Deep Learning Powers Protein Identification from Precursor MS Information.

Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundance dynamic range samples. Therefore, leveraging MS1 features as a complement to MS/MS has become an attractive option to improve the coverage of feature identification. Herein, we propose MonoMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and logistic regression-based scoring for MS1 feature identification. The approach achieved a significant increase in MS1 feature identification based on an E. coli data set. Application of MonoMS1 to data sets with wide dynamic range, such as human serum proteome samples, and with low sample abundance, such as single-cell proteome samples, enabled substantial complementation of MS/MS-based peptide and protein identification. This method opens a new avenue for proteomic analysis and can boost proteomic research on complex samples.

Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification.

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the "in-cell digest." We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with "waves" of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle-regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.

Highly Reproducible Quantitative Proteomics Analysis of Pancreatic Cancer Cells Reveals Proteome-Level Effects of a Novel Combination Drug Therapy That Induces Cancer Cell Death via Metabolic Remodeling and Activation of the Extrinsic Apoptosis Pathway.

Pancreatic cancer patients have poor survival rates and are frequently treated using gemcitabine (Gem). However, initial tumor sensitivity often gives way to rapid development of resistance. Gem-based drug combinations are employed to increase efficacy and mitigate resistance, but our understanding of molecular-level drug interactions, which could assist in the development of more effective therapeutic regimens, is limited. Global quantitative proteomic analysis could provide novel mechanistic insights into drug combination interactions, but it is challenging to achieve high-quality quantitative proteomics analysis of the large sample sets that are typically required for drug combination studies. Here, we investigated molecular-level temporal interactions of Gem with BGJ398 (infigratinib), a recently approved pan-FGFR inhibitor, in multiple treatment groups (N = 42 samples) using IonStar, a robust large-scale proteomics method that employs well-controlled, ultrahigh-resolution MS1 quantification. A total of 5514 proteins in the sample set were quantified without missing data, requiring >2 unique peptides/protein, <1% protein false discovery rate (FDR), <0.1% peptide FDR, and CV < 10%. Functional analysis of the differentially altered proteins revealed drug-dysregulated processes such as metabolism, apoptosis, and antigen presentation pathways. These changes were validated experimentally using Seahorse metabolic assays and immunoassays. Overall, in-depth analysis of large-scale proteomics data provided novel insights into possible mechanisms by which FGFR inhibitors complement and enhance Gem activity in pancreatic cancers.

Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach.

Current proteomics approaches rely almost exclusively on using the positive ionization mode, resulting in inefficient ionization of many acidic peptides. This study investigates protein identification efficiency in the negative ionization mode using the DirectMS1 method. DirectMS1 is an ultrafast data acquisition method based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identification in the negative ion mode to date, identifying over 1000 proteins in a human cell line at a 1% false discovery rate. This is accomplished using a single-shot 10 min separation gradient, comparable to lengthy MS/MS-based analyses. Optimizing separation and experimental conditions was achieved by utilizing mobile buffers containing 2.5 mM imidazole and 3% isopropanol. The study emphasized the complementary nature of data obtained in positive and negative ion modes. Combining the results from all replicates in both polarities increased the number of identified proteins to 1774. Additionally, we analyzed the method's efficiency using different proteases for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), trypsin and LysC demonstrated the highest protein identification yield. This suggests that digestion procedures utilized in positive-mode proteomics can be effectively applied in the negative ion mode. Data are deposited to ProteomeXchange: PXD040583.

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

[Rapid chemome characterization of Pien-Tze-Huang by DI-MS/MS~(ALL)].

Pien-Tze-Huang, one of the most famous and widely used Chinese medicinal prescriptions in China, consists of Notoginseng Radix et Rhizoma, Bovis Calculus, Fel Serpentis, and Moschus.The prescription can clear heat and remove toxin, cool blood and resolve blood stasis, and relieve swelling and pain.Characterizing the chemical composition can facilitate the construction of the quality standard and the research on the effective compounds and action mechanism of Pien-Tze-Huang.Therefore, this study used direct infusion(DI)-MS/MS~(ALL) method to rapidly and accurately reveal the chemical composition of Pien-Tze-Huang.The principle of chemical composition profiling of Chinese medicinal prescriptions lies in the MS~1-MS~2 dataset construction, followed by structural annotation based on MS/MS spectra and summarizing of mass fragmentation pathways.MS/MS~(ALL) owns unique mass spectrometric separation ability via applying gas phase fractionation which enables MS~1 ion cohort successively enter the collision cell and acquire MS~2 spectrum for each precursor ion current with a width of m/z=1.Because DI can provide desired measurement time, MS/MS~(ALL) is able to acquire MS~2 spectrum for each compound individually except for the compounds which share identical nominal molecular weight, even isomers.A total of 52 compounds were identified in Pien-Tze-Huang, including 16 saponins, 24 bile acids, 9 fatty acids, 2 saccharides, and 1 other compound.DI-MS/MS~(ALL) can simultaneously identify the compounds with different polarities in a short time, which is superior to LC-MS.This study provides a powerful tool for the rapid chemome profiling of Chinese medicinal prescriptions.

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

A web-based system for creating, viewing, and editing precursor mass spectrometry ground truth data.

BACKGROUND: Mass spectrometry (MS) uses mass-to-charge ratios of measured particles to decode the identities and quantities of molecules in a sample. Interpretation of raw MS depends upon data processing algorithms that render it human-interpretable. Quantitative MS workflows are complex experimental chains and it is crucial to know the performance and bias of each data processing method as they impact accuracy, coverage, and statistical significance of the result. Creation of the ground truth necessary for quantitatively evaluating MS1-aware algorithms is difficult and tedious task, and better software for creating such datasets would facilitate more extensive evaluation and improvement of MS data processing algorithms. RESULTS: We present JS-MS 2.0, a software suite that provides a dependency-free, browser-based, one click, cross-platform solution for creating MS1 ground truth. The software retains the first version's capacity for loading, viewing, and navigating MS1 data in 2- and 3-D, and adds tools for capturing, editing, saving, and viewing isotopic envelope and extracted isotopic chromatogram features. The software can also be used to view and explore the results of feature finding algorithms. CONCLUSIONS: JS-MS 2.0 enables faster creation and inspection of MS1 ground truth data. It is publicly available with an MIT license at github.com/optimusmoose/jsms.

Oxidative Catabolism of (+)-Pinoresinol Is Initiated by an Unusual Flavocytochrome Encoded by Translationally Coupled Genes within a Cluster of (+)-Pinoresinol-Coinduced Genes in Pseudomonas sp. Strain SG-MS2.

Burkholderia sp. strain SG-MS1 and Pseudomonas sp. strain SG-MS2 have previously been found to mineralize (+)-pinoresinol through a common catabolic pathway. Here, we used comparative genomics, proteomics, protein semipurification, and heterologous expression to identify a flavoprotein from the vanillyl alcohol oxidase/p-cresol methyl hydroxylase (VAO/PCMH) enzyme family in SG-MS2 that carries out the initial hydroxylation of (+)-pinoresinol at the benzylic carbon. The cognate gene is translationally coupled with a downstream cytochrome gene, and the cytochrome is required for activity. The flavoprotein has a unique combination of cofactor binding and cytochrome requirements for the VAO/PCMH family. The heterologously expressed enzyme has a Km of 1.17 µM for (+)-pinoresinol. The enzyme is overexpressed in strain SG-MS2 upon exposure to (+)-pinoresinol, along with 45 other proteins, 22 of which were found to be encoded by genes in an approximately 35.1-kb cluster also containing the flavoprotein and cytochrome genes. Homologs of 18 of these 22 genes, plus the flavoprotein and cytochrome genes, were also found in a 38.7-kb cluster in SG-MS1. The amino acid identities of four of the other proteins within the SG-MS2 cluster suggest they catalyze conversion of hydroxylated pinoresinol to protocatechuate and 2-methoxyhydroquinone. Nine other proteins upregulated in SG-MS2 on exposure to (+)-pinoresinol appear to be homologs of proteins known to comprise the protocatechuate and 2-methoxyhydroquinone catabolic pathways, but only three of the cognate genes lie within the cluster containing the flavoprotein and cytochrome genes.IMPORTANCE (+)-Pinoresinol is an important plant defense compound, a major food lignan for humans and some other animals, and the model compound used to study degradation of the ß-ß' linkages in lignin. We report a gene cluster, in one strain each of Pseudomonas and Burkholderia, that is involved in the oxidative catabolism of (+)-pinoresinol. The flavoprotein component of the α-hydroxylase which heads the pathway belongs to the 4-phenol oxidizing (4PO) subgroup of the vanillyl alcohol oxidase/p-cresol methyl hydroxylase (VAO/PCMH) enzyme family but constitutes a novel combination of cofactor and electron acceptor properties for the family. It is translationally coupled with a cytochrome gene whose product is also required for activity. The work casts new light on the biology of (+)-pinoresinol and its transformation to other bioactive molecules. Potential applications of the findings include new options for deconstructing lignin into useful chemicals and the generation of new phytoestrogenic enterolactones from lignans.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

MS1, a direct target of MS188, regulates the expression of key sporophytic pollen coat protein genes in Arabidopsis.

Sporophytic pollen coat proteins (sPCPs) derived from the anther tapetum are deposited into pollen wall cavities and function in pollen-stigma interactions, pollen hydration, and environmental protection. In Arabidopsis, 13 highly abundant proteins have been identified in pollen coat, including seven major glycine-rich proteins GRP14, 16, 17, 18, 19, 20, and GRP-oleosin; two caleosin-related family proteins (AT1G23240 and AT1G23250); three lipase proteins EXL4, EXL5 and EXL6, and ATA27/BGLU20. Here, we show that GRP14, 17, 18, 19, and EXL4 and EXL6 fused with green fluorescent protein (GFP) are translated in the tapetum and then accumulate in the anther locule following tapetum degeneration. The expression of these sPCPs is dependent on two essential tapetum transcription factors, MALE STERILE188 (MS188) and MALE STERILITY 1 (MS1). The majority of sPCP genes are up-regulated within 30 h after MS1 induction and could be restored by MS1 expression driven by the MS188 promoter in ms188, indicating that MS1 is sufficient to activate their expression; however, additional MS1 downstream factors appear to be required for high-level sPCP expression. Our ChIP, in vivo transactivation assay, and EMSA data indicate that MS188 directly activates MS1. Together, these results reveal a regulatory cascade whereby outer pollen wall formation is regulated by MS188 followed by synthesis of sPCPs controlled by MS1.

Increased expression of the MALE STERILITY1 transcription factor gene results in temperature-sensitive male sterility in barley.

Understanding the control of fertility is critical for crop yield and breeding; this is particularly important for hybrid breeding to capitalize upon the resultant hybrid vigour. Different hybrid breeding systems have been adopted; however, these are challenging and crop specific. Mutants with environmentally reversible fertility offer valuable opportunities for hybrid breeding. The barley HvMS1 gene encodes a PHD-finger transcription factor that is expressed in the anther tapetum, which is essential for pollen development and causes complete male sterility when overexpressed in barley. This male sterility is due at least in part to indehiscent anthers resulting from incomplete tapetum degeneration, failure of anther opening, and sticky pollen under normal growth conditions (15 °C). However, dehiscence and fertility are restored when plants are grown at temperatures >20 °C, or when transferred to >20 °C during flowering prior to pollen mitosis I, with transfer at later stages unable to rescue fertility in vivo. As far as we are aware, this is the first report of thermosensitive male sterility in barley. This offers opportunities to understand the impact of temperature on pollen development and potential applications for environmentally switchable hybrid breeding systems; it also provides a 'female' male-sterile breeding tool that does not need emasculation to facilitate backcrossing.

Integration of mass spectral fingerprinting analysis with precursor ion (MS1) quantification for the characterisation of botanical extracts: application to extracts of Centella asiatica (L.) Urban.

INTRODUCTION: The phytochemical composition of plant material governs the bioactivity and potential health benefits as well as the outcomes and reproducibility of laboratory studies and clinical trials. OBJECTIVE: The objective of this work was to develop an efficient method for the in-depth characterisation of plant extracts and quantification of marker compounds that can be potentially used for subsequent product integrity studies. Centella asiatica (L.) Urb., an Ayurvedic herb with potential applications in enhancing mental health and cognitive function, was used as a case study. METHODS: A quadrupole time-of-flight analyser in conjunction with an optimised high-performance liquid chromatography (HPLC) separation was used for in-depth untargeted fingerprinting and post-acquisition precursor ion quantification to determine levels of distinct phytochemicals in various C. asiatica extracts. RESULTS: We demonstrate the utility of this workflow for the characterisation of extracts of C. asiatica. This integrated workflow allowed the identification or tentative identification of 117 compounds, chemically interconnected based on Tanimoto chemical similarity, and the accurate quantification of 24 phytochemicals commonly found in C. asiatica extracts. CONCLUSION: We report a phytochemical analysis method combining liquid chromatography, high resolution mass spectral data acquisition, and post-acquisition interrogation that allows chemical fingerprints of botanicals to be obtained in conjunction with accurate quantification of distinct phytochemicals. The variability in the composition of specialised metabolites across different C. asiatica accessions was substantial, demonstrating that detailed characterisation of plant extracts is a prerequisite for reproducible use in laboratory studies, clinical trials and safe consumption. The methodological approach is generally applicable to other botanical products.

Incorporating In-Source Fragment Information Improves Metabolite Identification Accuracy in Untargeted LC-MS Data Sets.

In-source fragmentation occurs as a byproduct of electrospray ionization. We find that ions produced as a result of in-source fragmentation often match fragment ions produced during MS/MS fragmentation, and we take advantage of this phenomenon in a novel algorithm to analyze LC-MS metabolomics data sets. Our approach organizes coeluting MS1 features into a single peak group and then identifies in-source fragments among coeluting features using MS/MS spectral libraries. We tested our approach using previously published data of verified metabolites and compared the results to features detected by other mainstream metabolomics tools. Our results indicate that considering in-source fragment information as a part of the identification process increases the annotation quality, allowing us to leverage MS/MS data in spectrum libraries even if MS/MS scans were not collected.

mineXpert: Biological Mass Spectrometry Data Visualization and Mining with Full JavaScript Ability.

Biological mass spectrometry mainly comprises three fields of endeavor, namely, proteomics, metabolomics, and structural biology. In each of these specialties, the mass spectrometrist needs to access MS1 mass spectral data, although not necessarily on the same basis. For example, the bottom-up proteomics scientist will occasionally access MS1 data to perform data inspection, quality assessments, and quantitation measurements, whereas top-down proteomics, structural biology, or metabolomics scientists will actually spend most of their time mining profile-mode MS1 data. Furthermore, the advent of ion mobility-mass spectrometry imposes new manners of mass spectral data visualization. An open-source MS1-only mass data visualization software for the desktop was developed to allow scientists to visualize conventional and drift time mass data. Various mass data integrations are possible, allowing a thorough mass spectral data scrutiny. Isotopic cluster calculations are easily carried over from the chemical formula up to the display of the mass spectrum. Deconvolution of mass peaks can be achieved with a simple mouse drag. Flexible reporting of data inspection events and of mining discoveries is provided. Very large sparse data sets can be sliced into smaller chunks replicating the original data without data loss. Task automation is achieved in a JavaScript environment. This project allows users of mass spectrometry facilities to inspect and mine their MS1 mass data outside of these facilities without having to resort to the closed-source vendor software shipped with the instruments. mineXpert requires no proprietary software whatsoever once the mass spectrometry data have been converted to mzML. The reference implementation is version 5.8.2 or greater. Reference material, a detailed user manual, and video tutorials are available at http://www.msxpertsuite.org .

Update on the moFF Algorithm for Label-Free Quantitative Proteomics.

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF.

CRISPR/Cas9-mediated knockout of Ms1 enables the rapid generation of male-sterile hexaploid wheat lines for use in hybrid seed production.

The development and adoption of hybrid seed technology have led to dramatic increases in agricultural productivity. However, it has been a challenge to develop a commercially viable platform for the production of hybrid wheat (Triticum aestivum) seed due to wheat's strong inbreeding habit. Recently, a novel platform for commercial hybrid seed production was described. This hybridization platform utilizes nuclear male sterility to force outcrossing and has been applied to maize and rice. With the recent molecular identification of the wheat male fertility gene Ms1, it is now possible to extend the use of this novel hybridization platform to wheat. In this report, we used the CRISPR/Cas9 system to generate heritable, targeted mutations in Ms1. The introduction of biallelic frameshift mutations into Ms1 resulted in complete male sterility in wheat cultivars Fielder and Gladius, and several of the selected male-sterile lines were potentially non-transgenic. Our study demonstrates the utility of the CRISPR/Cas9 system for the rapid generation of male sterility in commercial wheat cultivars. This represents an important step towards capturing heterosis to improve wheat yields, through the production and use of hybrid seed on an industrial scale.

In-depth analysis of crocetin ester glycosides from dried/processed stigmas of Crocus sativus L. by HPLC-ESI-MSn (n = 2, 3).

INTRODUCTION: Saffron stigmas from Crocus sativus L. (Iridaceae) are used as a drug in folk medicine, as a food additive and as a dying agent for at least 3500 years. Despite this long-term use the chemical composition of saffron seems still to be not fully known. OBJECTIVE: An analytical strategy for detailed investigations of aqueous saffron extract is developed based on reverse-phase high-performance liquid chromatography electrospray ionisation (HPLC-ESI) multistage mass spectrometry (MSn ) for crocins. METHODS: Commercially available stigmas are analysed by reverse-phase HPLC in combination with ESI/three-dimensional (3D)-ion trap mass spectrometry (MS) and MSn (n = 2 and 3). Sodium chloride is added to the analyte solution ready for injection to promote abundant [M + Na]+ adduct ions of crocins, being ideal precursor ions for low-energy collision-induced dissociation (CID)-MS2/3 . RESULTS: This strategy allows the detailed structural elucidation of known as well as previously unknown crocin derivatives (molecular mass of the aglycon, oligosaccharide chain length and linkage determination). The two isomeric trisaccharide substituents neapolitanose and gentiotriose are distinguished based on linkage-specific cross-ring cleavage for the first time. Furthermore, crocins containing up to six hexose units are also observed. Five novel crocin ester glycosides shifted by a mass difference of -40 Da indicate the presence of the here newly described C17 -aglycon, termed norcrocetin (crocetin = C20 ). CONCLUSIONS: These findings indicate the action of at least two different carotenoid cleavage dioxygenases (CCD2 and tentatively CCD4) during biosynthesis of this new bis-apocarotenoid aglycon (norcrocetin) and the existence of even higher glycosylated crocin derivatives at trace level.

Human EndoC-ßH1 ß-cells form pseudoislets with improved glucose sensitivity and enhanced GLP-1 signaling in the presence of islet-derived endothelial cells.

Three-dimensional (3D) pseudoislets (PIs) can be used for the study of insulin-producing ß-cells in free-floating islet-like structures similar to that of primary islets. Previously, we demonstrated the ability of islet-derived endothelial cells (iECs) to induce PIs using murine insulinomas, where PI formation enhanced insulin production and glucose responsiveness. In this report, we examined the ability of iECs to spontaneously induce the formation of free-floating 3D PIs using the EndoC-ßH1 human ß-cell line murine MS1 iEC. Within 14 days, the coculturing of both cell types produced fully humanized EndoC-ßH1 PIs with little to no contaminating murine iECs. The size and shape of these PIs were similar to primary human islets. iEC-induced PIs demonstrated reduced dysregulated insulin release under low glucose levels and higher insulin secretion in response to high glucose and exendin-4 [a glucagon-like peptide-1 (GLP-1) analog] compared with monolayer cells cultured alone. Interestingly, iEC-PIs were also better at glucose sensing in the presence of extendin-4 compared with PIs generated on a low-adhesion surface plate in the absence of iECs and showed an overall improvement in cell viability. iEC-induced PIs exhibited increased expression of key genes involved in glucose transport, glucose sensing, ß-cell differentiation, and insulin processing, with a concomitant decrease in glucagon mRNA expression. The enhanced responsiveness to exendin-4 was associated with increased protein expression of GLP-1 receptor and phosphokinase A. This rapid coculture system provides an unlimited number of human PIs with improved insulin secretion and GLP-1 responsiveness for the study of ß-cell biology.

Assessing the reproducibility of an O-glycopeptide enrichment method with a novel software, Pinnacle.

A novel software, Pinnacle was used to reassess the reproducibility of a 2-step lectin-based O-glycopeptide enrichment method. A publicly available dataset consisting of 12 data files representing 3 technical replicates of enriched glycopeptides from human serum was investigated. Previously, an attempt for reproducibility assessment was made utilizing an MS/MS scan (MS2)-based method. However, the stochastic nature of precursor ion selection strongly biased this approach leading to underestimated rate of reproducibility. To bypass this problem, our present method follows the general path to confidently identify O-glycopeptides (database search with MS/MS data) supplemented with full scan/survey scan (MS1)/extracted ion chromatogram (XIC) mining in all files using two software packages, Pinnacle and Skyline. Confident MS/MS identifications were delivered by Protein Prospector. With this input Skyline indicated a 70% reproducibility for our workflow. However, Pinnacle performed better, indicating the presence of 90% of the confidently assigned glycopeptides in all the three replicates. Pinnacle, just like Skyline, performs ion extraction using the high accuracy, high resolution mass measurement data but it also utilizes all the available MS/MS spectra, even from different activation methods, within the same file to make mass spectrometric data evaluation for glycopeptides more reliable.