these mixtures showed only small differences between them, ranging between of between 0.1 - 0.7 % depending on structure. These small differences highlight the high degree of mobility resolution required for separating the isomers. Experimentally different isoforms of tyrosine sulfate and sulfated tyrosine metabolites could be sufficiently resolved via multipass separations (3-35 passes). This degree of separation provided resolving powers of up to 384 CCS/Delta CCS for sulfated dopamine which enabled good MS/MS spectra to be generated. In human urine the presence of a single sulfated form of tyrosine was detected and identified as the Osulfate after 3 passes based on the synthetic standard. Of the other tyrosine-related sulfates for which synthetic standards had been prepared only dopamine sulfate was detected in this sample.

Lipids play a key role in many biological processes, and their accurate measurement is critical to unraveling the biology of diseases and human health. A high throughput HILIC-based (LC-MS) method for the semiquantitative screening of over 2000 lipids, based on over 4000 MRM transitions, was devised to produce an accessible and robust lipidomic screen for phospholipids in human plasma/serum. This methodology integrates many of the advantages of global lipid analysis with those of targeted approaches. Having used the method as an initial “wide class” screen, it can then be easily adapted for a more targeted analysis and quantification of key, dysregulated lipids. Robustness was assessed using 1550 continuous injections of plasma extracts onto a single column and via the evaluation of columns from 5 different batches of stationary phase. Initial screens in positive (239 lipids, 431 MRM transitions) and negative electrospray ionization (ESI) mode (232 lipids, 446 MRM transitions) were assessed for reproducibility, sensitivity, and dynamic range using analysis times of 8 min. The total number of lipids monitored using these screening methods was 433 with an overlap of 38 lipids in both modes. A polarity switching method for accurate quantification, using the same LC conditions, was assessed for intra- and interday reproducibility, accuracy, dynamic range, stability, carryover, dilution integrity, and matrix interferences and found to be acceptable. This polarity switching method was then applied to lipids important in the stratification of human prostate cancer samples.

Reversed-phase high performance thin-layer chromatography (RP-HPTLC) on C18 bonded silica gel was combined with desorption electrospray ionization (DESI) and high resolution time of flight mass spectrometry (HRToFMS) to detect, characterize and image (MSI) phytoecdysteroids (plant-derived insect moulting hormones) in ethanolic extracts of members of the Silene plant family. As seen previously for silica gel, DESI provided a simple and convenient method for recovering polar polyhydoxysteroids from RP-HPTLC plates for the purposes of both the MS and MSI of extracts obtained from three species of the Silene family (Silene otites, S. nutans and S. viridiflora). Using RP-HPTLC/DESI/MSI/HRToFMS a number of ecdysteroids, including 20-hydroxyecdysone, polypodine-B, 2-deoxy-20-hydroxyecdysone and 2-deoxyecdysone were identified in these extracts. Differences were noted in the mass spectra obtained depending upon both the stationary phase on which they were separated, and the temperatures used in the heated transfer line used for introduction into the ion source. Ecdysteroids detected after chromatography on C18 bonded silica showed increased fragmentation due to water loss compared to those imaged from silica. In addition, the benefits of the additional resolution provided by 2-dimensional TLC for increasing spectral quality compared to a 1-dimensional separation are demonstrated.