Mass Spectrum Pattern Research Articles

Automated compound speciation, cluster analysis, and quantification of organic vapors and aerosols using comprehensive two-dimensional gas chromatography and mass spectrometry

Abstract. The advancement of analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS), enables the efficient separation of complex organics. Developing innovative methods for data processing and analysis is crucial to unlock the full potential of GC×GC–MS in understanding intricate chemical mixtures. In this study, we proposed an innovative method for the semi-automated identification and quantification of complex organic mixtures using GC×GC–MS. The method was formulated based on self-constructed mass spectrum patterns and the traversal algorithms and was applied to organic vapor and aerosol samples collected from the tailpipe emissions of heavy-duty diesel vehicles and the ambient atmosphere. Thousands of compounds were filtered, speciated, and clustered into 26 categories, including aliphatic and cyclic hydrocarbons, aromatic hydrocarbons, aliphatic oxygenated species, phenols and alkylphenols, and heteroatom-containing species. The identified species accounted for over 80 % of all the eluted chromatographic peaks at the molecular level. A comprehensive analysis of quantification uncertainty was undertaken. Using representative compounds, quantification uncertainties were found to be less than 37.67 %, 22.54 %, and 12.74 % for alkanes, polycyclic aromatic hydrocarbons (PAHs), and alkyl-substituted benzenes, respectively, across the GC×GC space, excluding the first and the last time intervals. From a source apportionment perspective, adamantane was clearly isolated as a potential tracer for heavy-duty diesel vehicle (HDDV) emissions. The systematic distribution of nitrogen-containing compounds in oxidized and reduced valences was discussed, and many of them served as critical tracers for secondary nitrate formation processes. The results highlighted the benefits of developing self-constructed models for the enhanced peak identification, automated cluster analysis, robust uncertainty estimation, and source apportionment and achieving the full potential of GC×GC–MS in atmospheric chemistry.

Towards understanding fermion masses and mixings

The Standard Model does not constrain the form of the Yukawa matrices and thus the origin of fermion mass hierarchies and mixing pattern remains puzzling. On the other hand, there are intriguing relations between the quark masses and their weak mixing angles, such as the well-known relationship [Formula: see text] for the Cabibbo angle, which may point towards specific textures of Yukawa matrices hypothesized by Harald Fritzsch at the end of the 70s. Though the original ansatz of Fritzsch is excluded by the experimental data, one can consider its minimal modification which consists in introducing an asymmetry between the 23 and 32 entries in the down-quark Yukawa matrix. We show that this structure is perfectly compatible with the present precision data on quark masses and CKM mixing matrix, and theoretically it can be obtained in the context of [Formula: see text] model with inter-family [Formula: see text] symmetry. We also discuss some alternative approaches which could give a natural description of the fermion mass spectrum and weak mixing pattern.

Mass spectra prediction with structural motif-based graph neural networks.

Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures. A prevalent analysis method involves spectral library searches, where unknown spectra are cross-referenced with a database. The effectiveness of such search-based approaches, however, is restricted by the scope of the existing mass spectra database, underscoring the need to expand the database via mass spectra prediction. In this research, we propose the Motif-based Mass Spectrum prediction Network (MoMS-Net), a GNN-based architecture to predict the mass spectra pattern utilizing the structural motif information of the molecule. MoMS-Net considers both a molecule and its substructures as a graph form, which facilitates the incorporation of long-range dependencies while using less memory compared to the graph transformer model. We evaluated our model over various types of mass spectra and showed the validity and superiority over the conventional models.

Promising potential of machine learning-assisted MALDI-TOF MS as an effective detector for Streptococcus suis serotype 2 and virulence thereof.

To the best of our knowledge, this study reveals a strong correlation between mass spectra pattern and virulence phenotype among S. suis for the first time. In order to make the findings applicable and to excavate the intrinsic information in the spectra, the classifiers based on the machine learning algorithms were established, and RF (Random Forest)-based models have achieved an accuracy of over 90%. Overall, this study will pave the way for virulent SS2 (Streptococcus suis serotype 2) rapid detection, and the important findings on the association between genotype and mass spectrum may provide a new idea for the genotype-dependent detection of specific pathogens.

NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter.

The present study introduces NOMspectra, a Python package for processing high resolution mass spectrometry data on complex systems of natural organic matter (NOM). NOM is characterized by multicomponent composition reflected as thousands of signals producing very complex patterns in high resolution mass spectra. This complexity sets special demands on the methods of data processing used for analysis. The developed NOMspectra package offers a comprehensive workflow for processing, analyzing, and visualizing information-rich mass spectra of NOM and HS including algorithms for filtering spectra, recalibrating, and assigning elemental compositions to molecular ions. Additionally, the package includes functions for calculating various molecular descriptors and methods for data visualization. A graphical user interface (GUI) has been developed to make a user-friendly interface for the proposed package.

Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data.

The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents' brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.

PB1974: MULTIVARIATE SPECTRAL BIOTYPING OF EXTRAMEDULLARY MULTIPLE MYELOMA

Background: Multiple myeloma (MM) is the second most common hematological malignancy of the elderly. The bone marrow is infiltrated by malignant plasma cells. MM may progress into so-called extramedullary disease (EMD) - EMD occurs when a subclone of malignant plasma cells migrates out of the bone marrow and infiltrates soft tissues. Despite recent progress, pathogenesis of EMD still needs to be clarified. Aims: We focused on the analysis of low molecular weight molecules in peripheral blood of 20 MM and 20 EMD patients using MALDI-TOF mass spectrometry to create a tool for identification of MM and EMD, and discrimination of individual types. Methods: Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) has become an indispensable research tool, which is used for analysis of biomolecules and various organic molecules. Artificial Neural Networks (ANN) are components of artificial intelligence inspired by biological neural networks. Using ANN, we can model complex non-linear systems, as previously published. In our previous study, we recorded mass spectra of MM and healthy donor samples. ANN specifically predicted MM samples with high sensitivity, specificity and accuracy. The same approach as applied on MM and EMD. Results: The RStudio was used for statistical analysis, where the data were evaluated using Principal Component Analysis (PCA) and Partial least squares discriminant analysis (PSL-DA). Using MALDI-TOF MS, it was possible to distinguish between samples of MM patients and healthy donors, as well as MM and EMD patients. Informative patterns in mass spectra served as inputs for ANN that specifically distinguished between healthy donors and patients. Summary/Conclusion: We demonstrated that using MALDI-TOF MS coupled with ANN is a useful tool that can distinguish between healthy donors and MM/EMD patients. Thus, it can be used as a fast alternative to conventional analyses. This study was supported by grant of the Ministry of Health of the Czech Republic, NU21-03-00076.

Multicellular tumor spheroids bridge the gap between two-dimensional cancer cells and solid tumors: The role of lipid metabolism and distribution

Previous studies demonstrated that three-dimensional (3D) multicellular tumor spheroids (MCTS) could more closely mimic solid tumors than two-dimensional (2D) cancer cells in terms of the spatial structure, extracellular matrix-cell interaction, and gene expression pattern. However, no study has been reported on the differences in lipid metabolism and distribution among 2D cancer cells, MCTS, and solid tumors. Here, we used HepG2 liver cancer cell lines to establish these three cancer models. The variations of lipid profiles and spatial distribution among them were explored by using mass spectrometry-based lipidomics and matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI). The results revealed that MCTS, relative to 2D cells, had more shared lipid species with solid tumors. Furthermore, MCTS contained more comparable characteristics than 2D cells to solid tumors with respect to the relative abundance of most lipid classes and mass spectra patterns. MSI data showed that 46 of 71 lipids had similar spatial distribution between solid tumors and MCTS, while lipids in 2D cells had no specific spatial distribution. Interestingly, most of detected lipid species in sphingolipids and glycerolipids preferred locating in the necrotic region to the proliferative region of solid tumors and MCTS. Taken together, our study provides the evidence of lipid metabolism and distribution demonstrating that MCTS are a more suitable in vitro model to mimic solid tumors, which may offer insights into tumor metabolism and microenvironment.

Matrix enrichment by black phosphorus improves ionization and reproducibility of mass spectrometry of intact cells, peptides, and amino acids

Intact (whole) cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) is an established method for biotyping in clinical microbiology as well as for revealing phenotypic shifts in cultured eukaryotic cells. Intact cell MALDI-TOF MS has recently been introduced as a quality control tool for long-term cultures of pluripotent stem cells. Despite the potential this method holds for revealing minute changes in cells, there is still a need for improving the ionization efficiency or peak reproducibility. Here we report for the first time that supplementation by fine particles of black phosphorus to the standard MALDI matrices, such as sinapinic and α-cyano-4-hydroxycinnamic acids enhance intensities of mass spectra of particular amino acids and peptides, presumably by interactions with aromatic groups within the molecules. In addition, the particles of black phosphorus induce the formation of small and regularly dispersed crystals of sinapinic acid and α-cyano-4-hydroxycinnamic acid with the analyte on a steel MALDI target plate. Patterns of mass spectra recorded from intact cells using black phosphorus-enriched matrix were more reproducible and contained peaks of higher intensities when compared to matrix without black phosphorus supplementation. In summary, enrichment of common organic matrices by black phosphorus can improve discrimination data analysis by enhancing peak intensity and reproducibility of mass spectra acquired from intact cells.

Triboionization in Discontinuous Atmospheric Pressure Inlet for a Miniature Ion Trap Mass Spectrometer.

Discontinuous atmospheric pressure interface (DAPI) consisting of a pinch valve, a silicone tube, and two metal capillaries has been widely used in miniature mass spectrometry. It is interesting that clear ion signals could be observed even when the extra ionization source was turned off. In-depth analysis suggested that this new ionization phenomenon known as triboionization is based on the surface friction on the inner surface of the silicone tube during the on/off of the pinch valve. In this study, triboionization in the DAPI of a miniature ion trap mass spectrometer was investigated. It was discovered that the signal intensity depended greatly on the material and the roughness of the silicone tube used in the DAPI. By rubbing the inner surface of the silicone tube, for example, the signal intensity can increase by nearly 20 times. Two connected pinch valves were developed to study the effects of the discharge pressure, the number, and the frequency of on/off of the pinch valve on triboionization, which were verified to have a large impact on the product ions. In addition, the humidity of the inner surface of the silicone tube impacted the signal intensity of product ions and the mass spectrum patterns, where the product ions were typically protonated ions. As the humidity increases, the signal intensity of analytes with high proton affinity increases accordingly. This triboionization source, which does not require heat, light, radiation, auxiliary gas, or solution, has been preliminarily proved to have potential for surface detection after continuous enrichment.

P-105: Mass spectrometry and artificial neural networks for discrimination of extramedullary Multiple Myeloma

<h3>Background</h3> Multiple myeloma (MM) is the second most common hematological malignancy of the elderly. The bone marrow is infiltrated by malignant plasma cells. MM may progress into so-called extramedullary disease (EMD). EMD occurs when a subclone of clonal plasma cells migrates out of the bone marrow and infiltrates soft tissues. <h3>Aims</h3> We focused on the analysis of low molecular weight molecules in peripheral blood of 20 MM and 20 EMD patients using MALDI-TOF mass spectrometry to create a diagnostic tool based on prediction by artificial neural network, which should distinguish different groups of diseases. <h3>Methods</h3> Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) has become an indispensable research tool, which is used for analysis of biomolecules and various organic molecules. Artificial Neural Networks (ANN) are components of artificial intelligence inspired by biological neural networks. Using ANN, we can model complex non-linear systems, as previously published. In our previous study, we recorded mass spectra of MM and healthy donor samples. ANN specifically predicted MM samples with high sensitivity, specificity and accuracy. <h3>Results</h3> The RStudio was used for statistical analysis, where the data were evaluated using Principal Component Analysis (PCA) and Partial least squares discriminant analysis (PSL-DA). Using MALDI-TOF MS, it was possible to distinguish between samples of MM patients and healthy donors, as well as MM and EMD patients. Informative patterns in mass spectra served as inputs for ANN that specifically distinguished between healthy donors and patients. <h3>Conclusion</h3> We demonstrated that using MALDI-TOF MS coupled with ANN is a useful tool that can distinguish between healthy donors and patients. Thus, it can be used as a fast alternative to conventional analyses. This study was supported by grants of the Ministry of Health of the Czech Republic, grant nr. NV18-08-00299, AZV 18-03-00203.

Qualitative screening and quantitative determination of multiclass water-soluble synthetic dyes in foodstuffs by liquid chromatography coupled to quadrupole Orbitrap mass spectrometry

A LC-Q-Orbitrap HRMS analytical method for both qualitative screening and quantitative determination of 90 synthetic dyes including ten groups of isomers in foods has been established. An in-house synthetic dyes database and characteristic ions were also developed. Based on Q-Orbitrap HRMS, mass spectrum and fragmentation patterns of synthetic dyes were studied, which indicated that double charged ions were usually the main precursor ions. Matrix effects were successfully eliminated by the C18 d-SPE clean-up coupled with dilute and shoot approach with methanol–water (1:4, v/v) in 100-fold. For most of the compounds, mean recoveries were satisfactory between 70% and 120% with RSD < 20% at three spiked level in the range of 0.025–1.0 mg/kg. The screening detection limits ranged from 0.025 − 1.0 mg/kg. Method validation showed that the established method was efficient, rapid and high-throughput, which has been successfully applied to the monitoring of these water-soluble synthetic dyes in foods.

Histopathologic, immunophenotypic, and proteomics characteristics of low-grade phyllodes tumor and fibroadenoma: more similarities than differences

Distinguishing low-grade phyllodes tumor from fibroadenoma is practically challenging due to their overlapping histologic features. However, the final interpretation is essential to surgeons, who base their management on the final pathology report. Patients who receive a diagnosis of fibroadenoma might not undergo any additional intervention while lumpectomy with wide margins is the standard of care for phyllodes tumor, which can have significant cosmetic consequences. We studied the clinical, immunophenotypic, and proteomics profiles of 31 histologically confirmed low-grade phyllodes tumor and 30 fibroadenomas. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) and immunohistochemistry for Ki-67, p53, β-catenin, and E-cadherin were performed on all cases. After the mass spectra for all 31 cases of low-grade phyllodes tumor and 30 cases of fibroadenoma were collected, an average peak value for all cases was generated. There was no significant difference in the overall mass spectra pattern in any of the peaks identified. There was also overlap in the percentage of cells staining positive for Ki-67, p53, β-catenin, and E-cadherin. The two groups of patients showed no statistically significant difference in age, tumor size, or disease-free survival. Neither group developed malignant transformation, distant metastases, or disease-related mortality. We have demonstrated low-grade phyllodes tumor and fibroadenoma to show significant overlapping clinical and proteomics features.

A 3-3-1 model with low scale seesaw mechanisms

We construct a viable 3-3-1 model with two SU(3)_L scalar triplets, extended fermion and scalar spectrum, based on the T^{prime } family symmetry and other auxiliary cyclic symmetries, whose spontaneous breaking yields the observed pattern of SM fermion mass spectrum and fermionic mixing parameters. In our model the SM quarks lighter than the top quark, get their masses from a low scale Universal seesaw mechanism, the SM charged lepton masses are produced by a Froggatt-Nielsen mechanism and the small light active neutrino masses are generated from an inverse seesaw mechanism. The model is consistent with the low energy SM fermion flavor data and successfully accommodates the current Higgs diphoton decay rate and predicts charged lepton flavor violating decays within the reach of the forthcoming experiments.

Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

Quantitative analysis of fatty acids in &lt;em&gt;Prosopis laevigata&lt;/em&gt; flour

Ripe mesquite pods are widely consumed by humans and animals in arid and semi-arid areas for their protein, carbohydrate, crude fiber and fat contents. The goal of this work is to identify and to quantify the fatty acid profile of flour from mesquite pods. Structural assignments were confirmed by the analysis of fragmentation patterns of mass spectra obtained by GC-MS. The results showed that 75% of the fatty acids were unsaturated, of which linoleic acid was predominant, while palmitic and stearic acids, and saturated fatty acids were found in minor proportions.

An automated workflow for MALDI-ToF mass spectra pattern identification on large data sets: An application to detect aneuploidies from pregnancy urine

Abstract Urine from first trimester pregnancies has been found to be rich in information related to aneuploidies and other clinical conditions. Mass spectral analysis derived from matrix assisted laser desorption ionization (MALDI) time of flight (ToF) data has been proven to be a cost effective method for clinical diagnostics. However, urine mass spectra are complex and require data modelling frameworks. Therefore, computational approaches that systematically analyse big data generated from MALDI-ToF mass spectra are essential. To address this issue, we developed an automated workflow that successfully processed large data sets from MALDI-ToF which is 100-fold faster than using a common software tool. Our method performs accurate data quality control decisions, and generates a comparative analysis to extract peak intensity patterns from a data set. We successfully applied our framework to the identification of peak intensity patterns for Trisomy 21 and Trisomy 18 gestations on data sets from maternal pregnancy urines obtained in the UK and China. The results from our automated comparative analysis have shown characteristic patterns associated with aneuploidies in the first trimester pregnancy. Moreover, we have shown that the intensity patterns depended on the population origin, gestational age, and MALDI-ToF instrument.

2026. Phenotypic and Molecular Characterization of Drug Resistance and Biofilm Production in Stenotrophomonas maltophilia Obtained in a 10-Year Period from a Mexican Hospital

Background Stenotrophomonas maltophilia is an emerging drug-resistant opportunistic pathogen related with healthcare-associated infections. The aim was to perform a phenotypic and molecular characterization of drug resistance and biofilm formation in S. maltophilia obtained in a 10-year period from a Mexican Hospital.Methods S. maltophilia isolates from a 10-year period from a tertiary care hospital in Mexico were identified by MALDI-TOF MS. Antimicrobial susceptibility was determined by the broth microdilution method. Drug resistance-associated genes were analyzed by PCR. Biofilm formation and its composition was assessed by crystal violet staining, and detachment assays, respectively. Biofilm production-associated genes were analyzed by PCR. MALDI-TOF mass spectra patterns were analyzed to search markers of drug resistance and biofilm production using the ClinProTools software.ResultsIn the 10-year study period, 258 S. maltophilia isolates were identified, with high resistance detected for ceftazidime (48.4%), cefepime (31.6%), ciprofloxacin (25.0%), trimethoprim-sulfamethoxazole (18.8%), and lower resistance to chloramphenicol (13.6%) and levofloxacin (2.2%). Drug resistance was not associated to the presence of the sul1 (4.1%), sul2 (0.5%), sul3 (0.0%) or ISCR (0.0%) genes. Overexpression of the SmeABC efflux pump was associated to higher drug resistance (P < 0.05). Overall, 97.7% isolates were biofilm producers, i.e., 41.7% were strong producers; 32.0% were moderate, and 26.2% were weak producers. Biofilm composition analysis showed similar percentages for carbohydrates, proteins and DNA in all isolates (36.0, 39.7 and 40.9%, respectively). Biofilm-associated genes fsnR, rmlA, rpfF, xanB and spgM were detected in 94.0, 75.3, 28.6, 17.2% and 0.0% of isolates, respectively. Four potential biomarker peaks were detected in rmlA positive biofilm-producing isolates (P < 0.05).ConclusionMost of S. maltophilia clinical isolates showed high antimicrobial resistance levels and were able to produce biofilm. Biofilm biomass composition of these isolates showed a similar proportion of polysaccharides, proteins, and DNA. The presence of the rmlA gene was associated with biofilm production. Four potential markers of the presence of the biofilm production-associated rmlA gene were detected by mass spectrometry.Disclosures All authors: No reported disclosures.

Study of the structural ligand effects on the fragmentation pattern of some Schiff base complexes of V (IV), Cu(II) and Ni (II) against the IR laser radiation using Matrix-free LDI-TOF technique

In this work, the time of flight (TOF) mass spectra of several Schiff base complexes of V(IV), Ni (II) and Cu(II) were recorded using matrix-free laser desorption-ionization (Direct-LDI) technique at λ = 1064 nm. Two different substrates including simple aluminum foil and its anodized form were used. The change in the mass spectra patterns due to the structural variation of the ligand for the complexes with similar central metal atoms was studied. It was found that specific substitutions in the structure of ligand decrease the fragmentation of the complex against the laser radiation, so that the peak related to the precursor ion appears in the mass spectra. It was also observed that the type of substrate affects the fragmentation pattern of the complexes.

Development of a mass spectrometry method for the characterization of a series of chitosan

Chitosan has multiple biological activities, but a sensitive and rapid characterization method is yet to be developed for its further application. This study presented an optional mass spectrometry method for the characterization of chitosan. Nine kinds of chitosan (degree of deacetylation (DD), 63.08%–89.06% & MW, 106.1–485.0 kDa) were adopted for the method development. Most species of chitosan, detected by an ESI-MS technique, were observed below 1000 m/z, which seemed that only chito-oligosaccharide (COS) impurities were detected. Then, a sensitive UPLC-ESI-MS/MS method was established to assess the COS impurities in chitosan, and no COS impurities were detected. However, dissociation of chitosan and COSs in the ESI source were observed, and then the mass spectra patterns were deeply evaluated via an accurate Q-TOF mass spectrometer. Our research demonstrated that the mass spectra of COSs and chitosan resulted from the dissociation of glycosidic linkage and dehydration. Although the distribution of GlcN and GlcNAc units in these chitosan samples might be different, similar dissociation efficiencies were observed. Furthermore, good linearities were obtained between the intensities of product ions, detected by an UPLC-pseuedo-MS2 method, and DDs determined by conventional method. This method could be suitable for the DD determination and quantitative analysis of chitosan.