In-source Fragmentation Research Articles

Comprehensive targeted profiling of multiple steroid classes in rodent plasma using liquid chromatography-mass spectrometry

BackgroundReliable quantification of multiple steroid classes in biological fluids within a single method remains an analytical challenge despite many previously published methods. Crosstalk of positional isomers, overlap of stereoisomer fragmentation patterns, differing proton affinities, in-source fragmentation, varying stability of protonated ions in the gas phase across steroid classes, and non-existence of steroid-free matrix are the main challenges limiting the number of simultaneously profiled steroids. ResultsIn this study, we focused on the development of a derivatization-free, achiral, high-throughput, and cost-effective UHPLC-MS/MS approach that allows simultaneous profiling of a spectrum of 38 steroids covering progestogens, androgens, corticosteroids, and estrogens, while properly addressing the hurdles of steroid analysis. Within a 20-min method, 16 stereoisomers and 15 positional isomers were fully resolved within a single run while separated from 7 additional non-interfering steroids and matrix interferences in rodent plasma. Protein precipitation (PP) and supported liquid extraction (SLE) methods using only 40 μL of sample were developed to achieve the lowest possible limits of quantification. Nevertheless, 5α-dihydroprogesterone and 3α,5α-THDOC could be only qualitatively assessed when using PP. In contrast, DHEA-S could not be quantified or identified when using SLE. A novel surrogate matrix-background subtraction approach, using rat plasma after the animal's adrenalectomy, has been implemented into the optimized PP-UHPLC-MS/MS workflow, successfully validated according to the unified ICH/EMA M10 guidelines, and compared to the traditional quantification strategies. Moreover, the validity of the newly adopted approach has been verified by the targeted profiling of multiple biologically active endogenous steroids in more than 500 samples of mouse plasma in total. SignificanceUnderestimation of hurdles associated with steroid analysis often compromises the accurate steroid quantification. Our comprehensive, fully validated UHPLC-MS/MS method targeting a wide spectrum of endogenous steroids, mitigating steroid crosstalk and using a minimal sample volume together with a novel surrogate matrix-background subtraction approach significantly advances steroid analysis for research and clinical applications covering multiple biological scopes.

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Enhancing Influenza Detection through Integrative Machine Learning and Nasopharyngeal Metabolomic Profiling: A Comprehensive Study

Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. Method: A publicly available dataset of 236 nasopharyngeal samples screened via liquid chromatography–quadrupole time-of-flight (LC/Q-TOF) mass spectrometry was used. Among these, 118 samples tested positive for influenza (40 A H1N1, 39 A H3N2, 39 Influenza B), while 118 were negative controls. A stacking-based model was proposed using the top 20 selected features. Thirteen machine learning models were initially trained, and the top three were combined using predicted probabilities to form a stacking classifier. Results: The ExtraTrees stacking model outperformed other models, achieving 97.08% accuracy. External validation on a prospective cohort of 96 symptomatic individuals (48 positive and 48 negatives for influenza) showed 100% accuracy. SHAP values were used to enhance model explainability. Metabolites such as Pyroglutamic Acid (retention time: 0.81 min, m/z: 84.0447) and its in-source fragment ion (retention time: 0.81 min, m/z: 130.0507) showed minimal impact on influenza-positive cases. On the other hand, metabolites with a retention time of 10.34 min and m/z 106.0865, and a retention time of 8.65 min and m/z 211.1376, demonstrated significant positive contributions. Conclusions: This study highlights the effectiveness of integrating metabolomics data with machine learning for accurate influenza diagnosis. The stacking-based model, combined with SHAP analysis, provided robust performance and insights into key metabolites influencing predictions.

B-274 Determining Novel Biomarker Candidates Associated With Heroin and Cocaine Exposure Using Urine Metabolomics

Abstract Background Substance abuse poses an escalating health risk in the United States. While acute drug exposure can be tracked using drugs and their metabolites as biomarkers in clinical settings, there is a notable absence of biomarkers for chronic drug exposure or addiction. The development and availability of such biomarkers could significantly enhance clinicians’ ability to identify patients at high risk of addiction, and more accurately predict disease progression and treatment responses. Methods Our study aimed to discover novel biomarkers associated with substance abuse by analyzing raw mass spectrometry (MS) data from urine comprehensive drug screening (UCDS) conducted at the University of Pittsburgh Medical Center (UPMC). The MS data, acquired through untargeted data collection using LC-qToF-MS at the UPMC Clinical Toxicology Laboratory, was initially preprocessed (including peak identification, alignment, and putative annotation) using MS-DIAL to generate a data matrix. This matrix, consisting of objects (cases) and features (m/z and retention time), was further normalized and analyzed using MetaboAnalyst. A combination of statistical methods, including point biserial correlation analysis, volcano plot, significance analysis of microarrays and metabolites (SAM), empirical Bayesian analysis of microarrays and metabolites (EBAM), partial least squares discriminant analysis (PLS-DA), and random forest (RF), were employed to select features significantly associated with the EIA-results. These features were subsequently evaluated for analyte identification using MS-FINDER. Results Our analysis identified nine significant features associated with 6MAM-EIA for heroin abuse, including 6-monoacetylmorphine itself and norfentanyl. For features associated with cocaine metabolite-EIA, 37 significant features were selected, including multiple cocaine metabolites, nicotine metabolites, and norfentanyl. Some of these selected features are likely products of in-source fragmentation or endogenous metabolites. Conclusions Our study has identified a set of potential biomarker candidates for illicit drug exposure. Notably, norfentanyl was found to be significantly associated with both 6MAM-EIA and cocaine metabolite-EIA, reflecting current trends in illicit drug use. Further chemical identification of these biomarkers is planned for future work.

In-Depth Analysis of Molecular Network Based on Liquid Chromatography Coupled with Tandem Mass Spectrometry in Natural Products: Importance of Redundant Nodes Discovery.

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC-MS/MS presents a challenge for data processing and interpretation, and the LC-MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Postionization with Matrix-Assisted Laser Desorption/Ionization.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide spatially resolved molecular information about a sample. Recently, a postionization approach (MALDI-2) has been commercially integrated with MALDI-MSI, allowing for bettered sensitivity and consequent improved spatial resolution. While advantages of MALDI-2 have previously been established, we demonstrate here statistically increased in-source fragmentation (ISF) results from postionization with a commercial instrument. Via lipid standard analyses, known MALDI ISF pathways (e.g., loss of trimethylamine) were statistically increased in MALDI-2 compared to MALDI-1 (65-172% increase in fragmentation). Gas phase molecular modeling with density functional theory estimated that the most-weighted virtual orbitals to excite within lipids involve ester and phosphate bonds. Protonated lipid excitation energies are furthermore red-shifted compared to those of other adduct types [e.g., 254 nm for protonated PC(16:0/18:1)] and approach the MALDI-2 laser energy (266 nm). Analysis of rat brain homogenate detected statistically more positive-ion mode peaks with MALDI-2 (1090) than that with MALDI-1 (719), where Kernel density estimations showed that the majority of this enhancement occurs with low m/z ions (i.e., m/z 75-500). Taken together with the lipid standard data, these observations may indicate ISF due to postionization. While artifact contributions from matrix blanks were also noted, both experimental and computational data sets suggest that the overall extent of ISF is statistically increased in MALDI-2 compared to MALDI-1.

Liquid chromatography-mass spectrometry quantification of phytochemicals in Withania somnifera using data-dependent acquisition, multiple-reaction-monitoring, and parallel-reaction-monitoring with an inclusion list.

Characterization of botanical extracts by mass spectrometry-based metabolomics analysis helps in determining the phytochemical composition that underlies their bioactivity and potential health benefits, while also supporting reproducibility of effects in clinical trials. The quantification of seven withanolides in Withania somnifera using three mass-spectrometry methods was evaluated using Deming regression. Two high-resolution time-of-flight mass spectrometry methods were used, one operating in data-dependent acquisition mode and the other in parallel-reaction-monitoring mode with an inclusion list. The two high-resolution time-of-flight mass spectrometry methods were compared to a multiple-reaction-monitoring method. We evaluated in-source fragmentation of steroidal glycosides and optimized the methods accordingly. A novel software approach to integrating parallel-reaction-monitoring data acquired with an inclusion list was developed. Combining and comparing quantitative results allowed for quantitative specificity, good precision, and adjustment of instrument source conditions for optimal quantification by multiple-reaction-monitoring mass spectrometry, an analytical method that is widely accessible in analytical and phytochemical laboratories.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies.

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Microcystins with Modified Adda5-Residues from a Heterologous Microcystin Expression System.

Microcystins are hepatotoxic cyclic heptapeptides produced by some cyanobacterial species and usually contain the unusual β-amino acid 3S-amino-9S-methoxy-2S,6,8S-trimethyl-10-phenyl-4E,6E-decadienoic acid (Adda) at position-5. The full microcystin gene cluster from Microcystis aeruginosa PCC 7806 has been expressed in Escherichia coli. In an earlier study, the engineered strain was shown to produce MC-LR and [d-Asp3]MC-LR, the main microcystins reported in cultures of M. aeruginosa PCC 7806. However, analysis of the engineered strain of E. coli using semitargeted liquid chromatography with high-resolution tandem mass spectrometry (LC-HRMS/MS) and thiol derivatization revealed the presence of 15 additional microcystin analogues, including four linear peptide variants and, in total, 12 variants with modifications to the Adda moiety. Four of the Adda-variants lacked the phenyl group at the Adda-terminus, a modification that has not previously been reported in cyanobacteria. Their HRMS/MS spectra contained the product-ion from Adda at m/z 135.1168, but the commonly observed product-ion at m/z 135.0804 from Adda-containing microcystins was almost completely absent. In contrast, three of the variants were missing a methyl group between C-2 and C-8 of the Adda moiety, and their LC-HRMS/MS spectra displayed the product-ion from Adda at m/z 135.0804. However, instead of the product-ion at m/z 135.1168, these three variants gave product-ions at m/z 121.1011. These observations, together with spectra from microcystin standards using in-source fragmentation, showed that the product-ion at m/z 135.1168 found in the HRMS/MS spectra of most microcystins originated from the C-2 to C-8 region of the Adda moiety. Identification of the fragmentation pathways for the Adda side chain will facilitate the detection of microcystins containing modifications in their Adda moieties that could otherwise easily be overlooked with standard LC-MS screening methods. Microcystin variants containing Abu at position-1 were also prominent components of the microcystin profile of the engineered bacterium. Microcystin variants with Abu1 or without the phenyl group on the Adda side chain were not detected in the original host cyanobacterium. This suggests not only that the microcystin synthase complex may be affected by substrate availability within its host organism but also that it possesses an unexpected degree of biosynthetic flexibility.

Characterization of spironolactone and metabolites derivatized using Girard's reagent P using mass spectrometry and ion mobility spectrometry.

Spironolactone is a steroidal drug prescribed for a variety of medical conditions and is extensively metabolized quickly after administration. Measurement of spironolactone and its metabolites remains challenging using mass spectrometry (MS) due to in-source fragmentation and relatively poor ionization using electrospray ionization. Therefore, improved methods of measurements are needed, particularly in the case of small sample volumes. Girard's reagent P (GP) derivatization of spironolactone was employed to improve response and provide an MS-based solution to the measurement of spironolactone and its metabolites. We performed ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS) and ion mobility spectrometry (IMS)-high-resolution mass spectrometry (HRMS) to fully characterize the GP derivatization products. Analytes were studied in positive ionization mode, and MS/MS was performed using nonresonance and resonance excitation collision-induced dissociation. We observed the successful GP derivatization of spironolactone and its metabolites using authentic chemical standards. A signal enhancement of 1-2 orders of magnitude was observed for GP-derivatized versions of spironolactone and its metabolites. Further, GP derivatization eliminated in-source fragmentation. Finally, we performed GP derivatization and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) in a small volume of murine serum (20 μL) from spironolactone-treated and control animals and observed multiple spironolactone metabolites only in the spironolactone-treated group. GP derivatization was proven to have advantageous mass spectral performance (e.g., limiting in-source fragmentation, enhancing signals, and eliminating isobaric analytes) for spironolactone and its metabolites. This work and the detailed characterization using ultra-high-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and IMS serve as the foundation for future developments in reaction optimization and/or quantitative assay development.

Improved Mass Accuracy and Precision for Multi-Attribute Methods Using a New Internally Calibrated High Resolution Orbitrap Mass Detector.

In the development of biotherapeutics, a thorough understanding of a molecule's product quality attributes (PQAs) and their effect on structure-function relationships and long-term stability is essential for ensuring the safety and efficacy of the product. First published in 2015, the multi-attribute method (MAM), based on LC-MS peptide mapping and automation principles, can be used to support biotherapeutic process and product development. The MAM provides simultaneous site-specific detection, identification, quantitation, and quality control monitoring of selected PQAs. In this article, a low-maintenance MAM-ready mass detector with a small footprint was evaluated for its ability to monitor PQAs on proteolytically digested proteins with high mass accuracy and precision. Optimized source parameters enable robust relative quantitation of attributes with high sensitivity and minimal in-source fragmentation. A combination of a built-in one-point mass calibration procedure prior to data acquisition and Scan-to-Scan on-the-fly mass correction allows monitoring of most peptides for at least 54 days with sub-1 ppm mass accuracies at high-resolution (180,000 at m/z 200). This enables the use of <3 ppm mass tolerances for peptide monitoring, supporting high method specificity and robustness. LC-MS based MAM data from this instrument compares well to data collected by earlier MAM systems and conventional HPLC profile-based drug substance release assays.

An Efficient Workflow for Quality Control Marker Screening and Metabolite Discovery in Dietary Herbs by LC-Orbitrap-MS/MS and Chemometric Methods: A Case Study of Chrysanthemum Flowers.

LC-MS is widely utilized in identifying and tracing plant-derived food varieties but quality control markers screening and accurate identification remain challenging. The adulteration and confusion of Chrysanthemum flowers highlight the need for robust quality control markers. This study established an efficient workflow by integrating UHPLC-Orbitrap-MS/MS with Compound Discoverer and chemometrics. This workflow enabled the systematic screening of 21 markers from 10,540 molecular features, which effectively discriminated Chrysanthemum flowers of different species and cultivars. The workflow incorporated targeted and untargeted methods by employing diagnostic product ions, fragmentation patterns, mzCloud, mzVault, and in-house databases to identify 206 compounds in the flowers, including 17 screened markers. This approach improved identification accuracy by reducing false positives, eliminating in-source fragmentation interference, and incorporating partial verification utilizing our established compound bank. Practically, this workflow can be instrumental in quality control, geolocation determination, and varietal tracing of Chrysanthemum flowers, offering prospective use in other plant-derived foods.

Characterization of PEGylation sites in Neulasta and a biosimilar candidate with a combined fragmentation strategy in mass spectrometry analysis.

In the development of biosimilar products to Neulasta, it is essential to determine the intact molecular mass and confirm precise PEGylation sites. In this study, we applied a combination of techniques, including post-column addition of triethylamine in reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) to determine the intact molecular mass, and in-source fragmentation (ISF) and higher-energy collision dissociation-tandem mass spectrometry (HCD-MS/MS) to identify the PEGylation site. Our results show that both the pegfilgrastim biosimilar candidate and Neulasta lots are mono-PEGylated at the N-terminal end. Furthermore, we show that the combined ISF and HCD-MS/MS method can be used for identifying the PEGylation sites in the diPEGylated variant of pegfilgrastim. The diPEGylated variant has modification sites at the N-terminal end and a lysine at position 35 in the protein sequence.

Comparison of N-Glycopeptide to Released N-Glycan Abundances and the Influence of Glycopeptide Mass and Charge States on N-Linked Glycosylation of IgG Antibodies.

We report the comparison of mass-spectral-based abundances of tryptic glycopeptides to fluorescence abundances of released labeled glycans and the effects of mass and charge state and in-source fragmentation on glycopeptide abundances. The primary glycoforms derived from Rituximab, NISTmAb, Evolocumab, and Infliximab were high-mannose and biantennary complex galactosylated and fucosylated N-glycans. Except for Evolocumab, in-source ions derived from the loss of HexNAc or HexNAc-Hex sugars are prominent for other therapeutic IgGs. After excluding in-source fragmentation of glycopeptide ions from the results, a linear correlation was observed between fluorescently labeled N-glycan and glycopeptide abundances over a dynamic range of 500. Different charge states of human IgG-derived glycopeptides containing a wider variety of abundant attached glycans were also investigated to examine the effects of the charge state on ion abundances. These revealed a linear dependence of glycopeptide abundance on the mass of the glycan with higher charge states favoring higher-mass glycans. Findings indicate that the mass spectrometry-based bottom-up approach can provide results as accurate as those of glycan release studies while revealing the origin of each attached glycan. These site-specific relative abundances are conveniently displayed and compared using previously described glycopeptide abundance distribution spectra "GADS" representations. Mass spectrometry data are available from the MAssIVE repository (MSV000093562).

Comparison of Cu+, Ag+, and Au+ Ions as Ionization Agents of Volatile Organic Compounds at Subatmospheric Pressure.

Ionization of volatile organic compounds (VOCs) by coinage metal ions (Cu+, Ag+, and Au+) generated by laser desorption and ionization (LDI) of a metal nanolayer in subatmospheric conditions is explored. The study was performed in a commercial subatmospheric dual MALDI/ESI ion source. Five compounds representing different VOC classes were chosen for a detailed study of the metal ionization mechanism: ethanol, acetone, acetic acid, xylene, and cyclohexane. In the gas phase, ion molecular complexes of all three metal ions were formed, typically with two ligand molecules. The successful detection of the metal complexes with VOCs strongly depended on the applied voltages across the ion source, minimizing the in-source fragmentation. The employed orbital trap with ultrahigh resolving power and sub-parts-per-million mass accuracy allowed unambiguous identification of the formed complexes based on their molecular formulas. The detection limits of the studied compounds in the gas were in the range 0.1-1.4 nmol/L. Compared to Cu+ and Ag+ ions, Au+ ions exhibited the highest reactivity, often complicating spectra by side products of reactions. On the other hand, they also allowed detecting saturated hydrocarbons, which did not produce any signals with Ag+ and Cu+.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Atomistic simulations for investigation of substrate and salt effects on lipid in-source fragmentation in secondary ion mass spectrometry: A follow-up study.

In-source fragmentation (ISF) poses a significant challenge in secondary ion mass spectrometry (SIMS). These fragment ions increase the spectral complexity and can lead to incorrect annotation of fragments as intact species. The presence of salt that is ubiquitous in biological samples can influence the fragmentation and ionization of analytes in a significant manner, but their influences on SIMS have not been well characterized. To elucidate the effect of substrates and salt on ISF in SIMS, we have employed experimental SIMS in combination with atomistic simulations of a sphingolipid on a gold surface with various NaCl concentrations as a model system. Our results revealed that a combination of bond dissociation energy and binding energy between N-palmitoyl-sphingomyelin and a gold surface is a good predictor of fragment ion intensities in the absence of salt. However, ion-fragment interactions play a significant role in determining fragment yields in the presence of salt. Additionally, the charge distribution on fragment species may be a major contributor to the varying effects of salt on fragmentation. This study demonstrates that atomistic modeling can help predict ionization potential when salts are present, providing insights for more accurate interpretations of complex biological spectra.

Measurement of haloacetic acids in human urine samples from six megacities of China using a refined detection method

Haloacetic acids (HAAs) are common disinfection by-products in chlorine disinfected water. Humans are extensively exposed to them. However, nationwide biomonitoring data were not available for any country. This study developed a labor-efficient and sensitive method for the detection of urinary HAAs, including trichloroacetic acid (TCAA) and dichloroacetic acid (DCAA), and conducted an exposure assessment in a Chinese population. A total of 850 first-morning-void urine samples were collected from adults in six megacities in China: Wuhan (central), Lanzhou (northwest), Chengdu (southwest), Taiyuan (north), Shanghai (east), and Dalian (northeast). Each participant (n = 425) provided a pair of urine samples during the warm (September to October 2018) and cold (December 2018 to January 2019) seasons. The detection method achieved good retention of the target analytes using a Fluoro-Phenyl column and excellent selectivity using in-source fragmentation ions as precursor ions in multiple reaction monitoring. The detection rate of urinary TCAA in Chinese populations was high (78.5%) but varied among different regions (54.0% to 98.0%). DCAA was rarely detected (<10%). The overall median value of specific gravity adjusted TCAA concentrations was 5.70 μg/L in the warm season and 3.87 μg/L in the cold season, respectively. Higher urinary TCAA concentrations were more likely to occur in Wuhan (Yangtze River Basin), urban areas, and during the warm season. The upstream region of the Yangtze River Basin (Chengdu) typically has lower TCAA concentrations. TCAA formation in coastal cities such as Shanghai and Dalian may be impacted by seawater intrusion. Estimated daily intakes of TCAA were lower than its chronic reference dose of 20 μg/kg-bw/day. This detection method can be applied to future biomonitoring of urinary HAAs. More attention should be paid to the highly exposed subgroups when exploring the health effects of long-term TCAA exposure.

Unraveling the Double Bond Position of Fatty Acids by GC-MS Using Electron Capture APCI and In-Source Fragmentation Patterns.

The position of double bonds in unsaturated fatty acids is strongly connected to their biological effects, but their analytical characterization is still challenging. However, the ionization of unsaturated fatty acids by a GC-APCI leads to regiospecific in-source fragment ions, which can be used to identify the double bond position. The fragment ions are oxidized species that occur mostly at the double bond closest to the carboxylic acid group. This effect can be further promoted by using benzaldehyde as a gas-phase reactant. This allows the identification of the Δ-notation of the fatty acid, and based on additional information such as m/z and retention time, it is possible to annotate the corresponding fatty acid. The developed method also enables the quantification of fatty acids in one step with high selectivity and sensitivity. Moreover, rare fatty acids can be identified in suspected target approaches that are often not available as standards. This was demonstrated by analyzing fish oil samples that provide a complex mixture of highly unsaturated fatty acids and by identifying rare fatty acids such as hexadecatetraenoic acid (FA 16:4 Δ6).

RMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC–MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.