Comprehensive workflow encompassing discovery, verification, and quantification of indicator peptide in snail mucin using LC-quadrupole Orbitrap high-resolution tandem mass spectrometry

Peptidomics analysis was conducted using high-resolution tandem mass spectrometry (MS2) to determine the peptide profile of snail-derived mucin extract (SM). The study was also aimed to identify an indicator peptide and validate a quantification method for this peptide. The peptide profiling and identification were conducted using discovery-based peptidomics analysis employing data-dependent acquisition, whereas the selected peptides were verified and quantified using parallel reaction monitoring acquisition. Among the 16 identified peptides, the selected octapeptide (TEAPLNPK) was quantified via precursor ion ionization (m/z 435.2400), followed by quantification of the corresponding quantifier ion fragment (m/z 639.3824) using MS2. The quantification method was optimized and validated in terms of specificity, linearity, accuracy, precision, and limit of detection/quantification. The validated method accurately quantified the TEAPLNPK content in the SM as 7.5 ± 0.2 μg/g. Our study not only identifies an indicator peptide from SM but also introduces a novel validation method, involving precursor ion ionization and quantification of specific fragments. Our findings may serve as a comprehensive workflow for the monitoring, selection, and quantification of indicator peptides from diverse food resources.

Mass spectrometry-based quantification of immunostimulatory gliadin proteins and peptides in coloured wheat varieties: Implications for Celiac Disease

Pigmented wheat varieties (Triticum aestivum spp.) are getting increasingly popular in modern nutrition and thoroughly researched for their functional and nutraceutical value. The colour of these wheat grains is caused by the expression of natural pigments, including carotenoids and anthocyanins, that can be restricted to either the endosperm, pericarp and/or aleurone layers. While contrasts in phytochemical synthesis give rise to variations among purple, blue, dark and yellow grain's antioxidant and radical scavenging capacities, little is known about their influence on gluten proteins expression, digestibility and immunogenic potential in a Celiac Disease (CD) framework. Herein, it has been found that the expression profile and immunogenic properties of gliadin proteins in pigmented wheat grains might be affected by anthocyanins and carotenoids upregulation, and that the spectra of peptide released upon simulated gastrointestinal digestion is also significantly different. Interestingly, anthocyanin accumulation, as opposed to carotenoids, correlated with a lower immunogenicity and toxicity of gliadins at both protein and peptide levels. Altogether, this study provides first-level evidence on the impact modern breeding practices, seeking higher expression levels of health promoting phytochemicals at the grain level, may have on wheat crops functionality and CD tolerability.

Comprehensive profiling of ceramides in human serum by liquid chromatography coupled to tandem mass spectrometry combining data independent/dependent acquisition modes

Ceramides are sphingolipids with a structural function in the cell membrane and are involved in cell differentiation, proliferation and apoptosis. Recently, these chemical species have been pointed out as potential biomarkers in different diseases, due to their abnormal levels in blood. In this research, we present an overall strategy combining data-independent and dependent acquisitions (DIA and DDA, respectively) for identification, confirmation, and quantitative determination of ceramides in human serum. By application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in DIA mode we identified 49 ceramides including d18:1, d18:0, d18:2, d16:1, d17:1 and t18:0 species. Complementary, quantitative determination of ceramides was based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC-MS/MS in DDA to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL-1 level, the intra-day and between-days variability were below 20 and 25 %, respectively; and the accuracy, expressed as bias, was always within ±25 %. The proposed method was tested with the CORDIOPREV cohort in order to obtain a qualitative and quantitative profiling of ceramides in human serum. This characterization allowed identifying d18:1 ceramides as the most concentrated with 70.8% of total concentration followed by d18:2 and d18:0 with 13.0 % and 8.8 %, respectively. Less concentrated ceramides, d16:1, d17:1 and t18:0, reported a 7.1 % of the total content. Combination of DIA and DDA LC-MS/MS analysis enabled to profile qualitative and quantitatively ceramides in human serum.

A Novel Adductomics Workflow Incorporating FeatureHunter Software: Rapid Detection of Nucleic Acid Modifications for Studying the Exposome

Exposure to the physicochemical agents that interact with nucleic acids (NA) may lead to modification of DNA and RNA (i.e., NA modifications), which have been associated with various diseases, including cancer. The emerging field of NA adductomics aims to identify both known and unknown NA modifications, some of which may also be associated with proteins. One of the main challenges for adductomics is the processing of massive and complex data generated by high-resolution tandem mass spectrometry (HR-MS/MS). To address this, we have developed a software called "FeatureHunter", which provides the automated extraction, annotation, and classification of different types of key NA modifications based on the MS and MS/MS spectra acquired by HR-MS/MS, using a user-defined feature list. The capability and effectiveness of FeatureHunter was demonstrated by analyzing various NA modifications induced by formaldehyde or chlorambucil in mixtures of calf thymus DNA, yeast RNA and proteins, and by analyzing the NA modifications present in the pooled urines of smokers and nonsmokers. The incorporation of FeatureHunter into the NA adductomics workflow offers a powerful tool for the identification and classification of various types of NA modifications induced by reactive chemicals in complex biological samples, providing a valuable resource for studying the exposome.

Identification and Targeted Quantification of Endogenous Neuropeptides in the Nematode Caenorhabditis elegans Using Mass Spectrometry

The nematode Caenorhabditis elegans lends itself as an excellent model organism for peptidomics studies. Its ease of cultivation and quick generation time make it suitable for high-throughput studies. The nervous system, with its 302 neurons, is probably the best-known and studied endocrine tissue. Moreover, its neuropeptidergic signaling pathways display numerous similarities with those observed in other metazoans. Here, we describe two label-free approaches for neuropeptidomics in C. elegans: one for discovery purposes, and another for targeted quantification and comparisons of neuropeptide levels between different samples. Starting from a detailed peptide extraction procedure, we here outline the liquid chromatography tandem mass spectrometry (LC-MS/MS) setup and describe subsequent data analysis approaches.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

Untargeted and targeted LC-MS and data processing workflow for the comprehensive analysis of oligopeptides from cyanobacteria

Cyanobacteria produce a plethora of structurally diverse bioactive secondary metabolites, including cyanotoxins which pose a serious threat to humans and other living organisms worldwide. Currently, a wide variety of mass spectrometry-based methods for determination of microcystins (MCs), the most commonly occurring and studied class of cyanotoxins, have been developed and employed for research and monitoring purposes. The scarcity of commercially available reference materials, together with the ever-growing range of mass spectrometers and analytical approaches, make the accuracy of quantitative analyses a critical point to be carefully investigated in view of a reliable risk evaluation. This study reports, a comparative investigation of the qualitative and quantitative MCs profile obtained using targeted and untargeted liquid chromatography-mass spectrometry approaches for the analyses of cyanobacterial biomass from Lake Kastoria, Greece. Comparison of the total MCs content measured by the two approaches showed good correlation, with variations in the range of 3.8-13.2%. In addition, the implementation of an analytical workflow on a hybrid linear ion trap/orbitrap mass spectrometer is described, based on combining data-dependent acquisition and a powerful database of cyanobacterial metabolites (CyanoMetDB) for the annotation of known and discovery of new cyanopeptides. This untargeted strategy proved highly effective for the identification of MCs, microginins, anabaenopeptins, and micropeptins. The systematic interpretation of the acquired fragmentation patterns allowed the elucidation of two new MC structural variants, MC-PrhcysR and MC-Prhcys(O)R, and proposal of structures for two new microginins, isomeric cyanostatin B and MG 821A, and three isomeric micropeptins at m/z 846.4715, 846.4711 and 846.4723.

Is non-targeted data acquisition for target analysis (nDATA) in mass spectrometry a forward-thinking analytical approach?

Targeted mass spectrometry is extensively used for the quantitative measurement of various molecules present in complex matrices. It is certainly one of the most important analytical duties in a mass spectrometry laboratory. Systematic development of selected-reaction monitoring (SRM), multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) methods for targeted mass spectrometry-based analysis were performed without considering future opportunities. The advancement of hardware and software technologies have resulted in more resolution, more accuracy, more speed and more depth. For sure, SRM, MRM or PRM acquisitions can quantify molecules very accurately at trace levels. However, it does not provide datasets allowing future data mining. Obviously, we cannot truly quantify something that we don't know is there. However, using non-targeted data acquisition for target analysis (nDATA), we can generate a MS¹ and MS² digital libraries of each sample providing future proof datasets. This is instrumental for data mining following new questions potentially arising in time permitting new and deeper processing and interpretation. This perspective article provides thoughts on why we believe it is time to question the status quo in targeted mass spectrometry.

Combining data acquisition modes in liquid-chromatography-tandem mass spectrometry for comprehensive determination of acylcarnitines in human serum

Acylcarnitines (ACs) are metabolites involved in fatty acid β-oxidation and organic acid metabolism. Metabolic disorders associated to these two processes can be evaluated by determining the complete profile of ACs. In this research, we present an overall strategy for identification, confirmation, and quantitative determination of acylcarnitines in human serum. By this strategy we identified the presence of 47 ACs from C2 to C24 with detection of the unsaturation degree by application of a data-independent acquisition (DIA) liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Complementary, quantitative determination of ACs is based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC-MS/MS in data-dependent acquisition (DDA) to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL^-1 level, the intra-day and between-day variability were below 15-20%, respectively; and the accuracy, expressed as bias, was always within ± 25%. The proposed method was tested with 40 human volunteers to determine the relative concentration of ACs in serum and identify predominant forms. Significant differences were detected by comparing the ACs profile of obese versus non-obese individuals.

Mass spectrometry-based ganglioside profiling provides potential insights into Alzheimer's disease development

Gangliosides are a family of glycosphingolipids which are particularly enriched in the nervous system. They play crucial roles in neuroprotection and neurological diseases. Alzheimer's disease (AD) is a neurodegenerative disease with cognitive, judgment and memory dysfunction. In this study, a mass spectrometry-based data-dependent acquisition method assisted with fragmentation characteristics screening by computer algorithm was developed for qualitative and quantitative analysis of gangliosides at low concentration. The developed method was applied to obtain detailed ganglioside species content in hippocampus of model mice (APPswe/PS1dE9 transgenic mice) with AD at 3- to 8-month-old. Up-regulated acetylated and N-acetylgalactosaminylated ganglioside species, and the down-regulated major gangliosides were observed with the development of AD from early to late stage. We speculated that deterioration of AD may be related to the acetylation/N-acetylgalactosaminylation transformation of complex gangliosides due to the inhibition of GD3 synthase activity. Moreover, the ganglioside species di-O-Ac-GT1a (d36:1), O-Ac-GD1b (d36:1) and O-Ac-GD1b (d36:0) were considered as the time-coursed biomarkers, and O-Ac-GT1a (d36:2) could be a candidate for early diagnosis of AD.

Global identification and determination of the major constituents in Kai-Xin-San by ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry and gas chromatography-mass spectrometry

Kai-Xin-San (KXS) is a traditional Chinese medicine (TCM) formula containing four herbal medicines: Ginseng Radix Rhizoma, Polygalae Radix, Poria and Acori Tatarinowii Rhizoma. A large number of pharmacological studies in vitro and in vivo have shown that KXS is characterized by anti-depression, anti-Alzheimer's disease, anti-oxidation and other activities. However, the pharmacodynamic substance basis studies of KXS are hitherto quite limited. Here, KXS was identified and determined by ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry (UPLC-Q-Orbitrap MS) and gas chromatography-mass spectrometry (GC-MS). Firstly, the data-dependent acquisition mode (DDA) of UPLC-Q-Orbitrap MS combined with the inclusion list were used to collected the chemical composition. The chemical constituents of KXS were identified by local database on compound discoverer™ 3.1 software and Xcalibur 4.1 software. With the use of this approach, a total of 211 compounds were identified from KXS. Wherein 60 compounds were from Ginseng Radix Rhizoma, 40 compounds were from Poria, and 111 compounds were from Polygala Radix, respectively. Secondly, 105 volatile constituents were identified by GC-MS analysis, which were mainly derived from Acori Tatarinowii Rhizoma. Besides, an adjusted parallel reaction monitoring method was established and validated to quantify the seventeen major compounds in different herbal medicines of KXS, which were chosen as the benchmarked substances to evaluate the quality of KXS. In conclusion, this study provided a generally applicable strategy for global metabolite identification of the complicated components and determination of multi-component content in traditional Chinese medicines.

Detection of Residual Host Cell Proteins in Biotherapeutic Drugs by Concatenated 2D LC-MS/MS

A sensitive and reliable two-dimensional LC-MS/MS method is described, which detects low level (≥10 ppm) host cell proteins (HCPs) in monoclonal antibody (mAb) drug products. This method applies a high pH-low pH two-dimensional reversed phase (RP) LC-MS/MS approach in conjunction with offline fraction concatenation, and uses a tandem column configuration for the second dimension RPLC. Direct database searching of MS/MS data through data-dependent acquisition (DDA) can be performed to identify the residual HCPs. The method impacts pharmaceutical company practices by using advanced LC-MS/MS technology to ensure product quality and patient safety.

Data-dependent acquisition based high-resolution mass spectrum for trace Alternaria mycotoxin analysis and sulfated metabolites identification

Alternaria mycotoxins are food-related compounds that are mainly produced by Alternaria fungi species. However, it's difficult for Alternaria mycotoxins analysis, especially for conjugated metabolites in food safety surveillance. In this work, a novel data-dependent acquisition (DDA) full mass scan and products scan protocol was proposed for qualitative and quantitative analysis of five target mycotoxins in tomato samples using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap). In total, 24 sulfated metabolites were detected with post-data analysis techniques, and two sulfated metabolites (AME-sulfated and AOH-sulfated) were identified in Alternaria fungi -inoculated tomatoes. In addition, a custom database was established, and it was successfully applied for Alternaria mycotoxins and sulfated metabolites screening in tomatoes. With the improvement in high-resolution mass spectrometry (HRMS) as well as post-data analysis techniques, DDA based HRMS method could be widely applied for compound analysis, identification, and screening in quantitative field.

Non-targeted screening of trace organic contaminants in surface waters by a multi-tool approach based on combinatorial analysis of tandem mass spectra and open access databases

Non-targeted screening (NTS) in mass spectrometry (MS) helps alleviate the shortcoming of targeted analysis such as missing the presence of concerning compounds that are not monitored and its lack of retrospective analysis to subsequently look for new contaminants. Most NTS workflows include high resolution tandem mass spectrometry (HRMS²) and structure annotation with libraries which are still limited. However, in silico combinatorial fragmentation tools that simulate MS² spectra are available to help close the gap of missing compounds in empirical libraries. Three NTS tools were combined and used to detect and identify unknown contaminants at ultra-trace levels in surface waters in real samples in this qualitative study. Two of them were based on combinatorial fragmentation databases, MetFrag and the Similar Partition Searching algorithm (SPS), and the third, the Global Natural Products Social Networking (GNPS), was an ensemble of empirical databases. The three NTS tools were applied to the analysis of real samples from a local river. A total of 253 contaminants were identified by combining all three tools: 209 were assigned a probable structure and 44 were confirmed using reference standards. The two major classes of contaminants observed were pharmaceuticals and consumer product additives. Among the confirmed compounds, octylphenol ethoxylates, denatonium, irbesartan and telmisartan are reported for the first time in surface waters in Canada. The workflow presented in this work uses three highly complementary NTS tools and it is a powerful approach to help identify and strategically select contaminants and their transformation products for subsequent targeted analysis and uncover new trends in surface water contamination.

A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing

Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.

Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry

Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.

Comprehensive identification of steroid hormones in human urine based on liquid chromatography-high resolution mass spectrometry

Steroid hormones, structural derivatives of cyclopentanoperhydrophenanthrene, play important roles in modulation of many physiological processes. Comprehensive characterization of steroid hormones is valuable for understanding the process of human life activities and even disease diagnosis. Hitherto systematical characterization of steroid hormones has been rarely investigated. Here, we presented an integrated method for human urine analysis based on ultra-high performance liquid chromatography-high resolution mass spectrometry in data-dependent acquisition mode with the following parallel reaction monitoring mode. To process the data acquired by two scan modes, a comparative study of standards' fragmentation behaviors and diagnostic product ions (DPIs) were firstly conducted to facilitate the characterization of steroid hormones. The fragmentation behaviors, DPIs, elemental composition and double-bond equivalent were then simultaneously utilized for systematical characterization of steroid hormones in human urine. Consequently, fragmentation pathways and DPIs for all types of steroid hormones were comprehensively interpreted. It is interesting to find that dehydration is not restricted in the form of hydroxyl groups loss, elimination of the carbonyl oxygen could also generate dehydrated ions. Ultimately, a total of 80 and 107 steroidal hormones were characterized or tentatively identified in human urine of male and female, respectively. The proposed method is expected to provide valuable insights for chemical characterization in complex matrixes.

Differential protein expression in human knee articular cartilage and medial meniscus using two different proteomic methods: a pilot analysis

Background

Proteomics is an emerging field in the study of joint disease. Our two aims with this pilot analysis were to compare healthy human knee articular cartilage with meniscus, two tissues both known to become affected in the osteoarthritic disease process, and to compare two mass spectrometry (MS)-based methods: data-dependent acquisition (DDA) and data-independent acquisition (DIA).

Methods

Healthy knee articular cartilage taken from the medial tibial condyle and medial meniscus samples taken from the body region were obtained from three adult forensic medicine cases. Proteins were extracted from tissue pieces and prepared for MS analysis. Each sample was subjected to liquid chromatography (LC)-MS/MS analysis using an Orbitrap mass spectrometer, and run in both DDA and DIA mode. Linear mixed effects models were used for statistical analysis.

Results

A total of 653 proteins were identified in the DDA analysis, of which the majority was present in both tissue types. Only proteins with quantitation information in both tissues (n = 90) were selected for more detailed analysis, of which the majority did not statistically significantly differ in abundance between the two tissue types, in either of the MS analyses. However, 21 proteins were statistically significantly different (p < 0.05) between meniscus and cartilage in the DIA analysis. Out of these, 11 proteins were also significantly different in the DDA analysis. Aggrecan core protein was the most abundant protein in articular cartilage and significantly differed between the two tissues in both methods. The corresponding protein in meniscus was serum albumin. Dermatopontin exhibited the highest meniscus vs articular cartilage ratio among the statistically significant proteins. The DIA method led to narrower confidence intervals for the abundance differences between the two tissue types than DDA.

Conclusions

Although articular cartilage and meniscus had similar proteomic composition, we detected several differences by MS. Between the two analyses, DIA yielded more precise estimates and more statistically significant different proteins than DDA, and had no missing values, which makes it preferable for future LC-MS/MS analyses.

Electronic supplementary material

The online version of this article (10.1186/s12891-018-2346-6) contains supplementary material, which is available to authorized users.

Pathway-Informed Discovery and Targeted Proteomic Workflows Using Mass Spectrometry

Recent advancements in mass spectrometry (MS) and data analysis software have enabled new strategies for biological discovery using proteomics. Proteomics has evolved from routine discovery and identification of proteins to integrated multi-omics projects relating specific proteins to their genes and metabolites. Using additional information, such as that contained in biological pathways, has enabled the use of targeted protein quantitation for monitoring fold changes in expression as well as biomarker discovery. Here we discuss a full proteomic workflow from discovery proteomics on a quadrupole Time-of-Flight (Q-TOF) MS to targeted proteomics using a triple quadrupole (QQQ) MS. A discovery proteomics workflow encompassing acquisition of data-dependent proteomics data on a Q-TOF and protein database searching will be described which uses the protein abundances from identified proteins for subsequent statistical analysis and pathway visualization. From the active pathways, a protein target list is created for use in a peptide-based QQQ assay. These peptides are used as surrogates for target protein quantitation. Peptide-based QQQ assays provide sensitivity and selectivity allowing rapid and robust analysis of large batches of samples. These quantitative results are then statistically compared and visualized on the original biological pathways with a more complete coverage of proteins in the studied pathways.

Plant Lipidomics Using UPLC-QTOF-MS

Lipids are primary metabolites used for energy storage, signal transduction, and pigmentation, besides being common membrane components. Considering the importance of comprehensive lipid analysis (lipidomics) for a detailed understanding of cellular metabolic states, this chapter introduces liquid chromatography-quadrupole time-of-flight mass spectrometry as a powerful untargeted plant lipidomics technique.

A high throughput targeted and non-targeted method for the analysis of microcystins and anatoxin-A using on-line solid phase extraction coupled to liquid chromatography-quadrupole time-of-flight high resolution mass spectrometry

Microcystins are cyclic heptapeptide hepatotoxins produced by cyanobacteria in freshwater. Sample preparation for the analysis of these cyanotoxins in water from algal blooms can take up to several days due to the matrix complexity and the low detection limits required to comply with current legislation. Moreover, there is a large number of unknown microcystins that could potentially exist in the environment resulting from different amino acid substitutions into the microcystin skeletal structure. To tackle these problems, the present study involved the development of a high throughput method based on on-line solid phase extraction coupled to liquid chromatography that could provide quantitative results for 12 microcystin variants (LR, YR, RR, HtyR, HilR, WR, LW, LA, LF, LY, Dha⁷-LR, and Dha⁷-RR) and anatoxin-A in less than 3 h with detection limits between 0.004 and 0.01 μg L^-1 and expanded uncertainty between 4 and 14%. Data-dependent acquisition was employed for the non-targeted analysis of these cyanotoxins. Filtering the data based on structure diagnostic fragments, two unknown microcystin variants not previously reported in the literature were detected. The structures Leu¹-microcystin-Met(O)R and Leu¹-microcystin-LY were fully characterized by accurate mass measurement, collision-induced dissociation, and fragmentation prediction software.

GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible. Graphical abstract Software increases the number of glycopeptides that get selected for MS/MS analysis.