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To date, poly- and perfluoroalkyl substances (PFAS) represent a real threat for their environmental persistence, wide physicochemical variability, and their potential toxicity. Thus far a large portion of these chemicals remain structurally unknown. These chemicals, therefore, require the implementation of complex non-targeted analysis workflows using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) for their comprehensive detection and monitoring. This approach, even though comprehensive, does not always provide the much-needed analytical resolution for the analysis of complex PFAS mixtures such as fire-fighting aqueous film-forming foams (AFFFs). This study consolidates the advantages of the LC×LC technique hyphenated with high-resolution tandem mass spectrometry (HRMS/MS) for the identification of PFAS in AFFF mixtures. A total of 57 PFAS homolog series (HS) were identified in 3M and Orchidee AFFF mixtures thanks to the (i) high chromatographic peak capacity (n'2D,c ~ 300) and the (i) increased mass domain resolution provided by the "remainder of Kendrick Mass" (RKM) analysis on the HRMS data. Then, we attempted to annotate the PFAS of each HS by exploiting the available reference standards and the FluoroMatch workflow in combination with the RKM defect by different fluorine repeating units, such as CF2, CF2O, and C2F4O. This approach resulted in 12 identified PFAS HS, including compounds belonging to the HS of perfluoroalkyl carboxylic acids (PFACAs), perfluoroalkyl sulfonic acids (PFASAs), (N-pentafluoro(5)sulfide)-perfluoroalkane sulfonates (SF5-PFASAs), N-sulfopropyldimethylammoniopropyl perfluoroalkane sulfonamides (N-SPAmP-FASA), and N-carboxymethyldimethylammoniopropyl perfluoroalkane sulfonamide (N-CMAmP-FASA). The annotated categories of perfluoroalkyl aldehydes and chlorinated PFASAs represent the first record of PFAS HS in the investigated AFFF samples.

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Polyglycolic acid (PGA) is a biologically friendly material with a wide range of applications. The production of dimethyl oxalate using coal-based syngas and the hydrogenation of dimethyl oxalate can produce the polymerization raw material of PGA, glycolide, which requires a methyl glycolate polymerization and depolymerization process. The intermediate products of the production process were analyzed using gas chromatogram-mass spectrometry (GC-MS) and Orbitrap mass spectrometry (Orbitrap MS), which revealed the presence of cyclic and linear PGAs with different capped ends. The impurities present in the oligomer were mostly methyl-capped PGA and were retained in the subsequent depolymerization process to glycolide, solvent washing can be used to remove this part of the impurity and ultimately obtain a refined glycolide product. Furthermore, it is proposed that the use of the specialized Kendrick Mass Defect (KMD) to plot and analyze PGA compounds obtained using mass spectrometry can enable the direct classification of PGAs without the need for exact molecular formula assignment.

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Dalbergia odorifera is a natural product rich in pharmacological ingredients, but the comprehensive characterization and rapid profiling of active components remain a challenge. Thus, an integrated data mining and identification strategy was exploited to efficiently identify the chemical constituents and screen acetylcholinesterase inhibitors (AChEIs) through affinity ultrafiltration and ultra-high-performance liquid chromatography-mass spectrometry (AUF-UHPLC-MS). As a result, polygonal mass defect filtering, diagnostic product ions, and neutral loss rules were created for rapid structural classification and component identification. A total of 140 flavonoids were tentatively characterized, including 41 isoflavonoids, 23 flavanones, 21 isoflavans, 19 flavones and flavonols, 13 neoflavonoids, 11 isoflavanones, seven flavone glycosides, and five chalcones. Subsequently, six natural AChEIs including tectorigenin, fisetin, dalbergin, pterostilbene, isoliquiritigenin, and biochanin A were screened out using AUF-UHPLC-MS and molecular docking. Meanwhile, the AChE inhibitory activities of the six compounds were assessed in vitro, tectorigenin, fisetinand, and dalbergin have moderate inhibitory activity. In conclusion, a novel strategy for systematic characterization and further screening of active compounds in natural products was established, which provides a material basis for quality control of Dalbergia odorifera.

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UPLC-Q-TOF-MS combined with mass defect filtering strategies were applied for the phytochemical investigation of Harrisonia perforata, leading to the isolation of thirteen undescribed limonoids named haperforatones A-M (1-13) and seventeen known compounds (14-30). Particularly, haperforatones D-E (4-5) have an unprecedented A, B, C, D-seco-6, 7-nor-C-24-limonoid skeleton, structurally stripped of the five-membered lactone ring B and formed a double bond at the C-5 and C-10 positions. Their 2D structures and relative configurations were identified using spectroscopic data. The absolute configurations of 1, 4, and 6 were established via X-ray diffraction crystallography. All 30 compounds were evaluated for anti-inflammatory potential in LPS-induced Raw 264.7 cell lines. Among those tested compounds, the most potent activity against LPS-induced NO generation was demonstrated by haperforatone F (6), with the IC50 value of inhibition NO production of 7.2 µM. Additionally, 6 could significantly inhibit IL-1ß and IL-6 release and markedly downregulate the protein expression level of iNOS in the LPS-stimulated RAW264.7 cells at 10 µM. The possible mechanism of NO inhibition of 6 was also investigated using molecular docking, which revealed the interaction of compound 6 with the iNOS protein.

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Glycans, which are ubiquitously distributed on most proteins and cell surfaces, are a class of important biomolecules playing crucial roles in various biological processes such as molecular recognition and cellular communication. Modern mass spectrometry (MS) coupled with novel chemical probe labeling strategies has greatly advanced analysis of glycans. However, the requirement of high-throughput and robust quantitative analysis still calls for the development of more advanced tools. Recently, we devised isobaric multiplex reagents for carbonyl-containing compound (SUGAR) tags for 4-plex N-glycan analysis. To further improve the throughput, we utilized the mass-defect strategy and expanded the multiplexing capacity to 12 channels without changing the chemical structure of the SUGAR tag, achieving a threefold enhancement in throughput compared with the original design and managing to perform high-throughput N-glycan analysis in a single LC - MS/MS injection. Herein, we present detailed methods for the synthesis of 12-plex SUGAR isobaric tags, the procedure to release and label the N-glycans from proteins, and the analysis by high-resolution LC-MS/MS, as well as data processing to achieve multiplexed quantitative glycomics.

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Lignin is a promising renewable source of valuable organic compounds and environmentally benign materials. However, its involvement in economic circulation and the creation of new biorefining technologies require an understanding of its chemical composition and structure. This problem can be overcome by applying mass spectrometry analytical techniques in combination with advanced chemometric methods for mass spectra processing. The present study is aimed at the development of mass defect filtering to characterize the chemical composition of lignin at the molecular level. This study introduces a novel approach involving resolution-enhanced Kendrick mass defect (REKMD) analysis for the processing of atmospheric pressure photoionization Orbitrap mass spectra of lignin. The set of priority Kendrick fractional base units was predefined in model experiments and provided a substantially expanding available mass defect range for the informative visualization of lignin mass spectra. The developed REKMD analysis strategy allowed to obtain the most complete data on all the homologous series typical of lignin and thus facilitated the interpretation and assignment of elemental compositions and structural formulas to oligomers detected in extremely complex mass spectra, including tandem ones. For the first time, the minor modifications (sulfation) of lignin obtained in ionic liquid-based biorefining processes were revealed.

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Triterpenoid saponins, a major bioactive component of liquorice, possess high hydrophilicity and often co-occur with other impurities of similar polarity. Additionally, subtle structural differences of some triterpenoid saponins bring challenges to comprehensive characterisation. In this study, triterpenoid saponins of three Glycyrrhiza species were systematically analysed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF-MS) coupled with mass defect filtering (MDF). Firstly, comprehensive date acquisition was achieved using RRLC-Q-TOF-MS. Secondly, a polygonal MDF method was established by summarizing known and speculated substituents and modifications based on the core structure to rapidly screen potential triterpenoid saponins. Thirdly, based on the fragmentation patterns of reference compounds, an identification strategy for characterisation of triterpenoid saponins was proposed. The strategy divided triterpenoid saponins into three distinct classes. By this strategy, 98 triterpenoid saponins including 10 potential new ones were tentatively characterised. Finally, triterpenoid saponins of three Glycyrrhiza species were further analysed using principle component analysis (PCA) and orthogonality partial least squares discriminant analysis (OPLS-DA). Among these, 18 compounds with variable importance in projections (VIP) > 1.0 and P values < 0.05 were selected to distinguish three Glycyrrhiza species. Overall, our study provided a reference for quality control and rational use of the three species.

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With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

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The taproot of Aconitum carmichaelii Debeaux (AC), a poisonous Traditional Chinese Medicine, has been widely used to treat joint pain, rheumatism and dysmenorrhea. Fermentation is a traditional drug processing method that reduces toxicity or increases efficacy. However, the chemical composition of AC, especially fermented AC, has not been fully elucidated. Therefore, it is necessary to establish a method to characterize the chemical composition of raw and fermented AC. In this study, a structural feature-based comprehensive strategy was employed to identify the chemical components of raw and fermented AC. A highly selective method consisting of mass defect filtering (MDF), ring double bond (RDB), nitrogen rule, and feature MS fragments filtering was established using UPLC-Q-Orbitrap-MS. By the established method, 230 diterpene alkaloids were characterized in raw AC, including 108 amine, 68 monoester, and 54 diester diterpene alkaloids. 145 of them were potential new compounds. Totals of 466 diterpene alkaloids were identified in fermented AC, including 231 amine, 162 monoester, and 73 diester diterpene alkaloids. 397 of them were potential new compounds. Ester hydrolysis, hydroxylation, and demethylation were the major transformation pathways during fermentation. An integrated approach with highly selective based on the structural feature of analytes was established and applied to identify the chemicals in AC. The strategy showed great performance in improving the accuracy and coverage of the identification by using LC-MS.

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Arnebiae Radix, commonly known as "Zicao," can be easily confused with other compounding species, posing challenges for its clinical use. Here, we developed a comprehensive strategy to systematically characterize the diverse components across Arnebiae Radix and its three confusing species. First, an offline two-dimensional liquid chromatography (2D-LC) system integrating hydrophilic interaction chromatography (HILIC) and reverse phase (RP) separations was established, enabling effective separation and detection of more trace constituents. Second, a polygonal mass defect filtering (MDF) workflow was implemented to screen target ions and generate a precursor ion list (PIL) to guide multistage mass (MSn) data acquisition. Third, a three-step characterization strategy utilizing diagnostic ions and neutral losses was developed for rapid determination of molecular formulas, structure classes, and compound identification. This approach enabled systematic characterization of Arnebiae Radix and its three confusing species, with 437 components characterized including 112 shikonins, 22 shikonfurans, 144 phenolic acids, 131 glycosides, 18 flavonoids, and 10 other compounds. Additionally, 361, 230, 340, and 328 components were identified from RZC, YZC, DZC, and ZZC, respectively, with 142 common components and 30 characteristic components that may serve as potential markers for distinguishing the four species. In summary, this is the first comprehensive characterization and comparison of the phytochemical profiles of Arnebiae Radix and its three confusing species, advancing our understanding of this herbal medicine for quality control.

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Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

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Capturing the breadth of chemical exposures in utero is critical in understanding their long-term health effects for mother and child. We explored methodological adaptations in a Non-Targeted Analysis (NTA) pipeline and evaluated the effects on chemical annotation and discovery for maternal and infant exposure. We focus on lesser-known/underreported chemicals in maternal and umbilical cord serum analyzed with liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The samples were collected from a demographically diverse cohort of 296 maternal-cord pairs (n = 592) recruited in San Francisco Bay area. We developed and evaluated two data processing pipelines, primarily differing by detection frequency cut-off, to extract chemical features from non-targeted analysis (NTA). We annotated the detected chemical features by matching with EPA CompTox Chemicals Dashboard (n = 860,000 chemicals) and Human Metabolome Database (n = 3140 chemicals) and applied a Kendrick Mass Defect filter to detect homologous series. We collected fragmentation spectra (MS/MS) on a subset of serum samples and matched to an experimental MS/MS database within the MS-Dial website and other experimental MS/MS spectra collected from standards in our lab. We annotated ~72 % of the features (total features = 32,197, levels 1-4). We confirmed 22 compounds with analytical standards, tentatively identified 88 compounds with MS/MS spectra, and annotated 4862 exogenous chemicals with an in-house developed annotation algorithm. We detected 36 chemicals that appear to not have been previously reported in human blood and 9 chemicals that were reported in less than five studies. Our findings underline the importance of NTA in the discovery of lesser-known/unreported chemicals important to characterize human exposures.

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Sample preparation of complex, natural mixtures such as lignin prior to mass spectrometry analysis, however minimal, is a critical step in ensuring accurate and interference-free results. Modern shotgun-MS techniques, where samples are directly injected into a high-resolution mass spectrometer (HRMS) with no prior separation, usually still require basic sample pretreatment such as filtration and appropriate solvents for full dissolution and compatibility with atmospheric pressure ionization interfaces. In this study, sample preparation protocols have been established for a unique sample set consisting of a wide variety of degraded lignin samples from numerous sources and treatment processes. The samples were analyzed via electrospray (ESI)-HRMS in negative and positive ionization modes. The resulting information-rich HRMS datasets were then transformed into the mass defect space with custom R scripts as well as the open-source Constellation software as an effective way to visualize changes between the samples due to the sample preparation and ionization conditions as well as a starting point for comprehensive characterization of these varied sample sets. Optimized conditions for the four investigated lignins are proposed for ESI-HRMS analysis for the first time, giving an excellent starting point for future studies seeking to better characterize and understand these complex mixtures.

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Many membrane proteins form functional complexes that are either homo- or hetero-oligomeric. However, it is challenging to characterize membrane protein oligomerization in intact lipid bilayers, especially for polydisperse mixtures. Native mass spectrometry of membrane proteins and peptides inserted in lipid nanodiscs provides a unique method to study the oligomeric state distribution and lipid preferences of oligomeric assemblies. To interpret these complex spectra, we developed novel data analysis methods using macromolecular mass defect analysis. Here, we provide an overview of how mass defect analysis can be used to study oligomerization in nanodiscs, discuss potential limitations in interpretation, and explore strategies to resolve these ambiguities. Finally, we review recent work applying this technique to studying formation of antimicrobial peptide, amyloid protein, and viroporin complexes with lipid membranes.

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Ambiguous reports in the literature exist regarding the use and usefulness of formalin-fixed paraffin-embedded (FFPE) tissues in mass spectrometry imaging (MSI). Especially for the study of endogenous (non-tryptic) peptides, several studies have concluded that MSI on archived FFPE tissue bank samples is virtually impossible. We here illustrate that by employing a variant of MSI, called mass spectrometry histochemistry (MSHC), biomolecular tissue localization data are obtained that unequivocally comprise endogenous peptides. We here discuss different informatics steps in a data analysis workflow to help filter peptide-related features out of large and complex datasets generated by atmospheric pressure matrix-assisted laser desorption/ionization high-resolution (Orbitrap mass analyzer) MSHC. These include, in addition to accurate mass measurements, Kendrick mass defect filtering and isotopic distribution scrutiny.

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Lasianthus, belonging to Rubiaceae, has been verified to improve clinical syndrome in immune diseases (e.g., hepatitis, nephritis, and rheumatoid arthritis). Both the anti-inflammatory function and chemical composition of Lasianthus vary considerably between different species but few studies focus. So essential it is to explore lasianthus and further search for anti-inflammatory substances. The target of this artical is to analyze the anti-inflammatory activity and chemical composition of lasianthus of different species. And the subsequent active compounds were explored. Primary, the anti-inflammatory activity among seven species of lasianthus (e.g., L. fordii., L. wallichii., L. hookeri C., L. verticillatus., L. sikkimensis., L. appressihirtus., and L. hookeri var) were evaluated by vitro experiments (RAW 264.7 cells). Next, UHPLC/Q-Orbitrap-MS-based metabolomics and the mass defect filter (MDF) algorithm were performed to explore metabolites. In addition, principal component analysis (PCA) was to screen out differential compounds in seven species. Finally, the correlation analysis between activities and composition to rapidly discover the active compounds (compounds were verified pharmacologically). Among the 7 species of lasianthus, the L. fordii. and L. hookeri C indicated the best anti-inflammatory activity. Untargeted metabolomics and MDF show 112 compounds, classified into six dominant types (e.g., flavonoids, phenolic acids, alkaloids, iridoids, coumarins, and anthraquinones). Furthermore, 33 differential metabolites were confirmed by PCA. Then according to correlation analysis and pharmacological validation, 7 compounds IC50＜100 (e.g., scopoletin, asperulosidic acid, chlorogenic acid, ferulic acid, betaine, syringic acid, and emodin) were verified as anti-inflammatory compounds and conduct quantitative analysis. Metabolomics integrated with activities evaluation might be a rapid and effective strategy to explore the active compounds from natural products.

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The purpose of this study is to develop an improved comprehensive data filtering strategy, which was implemented primarily through the Microsoft Office platform's Excel software for rapid screening of potential 2-(2-phenylethyl)chromone (PEC) monomers and their dimers (PEC dimers) obtained from agarwood. A total of 108 PEC monomers and 30 PEC dimers in agarwood were characterized. In conclusion, the results obtained in this work could provide useful information for the future utilization of agarwood. In particular, it is the first time to conduct an in-depth analysis of the MS/MS fragmentation behavior of a large number of PEC monomers and PEC dimers, including the identification of substituent positions of them. The proposed data filtering strategy could improve the comprehensive characterization efficiency of complex components in spices.

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Lignin is the second most abundant biopolymer in nature and a promising renewable feedstock for the production of aromatic compounds, composite materials, sorbents, etc. Being a complex mixture of oligomeric molecules with an irregular structure, natural lignin is an extremely difficult object to study. Its molecular level characterization requires advanced analytical techniques among which atmospheric pressure photoionization Orbitrap mass spectrometry holds a promising place. In the present study, Kendrick mass defect (KMD) analysis was proposed to facilitate the visualization and interpretation of Orbitrap mass spectra of the biopolymer on an example of Siberian pine dioxane lignin preparation. The use of the typical guaiacylpropane structure C10H12O4 as a Kendrick base unit made it possible to effectively identify oligomer series with different polymerization degrees and structurally related compounds, as well as to reliably determine the elemental compositions and structures of oligomers with high molecular weights (> 1 kDa). For the first time, KMD analysis was applied to the interpretation of the complex tandem mass spectra of lignin oligomers, rapid discrimination of the product ion series, and the establishment of the main collision-induced dissociation pathways. It was demonstrated that especially promising was the use of KMD filtering in the study of broadband fragmentation tandem mass spectra, which allows for the structural characterization of all oligomers with a particular degree of polymerization.

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BACKGROUND: Global xenobiotic profiling (GXP) is to detect and structurally characterize all xenobiotics in biological samples using mainly liquid chromatography-high resolution mass spectrometry (LC-HRMS) based methods. GXP is highly needed in drug metabolism study, food safety testing, forensic chemical analysis, and exposome research. For detecting known or predictable xenobiotics, targeted LC-HRMS data processing methods based on molecular weights, mass defects and fragmentations of analytes are routinely employed. For profiling unknown xenobiotics, untargeted and LC-HRMS based metabolomics and background subtraction-based approaches are required. OBJECTIVE: This study aimed to evaluate the effectiveness of untargeted metabolomics and the precise and thorough background subtraction (PATBS) in GXP of rat plasma. METHODS: Rat plasma samples collected from an oral administration of nefazodone (NEF) or Glycyrrhizae Radix et Rhizoma (Gancao, GC) were analyzed by LC-HRMS. NEF metabolites and GC components in rat plasma were thoroughly searched and characterized via processing LC-HRMS datasets using targeted and untargeted methods. RESULTS: PATBS detected 68 NEF metabolites and 63 GC components, while the metabolomic approach (MS-DIAL) found 67 NEF metabolites and 60 GC components in rat plasma. The two methods found 79 NEF metabolites and 80 GC components with 96% and 91% successful rates, respectively. CONCLUSION: Metabolomics methods are capable of GXP and measuring alternations of endogenous metabolites in a group of biological samples, while PATBS is more suited for sensitive GXP of a single biological sample. A combination of metabolomics and PATBS approaches can generate better results in the untargeted profiling of unknown xenobiotics.

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The absorbed prototypes and metabolites of traditional Chinese medicines (TCMs) serves an important part in pharmacological action and clinical effects. However, the comprehensive characterization of which is facing actual or possible rigorous challenges due to the lack of data mining methods and the complexity of metabolite samples. Yindan Xinnaotong soft capsule (YDXNT), a typical traditional Chinese medicine prescription consisting of extracts from 8 herbal medicines, is widely used for the treatment of angina pectoris and ischemic stroke in the clinic. This study established a systematic data mining strategy based on ultra-high performance liquid chromatography tandem quadrupole-time-of-fight mass spectrometry (UHPLC-Q-TOF MS) for comprehensive metabolite profiling of YDXNT in rat plasma after oral administration. The multi-level feature ion filtration strategy was primarily conducted through the full scan MS data of plasma samples. All potential metabolites were rapidly fileted out from the endogenous background interference based on the background subtract and the chemical type specifically mass defect filter (MDF) windows including flavonoids, ginkgolides, phenolic acids, saponins, and tanshinones. As the MDF windows of certain types were overlapped, the screened-out potential metabolites were deeply characterized and identified according to their retention times (RT), integrating neutral loss filtering (NLF), diagnostic fragment ions filtering (DFIF), and further confirmed by reference standards. Thus, a total of 122 compounds, consisting of 29 prototype components (16 confirmed with reference standards) and 93 metabolites had been identified. This study provides a rapid and robust metabolite profiling method for researching complicated traditional Chinese medicine prescriptions.

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