A modular, isolated high-voltage switch for application in ion mobility spectrometry.

Ion mobility spectrometry is an emerging technology in trace gas analysis that has moved from typical safety and security applications to many other fields ranging from environmental and food quality monitoring to medicine and life sciences. Nevertheless, further dissemination, including the development of new instruments and the expansion into new fields of application requires the availability of the fundamental components of ion mobility spectrometers. For example, the electronics is essential for the analytical performance, but is only provided by specialized manufacturers due to specific requirements. In this paper, we present a modular, isolated high-voltage switch that can be operated at an isolated potential. The modular design enables tailoring its configuration to the required application. Each module can switch a voltage of up to 3 kV, and can be operated with an offset voltage of up to 7 kV. The switch has rise and fall times of less than 25 ns, making it suitable for a wide range of applications, e.g., in ion mobility spectrometry. Finally, the presented modular, isolated high-voltage switch was used in a push-pull configuration to generate the injection pulse of the ion gate. The new modular, isolated high-voltage switch shows similar performance compared to a commercially available high-voltage switch.

Diurnal patterns of floral volatile emissions in three species of Narcissus.

PREMISE: Plants generate a wide array of signals such as olfactory cues to attract and manipulate the response of pollinators. The present study addresses the temporal patterns of scent emission as an additional dimension to the scent composition. The expectation is that divergent floral function is reflected in divergent qualitative and temporal emission patterns. METHODS: We used GC-ion mobility spectrometry with an integrated pre-concentration for automated acquisition of the temporal trends in floral volatile emissions for N. viridiflorus, N. papyraceus, and N. cantabricus subsp. foliosus. RESULTS: We found a considerable increase in scent emissions and changes in scent composition for N. viridiflorus at night. This increase was particularly pronounced for aromatic substances such as benzyl acetate and p-cresol. We found no diurnal patterns in N. papyraceus, despite a similar qualitative composition of floral volatiles. Narcissus cantabricus subsp. foliosus showed no diurnal patterns either and differed considerably in floral scent composition. CONCLUSIONS: Scent composition, circadian emission patterns, and floral morphology indicate divergent, but partially overlapping pollinator communities. However, the limited pollinator data from the field only permits a tentative correlation between emission patterns and flower visitors. Narcissus papyraceus and N. cantabricus show no clear diurnal patterns and thus no adjustment to the activity patterns of their diurnal pollinators. In N. viridiflorus, timing of scent emission indicates an adaptation to nocturnal flower visitors, contradicting Macroglossum as the only reported pollinator. We propose that the legitimate pollinators of N. viridiflorus are nocturnal and are still unidentified.

In Vitro Metabolism of Quaternary Ammonium Compounds and Confirmation in Human Urine by Liquid Chromatography Ion-Mobility High-Resolution Mass Spectrometry.

Quaternary ammonium compounds (QACs) are high-production chemicals used as cleaning and disinfecting agents. Due to their ubiquitous presence in the environment and several toxic effects described, human exposure to these chemicals gained increasing attention in recent years. However, very limited data on the biotransformation of QACs is available, hampering exposure assessment. In this study, three QACs (dimethyl dodecyl ammonium, C10-DDAC; benzyldimethyl dodecylammonium, C12-BAC; cetyltrimethylammonium, C16-ATMAC) commonly detected in indoor microenvironments were incubated with human liver microsomes and cytosol (HLM/HLC) simulating Phase I and II metabolism. Thirty-one Phase I metabolites were annotated originating from 19 biotransformation reactions. Four metabolites of C10-DDAC were described for the first time. A detailed assessment of experimental fragmentation spectra allowed to characterize potential oxidation sites. For each annotated metabolite, drift-tube ion-mobility derived collision cross section (DTCCSN2) values were reported, serving as an additional identification parameter and allowing the characterization of changes in DTCCSN2 values following metabolism. Lastly, eight metabolites, including four metabolites of both C12-BAC and C10-DDAC, were confirmed in human urine samples showing high oxidation states through introduction of up to four oxygen atoms. This is the first report of higher oxidized C10-DDAC metabolites in human urine facilitating future biomonitoring studies on QACs.

A study on the determination of the metabolites of 2,4,6-Trinitrotoluene using a dual-drift tube ion mobility spectrometer.

2,4,6-Trinitrotoluene (TNT) and its four metabolites, namely 2-ADNT, 4-ADNT, 2,4-DANT, and 2,6-DANT, are highly toxic substances. These metabolites also serve as biomarkers for assessing the health of individuals exposed to TNT. In this study, a homemade DDT-IMS apparatus was utilized to detect these metabolites. Under negative detection mode, the drift times of 2-ADNT and 4-ADNT showed subtle shifts within a drift tube temperature range of 100 °C-120 °C, aiding in their differentiation. In positive detection mode for 2,4-DANT and 2,6-DANT, significant variations were observed in both the number and drift time of their positive product ions across a drift tube temperature range of 80 °C-120 °C. Consequently, optimal analytical performance for these metabolites was achieved at approximately 100 °C. Evaluation of the instrumental response during the measurement of the four metabolites in both positive and negative modes revealed that negative detection mode offered greater advantages of detecting these compounds. The working ranges for measuring the four metabolites spanned two orders of magnitude, with detection limits for each metabolite nearly below 1 ng. Notably, clear identification of the signals for these metabolites was achieved even when samples were mixed in urine, highlighting the ability of the DDT-IMS in detecting TNT metabolites. The developed DDT-IMS detection method has significant potential for enhancing environmental risk assessment and biological hazard evaluation, particularly in relation to human exposure to TNT.

Advances in the Clinical Application of High-throughput Proteomics.

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements.

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

Recent advances in high-resolution traveling wave-based ion mobility separations coupled to mass spectrometry.

Recently, ion mobility spectrometry-mass spectrometry (IMS-MS) has become more readily incorporated into various omics-based workflows. These growing applications are due to developments in instrumentation within the last decade that have enabled higher-resolution ion mobility separations. Two such platforms are the cyclic (cIMS) and structures for lossless ion manipulations (SLIM), both of which use traveling wave ion mobility spectrometry (TWIMS). High-resolution separations achieved with these techniques stem from the drastically increased pathlengths, on the order of 10 s of meters to >1 km, in both cIMS-MS and SLIM IMS-MS, respectively. Herein, we highlight recent developments and advances, for the period 2019-2023, in high-resolution traveling wave-based IMS-MS through instrumentation, calibration strategies, hyphenated techniques, and applications. Specifically, we will discuss applications including CCS calculations in multipass IMS-MS separations, coupling of IMS-MS with chromatography, imaging, and cryogenic infrared spectroscopy, and isomeric separations of glycans, lipids, and other small metabolites.

Revealing alcoholization-related volatile compounds and determining alcoholization indices in tobacco using GC-IMS coupled with chemometrics.

Alcoholization is an integral part of tobacco processing and volatile compounds are key to assessing tobacco alcoholization. In this study, a total of 154 volatiles from nine categories were determined by gas chromatography-ion mobility spectrometry (GC-IMS) from four grades of tobacco, of which 114 were better identified. And then, the dynamic trends of volatile compounds with significant changes in tobacco alcoholization were analyzed. The relevant volatiles with the alcoholization indices (AIs) (R > 0.8) were screened as indicators of tobacco alcoholization. Cinnamyl isobutyrate, linolenic acid alcohol, propanoic acid-M and propanoic acid-D in all tobacco samples were highly correlated with the AIs and tended to increase during the alcoholization process. In addition, linear discriminant analysis (LDA), back-propagation neural network (BPNN) and random forest (RF) classifiers were constructed for discrimination of tobacco AIs. Three classifiers trained with a combination of 20 volatiles achieved satisfactory results with area under the curve (AUC) of 0.95 (LDA), 0.94 (BPNN) and 0.97 (RF), respectively. The RF classifier gained optimal accuracy of 100 % and 96.1 % for the training and test sets, respectively. The study confirmed that GC-IMS can be used to characterize the changes of volatile compounds in tobacco during alcoholization and combined with machine learning to achieve the determination of AIs. The results of the study may provide a new means for the tobacco industry to monitor the alcoholization process and determine the degree of alcoholization.

Comparative Analysis of Commercially Available Flavor Oil Sausages and Smoked Sausages.

This study utilized gas chromatography-ion mobility spectrometry (GC-IMS) to analyze the volatile flavor compounds present in various commercially available sausages. Additionally, it conducted a comparative assessment of the distinctions among different samples by integrating sensory evaluation with textural and physicochemical parameters. The results of the GC-IMS analysis showed that a total of 65 volatile compounds were detected in the four samples, including 12 hydrocarbons, 11 alcohols, 10 ketones, 9 aldehydes, 12 esters, and 1 acids. Fingerprinting combined with principal component analysis (PCA) showed that the volatiles of different brands of sausages were significantly different (p < 0.05). The volatiles of S1 and S4 were more similar and significantly different from the other two samples (p < 0.05). Among them, there were 14 key volatile substances in the four samples, of which 3-hydroxy-2-butanone and diallyl sulfide were common to all four sausages. Combined textural and sensory evaluations revealed that smoked sausages exhibited superior characteristics in resilience, cohesiveness, springiness, gumminess, and chewiness. Additionally, smoked sausages were found to be more attractive in color, moderately spicy, and salty, while having a lower fat content. In conclusion, smoked sausages are preferred by consumers over flavored oil sausages.

Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.

The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the "Histone Code". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K0) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me1-3) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.

Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.

The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chromatography-mass spectrometry, headspace gas chromatography-ion mobility spectrometry, E-nose, E-tongue, quantitative descriptive analysis, and free amino acid and 5'-nucleotide analyses to achieve reliable discrimination of four salted goose breeds. Volatile and non-volatile compounds and sensory characteristics and intelligent sensory characteristics were analyzed. A preliminary composite dataset was generated in InfoGAN and provided to several base classifiers for training. The prediction results were fused via dynamic weighting to produce an integrated model prediction. An ablation study demonstrated that ensemble learning was indispensable to improving the generalization capability of the model. The framework has an accuracy of 95%, a root mean square error (RMSE) of 0.080, a precision of 0.9450, a recall of 0.9470, and an F1-score of 0.9460.

Pursuing drug laboratories: Analysis of drug precursors with High Kinetic Energy Ion Mobility Spectrometry.

High Kinetic Energy Ion Mobility Spectrometry (HiKE-IMS) is a technique for rapid and reliable detection of trace compounds down to ppbV-levels within one second. Compared to classical IMS operating at ambient pressure and providing the ion mobility at low electric fields, HiKE-IMS can also provide the analyte-specific field dependence of the ion mobility and a fragmentation pattern at high reduced electric field strengths. The additional information about the analyte obtained by varying the reduced electric field strength can contribute to reliable detection. Furthermore, the reduced number of ion-molecule reactions at the low operating pressure of 10 - 40 mbar and the shorter reaction times reduce the impact of competing ion-molecule reactions that can cause false negatives. In this work, we employ HiKE-IMS for the analysis of phenyl-2-propanone (P2P) and other precursor chemicals used for synthesis of methamphetamine and amphetamine. The results show that the precursor chemicals exhibit different behavior in HiKE-IMS. Some precursors form a single significant ion species, while others readily form a fragmentation pattern. Nevertheless, all drug precursors can be distinguished from each other, from the reactant ions and from interfering compounds. In particular, the field-dependent ion mobility as an additional separation dimension aids identification, potentially reducing the number of false positive alarms in field applications. Furthermore, the analysis of a seized illicit P2P sample shows that even low levels of P2P can be detected despite the complex background present in the headspace of real samples.

Discriminative analysis of aroma profiles in diverse cigar products varieties through integrated sensory evaluation, GC-IMS and E-nose.

Cigars, treasured for their rich aromatic profiles, occupy a notable segment in the global consumer market. The objective of this study was to characterize the volatile aroma compounds that shape the flavor profiles of six distinct varieties of Great Wall cigars, contributing to the understanding of cigar aroma analysis. Utilizing HS-GC-IMS and sensory evaluation, the study discerned the aroma profiles of GJ No. 6 (GJ), Animal from the Chinese zodiac (SX), Range Rover No. 3 Classic (JD), Miracle 132 (QJ), Sheng Shi No. 5 (SS), and Red 132 (HS) cigars. The analysis uncovered a spectrum of characteristic aromas, including tobacco, creaminess, cocoa, leather, baking, herbaceous, leathery, woodsy, and fruity notes. A total of 88 compounds were identified, categorized into 11 chemical classes, with their quantities varying among the cigars in a descending order of QJ, JD, GJ, SS, HS, and SX. 24 compounds, such as 2-heptanone, n-butanol, 2,6-dimethylpyrazine and 2-furfuryl methyl sulfide were considered as key differential components. The volatile components were effectively differentiated using principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and cluster analysis, revealing correlations between sensory attributes, key components, and electronic nose (E-nose). This research introduces a novel method for analyzing volatile aroma components in cigars, offering insights to enhance cigar quality and to foster the development of new products with unique aroma profiles.

Comparison of Different Deodorizing Treatments on the Flavor of Paddy Field Carp, Analyzed by the E-Nose, E-Tongue and Gas Chromatography-Ion Mobility Spectrometry.

Changes in the flavor and taste profiles of Paddy Field Carp after deodorization with perilla juice (PJ), cooking wine (CW) and a mixture of the two (PJ-CW) were analyzed using the E-nose, E-tongue, gas chromatography-ion mobility spectrometry (GC-IMS), free amino acid analysis and taste nucleotide analysis. The E-nose and E-tongue revealed that deodorization reduced the content of sulfur-containing compounds, enhanced umami, bitterness, sourness and astringency, and decreased saltiness. PCA and OPLS-DA analysis successfully distinguished between the effects of the treatments. Free amino acids increased from 8777.67 to 11,125.98 mg/100 g and umami amino acids increased from 128.24 to 150.37 mg/100 g after PJ-CW deodorization (p < 0.05). Equivalent umami concentration (EUC) comparisons showed that PJ-CW treatment produced the greatest synergistic umami enhancement (to 3.15 g MSG equiv./100 g). GC-IMS detected 52 aroma compounds; PJ treatment produced the greatest diversity of aldehydes, including heptanal, nonanal, hexanal, 3-methylbutanal, (E)-2-heptenal and (E,E)-2,4-heptadienal. The total content of volatile flavor compounds was the highest after PJ-CW treatment, and the content of many characteristic flavor substances (3-hydroxy-2-butanone, benzaldehyde, 5-methyl-2(3H)-furanone) increased. These findings provided a theoretical basis for the further development of deodorization methods for Paddy Field Carp.

Odor Fingerprinting of Chitosan and Source Identification of Commercial Chitosan: HS-GC-IMS, Multivariate Statistical Analysis, and Tracing Path Study.

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R2X = 0.866, R2Y = 0.996, Q2 = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications.

A configuration for cooling assisted organic solvent coated thin film microextraction after dispersive liquid-liquid microextraction method: A microextraction method for ultra-trace analyzing of volatile sample.

A combination of the dispersive liquid-liquid microextraction (DLLME) method based on the total vaporization procedure and cooling-assisted organic solvent-coated thin film microextraction (TFME) was applied for extracting chlorpyrifos (as the model compound). Based on the high thermal conductivity, a nickel foam thin film with the dimensions of 5.0 mm × 5.0 mm was used as a substrate for holding the organic solvent. Supporting thin film by organic solvent increases the thickness and contact area of the film relative to TFME or single drop microextraction (SDME) alone, resulting in a dramatic increase in the extraction efficiency. To protect the organic solvent and enhance the analyte distribution coefficient between the film and the vapor phase, a cooling system was applied. The proposed design was effective due to condensing the target analyte only on the uniform cooled thin film and not on the other regions in the extraction chamber. A corona discharge ionization source-ion mobility spectrometer was employed to identify the analyte. After optimizing the effective parameters, the limits of quantification (S/N = 10) and detection (S/N = 3) were calculated 0.1 and 0.03 µg L-1, respectively, and the dynamic range was measured between 0.1 and 7.0 µg L-1, with a determination coefficient of 0.9997. For three concentration levels of 0.1, 3.0, and 7.0 µg L-1, the relative standard deviations (n = 3) as the repeatability index were to be 6 %, 5 %, and 4 % for intra-day and 9 %, 6 %, and 5 % for inter-day, respectively. The enrichment factor was also calculated to be 3630 for the analyte concentration of 1.0 µg L-1. Well water, potato, and agricultural wastewater were analyzed as the real samples and the relative recovery values were measured between 92 % and 99 %. The accuracy of the proposed technique was validated by the European Standards EN 12393 method. In this approach, two steps of analyte extraction (DLLME and TFME) were used consecutively, resulting in better preconcentration and reduced matrix interference during cleaning-up.

A strategy to distinguish similar traditional Chinese medicines by liquid chromatography-mass spectrometry, electronic senses, and gas chromatography-ion mobility spectrometry: Marsdeniae tenacissimae Caulis and Paederiae scandens Caulis as examples.

INTRODUCTION: Marsdeniae tenacissimae Caulis (MTC), a popular traditional Chinese medicine, has been widely used in the treatment of tumor diseases. Paederiae scandens Caulis (PSC), which is similar in appearance to MTC, is a common counterfeit product. It is difficult for traditional methods to effectively distinguish between MTC and PSC. Therefore, there is an urgent need for a rapid and accurate method to identify MTC and PSC. OBJECTIVES: The aim is to distinguish between MTC and PSC by analyzing the differences in nonvolatile organic compounds (NVOCs), taste, odor, and volatile organic compounds (VOCs). METHODS: Liquid chromatography-mass spectrometry (LC-MS) was utilized to analyze the NVOCs of MTC and PSC. Electronic tongue (E-tongue) and electronic nose (E-nose) were used to analyze their taste and odor respectively. Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to analyze VOCs. Finally, multivariate statistical analyses were conducted to further investigate the differences between MTC and PSC, including principal component analysis, orthogonal partial least squares discriminant analysis, discriminant factor analysis, and soft independent modeling of class analysis. RESULTS: The results of this study indicate that the integrated strategy of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis can be effectively applied to distinguish between MTC and PSC. Using LC-MS, 25 NVOCs were identified in MTC, while 18 NVOCs were identified in PSC. The major compounds in MTC are steroids, while the major compounds in PSC are iridoid glycosides. Similarly, the distinct taste difference between MTC and PSC was precisely revealed by the E-tongue. Specifically, the pronounced bitterness in PSC was proven to stem from iridoid glycosides, whereas the bitterness evident in MTC was intimately tied to steroids. The E-nose detected eight odor components in MTC and six in PSC, respectively. The subsequent statistical analysis uncovered notable differences in their odor profiles. GC-IMS provided a visual representation of the differences in VOCs between MTC and PSC. The results indicated a relatively high relative content of 82 VOCs in MTC, contrasted with 32 VOCs exhibiting a similarly high relative content in PSC. CONCLUSION: In this study, for the first time, the combined use of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis has proven to be an effective method for distinguishing between MTC and PSC from multiple perspectives. This approach provides a valuable reference for the identification of other visually similar traditional Chinese medicines.

Assessing Per- and Polyfluoroalkyl Substances in Fish Fillet Using Non-Targeted Analyses.

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters. Here, we evaluated PFAS in fish fillets from recreational and drinking water sources in central North Carolina to assess whether PFAS are present in these fillets that would not be detected by conventional targeted methods. We used liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to collect full scan feature data, performed suspect screening using an in-house library of 100 PFAS for high confidence feature identification, searched for additional PFAS features using non-targeted data analyses, and quantified perfluorooctanesulfonic acid (PFOS) in the fillet samples. A total of 36 PFAS were detected in the fish fillets, including 19 that would not be detected using common targeted methods, with a minimum of 6 and a maximum of 22 in individual fish. Median fillet PFOS levels were concerningly high at 11.6 to 42.3 ppb, and no significant correlation between PFOS levels and number of PFAS per fish was observed. Future PFAS monitoring in this region should target more of these 36 PFAS, and other regions not considered heavily PFAS contaminated should consider incorporating non-targeted analyses into ongoing fish monitoring studies.

Development of a cyclic ion mobility spectrometry-mass spectrometry-based collision cross-section database of permethylated human milk oligosaccharides.

Human milk oligosaccharides (HMOs) are an important class of biomolecules responsible for the healthy development of the brain-gut axis of infants. Unfortunately, their accurate characterization is largely precluded due to a variety of reasons - there are over 200 possible HMO structures whereas only 10s of these are available as authentic analytical standards. Furthermore, their isomeric heterogeneity stemming from their many possible glycosidic linkage positions and corresponding α/ß anomericities further complicates their analyses. While liquid chromatography coupled to tandem mass spectrometry remains the gold standard for HMO analyses, it often times cannot resolve all possible isomeric species and thus warrants the development of other orthogonal approaches. High-resolution ion mobility spectrometry coupled to mass spectrometry has emerged as a rapid alternative to condensed-phase separations but largely has remained limited to qualitative information related to the resolution of isomers. In this work, we have assessed the use of permethylation to improve both the resolution and sensitivity of HMO analyses with cyclic ion mobility separations coupled with mass spectrometry. In addition to this, we have developed the first-ever high-resolution collision cross-section database for permethylated HMOs using our previously established calibration protocol. We envision that this internal reference database generated from high-resolution cyclic ion mobility spectrometry-mass spectrometry will greatly aid in the accurate characterization of HMOs and provide a valuable, orthogonal, approach to existing liquid chromatography-tandem mass spectrometry-based methods.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.