Tandem Mass Spectra Research Articles

MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search.

Liquid chromatography-mass spectrometry (LC-MS) based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition (DDA) mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum (MS2), often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a novel database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each MS2, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate (FDR) control. It is also uniquely suited for wide-window acquisition (WWA) data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Quantitation and Abundance of Xylazine and Xylazine Metabolites in Human Urine and Plasma by LC-MS/MS

Abstract The most common cause of drug overdose fatality in the US involves synthetic opioids such as fentanyl. Recently the Director of the White House Office of National Drug Control Policy designated fentanyl adulterated or associated with xylazine as an emerging threat to the US, and a national response plan will include xylazine testing. Xylazine is a clonidine analog and is not FDA-approved for use in humans but has been increasingly detected in unregulated illicit drugs supplies. Our understanding of xylazine detection, metabolism, and clinical impact on patients, however, is limited. The objectives of this study are to develop a liquid chromatography tandem mass spectrometry (LC-MS/MS) method to detect xylazine and xylazine metabolites in urine and plasma, and to investigate the presence and abundance of xylazine metabolites in remnant urine and plasma samples submitted for clinical testing. This is achieved by first collecting paired urine and serial plasma samples when urine tests positive for xylazine during routine clinical testing. Plasma samples were prepared for LC-MS/MS analysis by protein precipitation with methanol and xylazine-D6 internal standard. Urines were prepared by dilution with xylazine-D6, followed by a hydrolysis with beta-glucuronidase. All experiments were performed using an Aquity HPLC coupled to a Waters TQS-micro triple quadrupole mass spectrometer. LC separation utilized a C18 column with a 4-minute gradient from 2% to 98% mobile phase B (0.1% formic acid and 2mM ammonium acetate in A: water; B: methanol) with a flow rate of 400 μl/min at 50°C. Multiple reaction monitoring for xylazine and 4-OH-xylazine was optimized with commercial standards. Calibration curves for both analytes were prepared from 0.5 – 2,000 ng/mL in plasma, and 5 – 5,000 ng/mL in urine. Product ion scanning was used to detect four additional xylazine metabolites by spectral matching with previously published full scan tandem mass spectra. The highest intensity fragment ions observed in the product ion spectra were used to develop multiple reaction monitoring experiments for four additional xylazine metabolites, previously described as: sulfone-xylazine, oxo-xylazine, OH-sulfone-xylazine, and OH-oxo-xylazine, in 50 remnant urines and 70 individual plasmas. In remnant urines, xylazine concentrations ranged from less than 5 to greater than 5,000 ng/mL. In remnant plasmas, xylazine ranged from less than 0.5 to 158 ng/mL. Previously reported xylazine metabolites were detected in both urine and plasma, and relative peak area ratios were calculated as metabolite-to-xylazine in both matrices. In urine, the highest average peak area ratio (3.1) was observed for sulfone xylazine, after hydrolysis. In plasma, the highest average peak area ratio (1.4) was observed for oxo-xylazine. We were unable to absolutely quantitate either metabolite due to lack of available standards. Future studies include investigation on the association of xylazine exposure and plasma concentrations with clinical presentation and patient outcomes.

EMBL-MCF 2.0: an LC-MS/MS method and corresponding library for high-confidence targeted and untargeted metabolomics using low-adsorption HILIC chromatography

IntroductionOver the past two decades, liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has experienced significant growth, playing a crucial role in various scientific disciplines. However, despite these advance-ments, metabolite identification (MetID) remains a significant challenge. To address this, stringent MetID requirements were established, emphasizing the necessity of aligning experimental data with authentic reference standards using multiple criteria. Establishing dependable methods and corresponding libraries is crucial for instilling confidence in MetID and driving further progress in metabolomics.ObjectiveThe EMBL-MCF 2.0 LC-MS/MS method and public library was designed to facilitate both targeted and untargeted metabolomics with exclusive focus on endogenous, polar metabolites, which are known to be challenging to analyze due to their hydrophilic nature. By accompanying spectral data with robust retention times obtained from authentic standards and low-adsorption chromatography, high confidence MetID is achieved and accessible to the metabolomics community.MethodsThe library is built on hydrophilic interaction liquid chromatography (HILIC) and state-of-the-art low adsorption LC hardware. Both high-resolution tandem mass spectra and manually optimized multiple reaction monitoring (MRM) transitions were acquired on an Orbitrap Exploris 240 and a QTRAP 6500+, respectively.ResultsImplementation of biocompatible HILIC has facilitated the separation of isomeric metabolites with significant enhancements in both selectivity and sensitivity. The resulting library comprises a diverse collection of more than 250 biologically relevant metabolites. The methodology was successfully applied to investigate a variety of biological matrices, with exemplary findings showcased using murine plasma samples.ConclusionsOur work has resulted in the development of the EMBL-MCF 2.0 library, a powerful resource for sensitive metabolomics analyses and high-confidence MetID. The library is freely accessible and available in the universal .msp file format under the CC-BY 4.0 license: mona.fiehnlab.ucdavis.edu https://mona.fiehnlab.ucdavis.edu/spectra/browse?query=exists(tags.text:%27EMBL-MCF_2.0_HRMS_Library%27), EMBL-MCF 2.0 HRMS https://www.embl.org/groups/metabolomics/instrumentation-and-software/#MCF-library.

GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.

Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Pemuchiamides A and B, Proline-Rich Linear Lipopeptides, Isolated from a Marine Hormoscilla sp. Cyanobacterium.

Pemuchiamides A and B (1 and 2) were isolated from a marine Hormoscilla sp. cyanobacterium collected from Pemuchi Beach on Hateruma Island, Japan. Although 1 and 2 existed as a complex mixture of rotamers in chloroform-d, detailed analyses of their 2D NMR and tandem mass spectra revealed their planar structures, respectively. The absolute configurations of 1 and 2 were established via the degradation and derivatization reactions. Pemuchiamide A (1) exhibited potent growth-inhibitory activity against Trypanosoma brucei rhodesiense, the causative organism of African sleeping sickness, while 2 showed 10-fold weaker activity than 1. This result indicates that the presence of a hydroxy group at the C-3 position of the 4-aminobutanoic acid moiety negatively affects antitrypanosomal activity.

Impact of Protonation Sites on Collision-Induced Dissociation-MS/MS Using CIDMD Quantum Chemistry Modeling.

Protonation is the most frequent adduct found in positive electrospray ionization collision-induced mass spectra (CID-MS/MS). In a parallel report Lee, J. J. Chem. Inf. Model. 2024, 10.1021/acs.jcim.4c00760, we developed a quantum chemistry framework to predict mass spectra by collision-induced dissociation molecular dynamics (CIDMD). As different protonation sites affect fragmentation pathways of a given molecule, the accuracy of predicting tandem mass spectra by CIDMD ultimately depends on the choice of its protomers. To investigate the impact of molecular protonation sites on MS/MS spectra, we compared CIDMD-predicted spectra to all available experimental MS/MS spectra by similarity matching. We probed 10 molecules with a total of 43 protomers, the largest study to date, including organic acids (sorbic acid, citramalic acid, itaconic acid, mesaconic acid, citraconic acid, and taurine) as well as aromatic amines including uracil, aniline, bufotenine, and psilocin. We demonstrated how different protomers can converge different fragmentation pathways to the same fragment ions but also may explain the presence of different fragment ions in experimental MS/MS spectra. For the first time, we used in silico MS/MS predictions to test the impact of solvents on proton affinities, comparing the gas phase and a mixture of acetonitrile/water (1:1). We also extended applications of in silico MS/MS predictions to investigate the impact of protonation sites on the energy barriers of isomerization between protomers via proton transfer. Despite our initial hypothesis that the thermodynamically most stable protomer should give the best match to the experiment, we found only weak inverse relationships between the calculated proton affinities and corresponding entropy similarities of experimental and CIDMD-predicted MS/MS spectra. CIDMD-predicted mechanistic details of fragmentation reaction pathways revealed a clear preference for specific protomer forms for several molecules. Overall, however, proton affinity was not a good predictor corresponding to the predicted CIDMD spectra. For example, for uracil, only one protomer predicted all experimental MS/MS fragment ions, but this protomer had neither the highest proton affinity nor the best MS/MS match score. Instead of proton affinity, the transfer of protons during the electrospray process from the initial protonation site (i.e., mobile proton model) better explains the differences between the thermodynamic rationale and experimental data. Protomers that undergo fragmentation with lower energy barriers have greater contributions to experimental MS/MS spectra than their thermodynamic Boltzmann populations would suggest. Hence, in silico predictions still need to calculate MS/MS spectra for multiple protomers, as the extent of distributions cannot be readily predicted.

Molecular interaction between ammonium sulfate and secondary organic aerosol from styrene

Secondary organic aerosol (SOA) and inorganic aerosol (SIA) are two major constituents of PM2.5, which are well-studied separately. However, the molecular-level investigation of mechanical interactions between them is still limited, which will affect the understanding of toxicity and optical properties of PM2.5. Styrene contains a benzene ring and a highly reactive double bond, mainly contributing to urban SOA formation. Here, chamber experiments were conducted to explore the effects of the (NH4)2SO4 seeds, as an example of SIA, on styrene SOA formation under varying relative humidity (RH) conditions. The particle-phase products were determined based on a high-resolution orbitrap mass spectrometer. The results showed that in the styrene-H2O2-hv systems, (NH4)2SO4 seeds facilitated the gas-particle partitioning of SOA precursors, leading to slightly higher SOA yields than the unseeded systems at 8.5 % RH. While RH exceeded 44.3 %, the presence of (NH4)2SO4 seeds increased the particle liquid water content, thus enhancing the partitioning of hydrophilic H2O2 to particle phase and the further oxidation of SOA, finally decreasing SOA yields. In the styrene-NOx-hv systems, NH4+ and SO42− were involved in the particle-phase reactions, producing prominent nitrogen- and sulfur-containing compounds of C8H7O2N and C8H9O6NS. In the styrene-O3 dark reaction systems, the existence of (NH4)2SO4 seeds significantly changed the oligomer components under humid conditions. The particle-phase formation pathways of oligomers affected by (NH4)2SO4 seeds were also illustrated based on tandem mass spectra data. This study emphasizes the importance of inorganic seed effects on particle-phase reactions and improves our understanding of the SOA formation pathways in the ambient atmosphere.

Towards a universal method for middle-down analysis of antibodies via proton transfer charge reduction-Orbitrap mass spectrometry.

Modern mass spectrometry technology allows for extensive sequencing of the ~ 25kDa subunits of monoclonal antibodies (mAbs) produced by IdeS proteolysis followed by disulfide bond reduction, an approach known as middle-down mass spectrometry (MD MS). However, the spectral congestion of tandem mass spectra of large polypeptides dramatically complicates fragment ion assignment. Here, we report the development and benchmark of an MD MS strategy based on the combination of different ion fragmentation techniques with proton transfer charge reduction (PTCR) to simplify the gas-phase sequencing of mAb subunits. Applied on the liquid chromatography time scale using an Orbitrap Tribrid mass spectrometer, PTCR produces easy-to-interpret mass spectra with limited ion signal overlap. We demonstrate that the accurate estimation of the number of charges submitted to the Orbitrap mass analyzer after PTCR allows for the detection of charge-reduced product ions over a wide mass-over-charge (m/z) window with low parts per million m/z accuracy. Therefore, PTCR-based MD MS analysis increases not only sequence coverage, number of uniquely identified fragments, and number of assigned complementary ion pairs, but also the general confidence in the assignment of subunit fragments. This data acquisition method can be readily applied to any class of mAbs without an apparent need for optimization, and benefits from the high resolving power of the Orbitrap mass analyzer to return sequence coverage of individual subunits exceeding 80% in a single run, and > 90% when just two experiments are combined.

Identification of Omitted Pollutants in Environmental Water via In Situ Thin-Film Microextraction.

Sample preparation is an inevitable step in the screening workflow for the identification of unknown pollutants in the aquatic environment. However, the possible loss of pollutants during sample preparation has aroused serious concern but remains not effectively resolved. This study shows that high-risk pollutants omitted in solid-phase extraction (SPE) can be identified via in situ thin-film microextraction (TFME) coupled with high-resolution mass spectrometry. It was observed that a total of 541 features showed higher mass spectrometry signal intensity by using in situ TFME in comparison with SPE. Subsequently, 28 compounds were identified from the features with higher intensity by comparing the recorded tandem mass spectra with the online database and validating with standards. Notably, six out of these compounds were completely omitted using SPE, including a dye, drug, and industrial product. It was confirmed that the lower extraction efficiencies of SPE were attributed to the limited sample volumes, the losses of compounds during sample transportation and storage, and the entrapment of compounds in SPE columns. This study demonstrates that in situ TFME should be used as a supplementary technique to SPE for extending the coverage of pollutants in the screening workflows.

GP-Marker facilitates the analysis of intact glycopeptide quantitative data at different levels.

Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.

Prioritization, Identification, and Quantification of Emerging Contaminants in Recycled Textiles Using Non-Targeted and Suspect Screening Workflows by LC-ESI-HRMS.

Recycled textiles are becoming widely available to consumers as manufacturers adopt circular economy principles to reduce the negative impact of garment production. Still, the quality of the source material directly impacts the final product, where the presence of harmful chemicals is of utmost concern. Here, we develop a risk-based suspect and non-targeted screening workflow for the detection, identification, and prioritization of the chemicals present in consumer-based recycled textile products after manufacture and transport. We apply the workflow to characterize 13 recycled textile products from major retail outlets in Sweden. Samples were extracted and analyzed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). In positive and negative ionization mode, 20,119 LC-HRMS features were detected and screened against persistent, mobile, and toxic (PMT) as well as other textile-related chemicals. Six substances were matched with PMT substances that are regulated in the European Union (EU) with a Level 2/3 confidence. Forty-three substances were confidently matched with textile-related chemicals reported for use in Sweden. For estimating the relative priority score, aquatic toxicity and concentrations were predicted for 7416 features with tandem mass spectra (MS2) and used to rank the non-targeted features. The top 10 substances were evaluated due to elevated environmental risk linked to the recycling process and potential release at end-of-life.

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information—de novo peptide sequencing—is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo’s superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics

Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics

New Wine in Old Bottle: Crown Ether-Functionalized Digital Polymer toward Efficient MALDI-TOF MS/MS Decoding via a Classical Supramolecular Interaction.

Digital polymers (DPs), which serve as promising molecular-level storage media, have increasingly garnered interest. Their application significantly depends on the efficiency of the information writing (synthesis) and reading processes (sequencing). For reading, rational incorporation of weak bonds in the main chain was applied in most cases in order to improve readability of the tandem mass spectra (MS/MS), which would limit the chain length of DPs, thus reducing the information storage capacity. In this study, the introduction of commercially available crown ether (CE) at the terminus of digital oligo(γ-butyrolactone)s (DOBLs) significantly enhances the predictability and fidelity of matrix-assisted laser desorption/ionization time-of-flight tandem mass spectra (MALDI-TOF MS/MS), thus improving the decoding process. The use of crown ether, leveraging a well-established supramolecular interaction with alkali cations known since 1967, offers a strong affinity between ionization agents and CE motifs, to form a selective effect of the desired fragments during the tandem MS. This method is particularly effective for long-chain DPs, extending up to 32-mer, and allows for customizable fragmentation patterns. The incorporation of CE at the DP chain end presents a novel and efficient strategy for enhancing MS/MS readability and amplifying the information storage capacity of polymers.

Large-scale prediction of collision cross-section with very deep graph convolutional network for small molecule identification

Ion mobility spectrometry (IMS) is a promising analytical technique for mass spectrometry (MS)-based compound identification by providing collision cross-section (CCS) value as an additional dimension with structural information. Here, GraphCCS was proposed to accurately predict the CCS value and expand the coverage of CCS libraries. A new adduct encoding method was proposed to encode SMILES strings and adduct types of compounds into adduct graphs. GraphCCS extended its predictive capability to ten different adduct types. A very deep graph convolutional network with up to 40 GCN layers was built to predict CCS values from adduct graphs. A curated dataset with 12,775 experimental CCS values was used to train, validate, and test the GraphCCS model. The resulting CCS predictions achieved a median relative error (MedRE) of 0.94 % and a coefficient of determination (R2) of 0.994 on the test set. Results on external test sets showed that GraphCCS outperformed AllCCS2, CCSbase, SigmaCCS, and DeepCCS. Based on the developed GraphCCS method, a large-scale in-silico database was built, including 2,394,468 CCS values. Those CCS values can be used to filter false positives complementary to retention times and tandem mass spectra. Finally, the effectiveness of GraphCCS in assisting compound identification was tested on a mouse adrenal gland lipid dataset with 1,960 lipids. The results demonstrated that the in-silico CCS values combined with MS spectra and retention times can efficiently filter the false positive candidates.

PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.

Mobility-Assisted Pseudo-MS3 Sequencing of Protein Ions.

The sequencing of intact proteins within a mass spectrometer has many benefits but is frequently limited by the fact that tandem mass spectrometry (MS/MS) techniques often generate poor sequence coverages when applied to protein ions. To overcome this limitation, exotic MS/MS techniques that rely on lasers and radical chemistry have been developed. These techniques generate high sequence coverages, but they require specialized instrumentation, create products through multiple dissociation mechanisms, and often require long acquisition times. Recently, we demonstrated that protein ions can be dissociated in a trapped ion mobility spectrometry (TIMS) device prior to mobility separation in a commercial timsTOF. All generated product ions were distributed throughout the mobility dimension, and this separation enabled deconvolution of complex tandem mass spectra and could enable facile pseudo-MS3 interrogation of generated product ions with the downstream quadrupole and collision cell. A second activation step improves sequence coverage because the most labile bonds have been depleted during the first dissociation and subsequent dissociation events are more evenly distributed throughout the product ion backbone. In this work, we explore the potential of this mobility-assisted pseudo-MS3 (MAP) method on a commercial timsTOF and timsTOF Pro 2. We demonstrate that while MAP only generates 92% of the sequence coverage of the most effective MS/MS technique, it accomplished this feat in 1.5 min and could be facilely integrated with liquid chromatographic separations.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Chain electrospray ionization mass spectrometry for ultra-low volume sample analysis

In this work, chain electrospray ionization (chain-ESI) was developed to efficiently ionize trace samples for mass spectrometry analysis. The primary ion source was found to have the ability to induce secondary electrospray ionization with an extraordinarily low sample consumption rate in the picoliters per minute (pLs/min). This allows low volume sample to generate substantial tandem mass spectrum (MS2) data for metabolite annotations. Notably, chain-ESI can effectively prevent the electro-redox reaction in the process of electrospray, so as to reflect the native state of the analytes. Furthermore, from a single Broussonetia papyrifera (B. papyrifera) trichome and a single A549 cancer cell, 1426 and 617 metabolites were detected respectively. All of those observations demonstrated that chain-ESI offers the advantages of direct, rapid analysis with extreme-low volumes and high coverage, enabling the measurement of bio-information in low volume samples.