Tentative identification of phytochelatins, their derivatives, and Cd-phytochelatin complexes in Ocimum basilicum L. roots by Paper Spray Mass Spectrometry.

An unprecedented and direct PS-MS (paper spray ionization mass spectrometry) method was proposed for the detection of native peptides, that is, glutathiones (GSHs), homoglutathiones (hGSHs), and phytochelatins (PCs), in basil (Ocimum basilicum L.) roots before and after cadmium exposure. The roots were submitted to cold maceration followed by sonication with formic acid as the extractor solvent for sample preparation. PS-MS was used to analyze such extracts in the positive mode, and the results allowed for the detection of several GSHs, hGSHs, and PCs. Some of these PCs were not distinguished in the control samples, that is, basil roots not exposed to cadmium. Other PCs were noticed in both types of roots, uncontaminated and cadmium-contaminated, but the intensities were higher in the former samples. Moreover, long-time exposure to cadmium stimulated the formation of some of these PCs and their cadmium complexes. The results, therefore, provided some crucial insights into the defense mechanism of plants against an external stress condition due to exposure to a toxic heavy metal. The present study represents a promising alternative to investigate other crucial physiological processes in plants submitted to assorted stress conditions.

APEX2-Mediated Proximity Protein Labeling in Dictyostelium.

Largely due to its simplicity, while being more like human cells compared to other experimental models, Dictyostelium continues to be of great use to discover basic molecular mechanisms and signaling pathways underlying evolutionarily conserved biological processes. However, the identification of new protein interactions implicated in signaling pathways can be particularly challenging in Dictyostelium due to its extremely fast signaling kinetics coupled with the dynamic nature of signaling protein interactions. Recently, the proximity labeling method using engineered ascorbic acid peroxidase 2 (APEX2) in mammalian cells was shown to allow the detection of weak and/or transient protein interactions and also to obtain spatial and temporal resolution. Here, we describe a protocol for successfully using the APEX2-proximity labeling method in Dictyostelium. Coupled with the identification of the labeled proteins by mass spectrometry, this method expands Dictyostelium's proteomics toolbox and should be widely useful for identifying interacting partners involved in a variety of biological processes in Dictyostelium.

Proteomic Analysis of Dictyostelium discoideum by Mass Spectrometry.

The large-scale proteomic analysis of Dictyostelium discoideum has contributed to our understanding of intracellular as well as secreted proteins in this versatile model eukaryote. Mass spectrometry-based proteomic analysis is a robust, sensitive, and rapid analytical method for identification and characterization of proteins extracted from tissues, cells, cell fractions, or pull-down assays. The availability of core facilities which make proteomics inexpensive and easy to do has facilitated a wide range of research projects. In this chapter, we present a simple standard methodology to extract proteins and prepare samples from D. discoideum for mass spectrometry and methods to analyze the identified proteins.

Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics.

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.

INTRODUCTION: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these. OBJECTIVE: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD. METHODS: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling. RESULTS: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides. CONCLUSIONS: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease.

To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from -0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more "off-target" proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.

Anion Exchange Chromatography-Mass Spectrometry to Characterize Proteoforms of Alpha-1-Acid Glycoprotein during and after Pregnancy.

Alpha-1-acid glycoprotein (AGP) is a heterogeneous glycoprotein fulfilling key roles in many biological processes, including transport of drugs and hormones and modulation of inflammatory and immune responses. The glycoform profile of AGP is known to change depending on (patho)physiological states such as inflammatory diseases or pregnancy. Besides complexity originating from five N-glycosylation sites, the heterogeneity of the AGP further expands to genetic variants. To allow in-depth characterization of this intriguing protein, we developed a method using anion exchange chromatography (AEX) coupled to mass spectrometry (MS) revealing the presence of over 400 proteoforms differing in their glycosylation or genetic variants. More precisely, we could determine that AGP mainly consists of highly sialylated higher antennary structures with on average 16 sialic acids and 0 or 1 fucose per protein. Interestingly, a slightly higher level of fucosylation was observed for AGP1 variants compared to that of AGP2. Proteoform assignment was supported by integrating data from complementary MS-based approaches, including AEX-MS of an exoglycosidase-treated sample and glycopeptide analysis after tryptic digestion. The developed analytical method was applied to characterize AGP from plasma of women during and after pregnancy, revealing differences in glycosylation profiles, specifically in the number of antennae, HexHexNAc units, and sialic acids.

[Applications of high performance liquid chromatography-mass spectrometry in proteomics].

Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the "bottom-up" approach and the "top-down" approach. The "shotgun" method is a typical "bottom-up" strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. "Top-down" analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the "bottom-up" "top-down" and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

[Research progress of deep learning applications in mass spectrometry imaging data analysis].

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

[Microbial metaproteomics--From sample processing to data acquisition and analysis].

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

[Applications of chromatography in glycomics].

Glycomics, an emerging "omics" technology that was developed after genomics and proteomics, is a discipline that studies the composition, structure, and functions of glycomes in cells, tissues, and organisms. Glycomics plays key roles in understanding the laws of major life activities, disease prevention and treatment, and drug quality control and development. At present, the structural analysis of glycans relies mainly on mass spectrometry. However, glycans have low abundance in biological samples. In addition, factors such as variable monosaccharide compositions, differences in glycosidic bond positions and modes, diverse branching structures, contribute to the complexity of the compositions and structures of glycans, posing great challenges to glycomics research. Liquid chromatography can effectively remove matrix interferences and enhance glycan separation to improve the mass spectrometric response of glycans. Thus, liquid chromatography and liquid chromatography coupled with mass spectrometry are important technical tools that have been actively applied to solve these problems; these technologies play indispensable roles in glycomics research. Different studies have highlighted similarities and differences in the applications of various types of liquid chromatography, which also reflects the versatility and flexibility of this technology. In this review, we first discuss the enrichment methods for glycans and their applications in glycomics research from the perspective of chromatographic separation mechanisms. We then compare the advantages and disadvantages of these methods. Some glycan-enrichment modes include affinity, hydrophilic interactions, size exclusion, and porous graphitized carbon adsorption. A number of newly developed materials exhibit excellent glycan-enrichment ability. We enumerate the separation mechanisms of reversed-phase high performance liquid chromatography (RP-HPLC), high performance anion-exchange chromatography (HPAEC), hydrophilic interaction chromatography (HILIC), and porous graphitic carbon (PGC) chromatography in the separation and analysis of glycans, and describe the applications of these methods in the separation of glycans, glycoconjugates, and glyco-derivatives. Among these methods, HILIC and PGC chromatography are the most widely used, whereas HPAEC and RP-HPLC are less commonly used. The HILIC and RP-HPLC modes are often used for the separation of derived glycans. The ionization efficiency and detectability of glycans are significantly improved after derivatization. However, the derivatization process is relatively cumbersome, and byproducts inevitably affect the accuracy and completeness of the detection results. HPAEC and PGC chromatography exhibit good separation effects on nonderivative glycans, but issues related to the detection integrity of low-abundance glycans owing to their poor detection effect continue to persist. Therefore, the appropriate analytical method for a specific sample or target analyte or mutual verification must be selected. Finally, we highlight the research progress in various chromatographic methods coupled with mass spectrometry for glycomics analysis. Significant progress has been made in glycomics research in recent years owing to advancements in the development of chromatographic separation techniques. However, several significant challenges remain. As the development of novel separation materials and methods continues, chromatographic techniques may be expected to play a critical role in future glycomics research.

[Applications of native mass spectrometry and ultraviolet photodissociation in protein structure and interaction analysis].

Dynamic changes in the structures and interactions of proteins are closely correlated with their biological functions. However, the precise detection and analysis of these molecules are challenging. Native mass spectrometry (nMS) introduces proteins or protein complexes into the gas phase by electrospray ionization, and then performs MS analysis under near-physiological conditions that preserve the folded state of proteins and their complexes in solution. nMS can provide information on stoichiometry, assembly, and dissociation constants by directly determining the relative molecular masses of protein complexes through high-resolution MS. It can also integrate various MS dissociation technologies, such as collision-induced dissociation (CID), surface-induced dissociation (SID), and ultraviolet photodissociation (UVPD), to analyze the conformational changes, binding interfaces, and active sites of protein complexes, thereby revealing the relationship between their interactions and biological functions. UVPD, especially 193 nm excimer laser UVPD, is a rapidly evolving MS dissociation method that can directly dissociate the covalent bonds of protein backbones with a single pulse. It can generate different types of fragment ions, while preserving noncovalent interactions such as hydrogen bonds within these ions, thereby enabling the MS analysis of protein structures with single-amino-acid-site resolution. This review outlines the applications and recent progress of nMS and UVPD in protein dynamic structure and interaction analyses. It covers the nMS techniques used to analyze protein-small-molecule ligand interactions, the structures of membrane proteins and their complexes, and protein-protein interactions. The discussion on UVPD includes the analysis of gas-phase protein structures and interactions, as well as alterations in protein dynamic structures, and interactions resulting from mutations and ligand binding. Finally, this review describes the future development prospects for protein analysis by nMS and new-generation advanced extreme UV light sources with higher brightness and shorter pulses.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. AVAILABILITY AND IMPLEMENTATION: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Using peptide barcodes for simultaneous profiling of protein expression from mRNA.

RATIONALE: mRNA technology has begun to play a significant role in the areas of therapeutic intervention and vaccine development. However, optimizing the mRNA sequence that influences protein expression levels is a resource-intensive and time-consuming process. This study introduces a new method to accelerate the selection of sequences of mRNA for optimal protein expression. METHODS: We designed the mRNA sequences in such a way that a unique peptide barcode, corresponding to each mRNA sequence, is attached to the expressed protein. These barcodes, cleaved off by a protease and simultaneously quantified by mass spectrometry, reflect the protein expression, enabling a parallel analysis. We validated this method using two mRNAs, each with different untranslated regions (UTRs) but encoding enhanced green fluorescence protein (eGFP), and investigated whether the peptide barcodes could analyze the differential eGFP expression levels. RESULTS: The fluorescence intensity of eGFP, a marker of its expression level, has shown noticeable changes between the two UTR sequences in mRNA-transfected cells when measured using flow cytometry. This suggests alterations in the expression level of eGFP due to the influence of different UTR sequences. Furthermore, the quantified amount of peptide barcodes that were released from eGFP showed consistent patterns with these changes. CONCLUSIONS: The experimental findings suggest that peptide barcodes serve as a valuable tool for assessing protein expression levels. The process of mRNA sequence selection, aimed at maximizing protein expression, can be enhanced by the parallel analysis of peptide barcodes using mass spectrometry.

A learned score function improves the power of mass spectrometry database search.

MOTIVATION: One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. RESULTS: To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.

Mass spectrometry-based metabolomics study of nicotine exposure in THP-1 monocytes.

The tobacco alkaloid nicotine is known for its activation of neuronal nicotinic acetylcholine receptors. Nicotine is consumed in different ways such as through conventional smoking, e-cigarettes, snuff or nicotine pouches. The use of snuff has been associated with several adverse health effects, such as inflammatory reactions of the oral mucosa and oral cavity cancer. We performed a metabolomic analysis of nicotine-exposed THP-1 human monocytes. Cells were exposed to 5 mM of the alkaloid for up to 4 h, and cell extracts and medium subjected to untargeted liquid chromatography high-resolution mass spectrometry. Raw data processing revealed 17 nicotine biotransformation products. Among these, cotinine and nornicotine were identified as the two major cellular biotransformation products. The application of multi- and univariate statistical analyses resulted in the annotation, up to a certain level of identification, of 12 compounds in the cell extracts and 13 compounds in the medium that were altered by nicotine exposure. Of these, four were verified as methylthioadenosine, cytosine, uric acid, and L-glutamate. Methylthioadenosine levels were affected in both cells and the medium, while cytosine, uric acid, and L-glutamate levels were affected in the medium only. The effects of smoking on the pathways involving these metabolites have been previously demonstrated in humans. Most of the other discriminating compounds, which were merely tentatively or not fully identified, were amino acids or amino acid derivatives. In conclusion, our preliminary data suggest that some of the potentially adverse effects related to smoking may also be expected when nicotine is consumed via snuff or nicotine pouches.

Broad-spectrum and sensitive screening of more than 1000 compounds in equine urine using liquid chromatography/high-resolution mass spectrometry.

RATIONALE: To uphold the integrity of horseracing and equestrian sports, it is critical for an equine doping control laboratory to develop a comprehensive screening method to cover a wide range of target substances at the required detection levels in equine urine. METHODS: The procedure involved the enzymatic hydrolysis of 3 mL urine samples followed by solid-phase extraction using HF Bond Elut C18 cartridge. The resulting extracts were then separated on a C18 reversed-phase column and analyzed using liquid chromatography/high-resolution mass spectrometry (LC/HRMS) in both electrospray ionization positive and negative modes in two separate injections. The analytical data were obtained in full scan and product ion scan (PIS) modes in an 11 min LC run. RESULTS: The method can detect 1011 compounds (in both positive and negative ion modes). Over 95% of the target compounds have limits of detections (LODs) ≤10 ng/mL, and more than 50% of the LODs are ≤0.5 ng/mL. The lowest LOD can reach down to 0.01 ng/mL. The applicability of the method was demonstrated by the successful detection of prohibited substances in overseas and domestic equine urine samples. CONCLUSIONS: We have successfully developed a regular screening method for equine urine samples that can detect more than 1000 compounds at sub-ppb levels in both positive and negative ion modes with full scan and PIS using LC/HRMS. Furthermore, this method can theoretically be expanded to accommodate an unlimited number of prohibited substances in full-scan mode.

Ultrafast, Selective, and Highly Sensitive Nonchromatographic Analysis of Fourteen Cannabinoids in Cannabis Extracts, Δ8-Tetrahydrocannabinol Synthetic Mixtures, and Edibles by Cyclic Ion Mobility Spectrometry-Mass Spectrometry.

The diversity of cannabinoid isomers and complexity of Cannabis products pose significant challenges for analytical methodologies. In this study, we developed a method to analyze 14 different cannabinoid isomers in diverse samples within milliseconds by leveraging the unique adduct-forming behavior of silver ions in advanced cyclic ion mobility spectrometry-mass spectrometry. The developed method achieved the separation of isomers from four groups of cannabinoids: Δ3-tetrahydrocannabinol (THC) (1), Δ8-THC (2), Δ9-THC (3), cannabidiol (CBD) (4), Δ8-iso-THC (5), and Δ(4)8-iso-THC (6) (all MW = 314); 9α-hydroxyhexahydrocannabinol (7), 9ß-hydroxyhexahydrocannabinol (8), and 8-hydroxy-iso-THC (9) (all MW = 332); tetrahydrocannabinolic acid (THCA) (10) and cannabidiolic acid (CBDA) (11) (both MW = 358); Δ8-tetrahydrocannabivarin (THCV) (12), Δ8-iso-THCV (13), and Δ9-THCV (14) (all MW = 286). Moreover, experimental and theoretical traveling wave collision cross section values in nitrogen (TWCCSN2) of cannabinoid-Ag(I) species were obtained for the first time with an average error between experimental and theoretical values of 2.6%. Furthermore, a workflow for the identification of cannabinoid isomers in Cannabis and Cannabis-derived samples was established based on three identification steps (m/z and isotope pattern of Ag(I) adducts, TWCCSN2, and MS/MS fragments). Afterward, calibration curves of three major cannabinoids were established with a linear range of 1-250 ng·ml-1 for Δ8-THC (2) (R2 = 0.9999), 0.1-25 ng·ml-1 for Δ9-THC (3) (R2 = 0.9987), and 0.04-10 ng·ml-1 for CBD (4) (R2 = 0.9986) as well as very low limits of detection (0.008-0.2 ng·ml-1). Finally, relative quantification of Δ8-THC (2), Δ9-THC (3), and CBD (4) in eight complex acid-treated CBD mixtures was achieved without chromatographic separation. The results showed good correspondence (R2 = 0.999) with those obtained by gas chromatography-flame ionization detection/mass spectrometry.

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS.

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

Rapid Separation and Detection of Drugs in Complex Biological Matrix Using TD-CDI Mass Spectrometer.

The drug detection technology plays a pivotal role in the domains of pharmaceutical regulation and law enforcement. In this study, we introduce a method that combines thermal desorption corona discharge ionization (TD-CDI) with mass spectrometry for efficient drug detection. The TD-CDI module, characterized by its compact and simple design, enables the separation of analytes within seconds and real-time presentation of one or two analyte peaks on the mass spectrum most of the time, which reduces matrix interference and improves detection performance. Through experimental investigation, we studied the characteristics of TD-CDI for analyte separation and detection, even with the same mass number, and optimized the TD-CDI approach. TD-CDI-MS was employed for the rapid detection of drugs in various traditional medicine, food products, and human samples. Additionally, by utilizing TD-CDI for segmented hair direct analysis, it becomes possible to trace the drug usage cycle of individuals. This underscores the feasibility of the proposed analytical method within the realm of drug detection.