Mass Spectrometry Data Analysis Research Articles

Differentially Expressed Proteins in Intra Synovial Compared to Extra Synovial Flexor Tendon Grafts in a Rabbit Tendon Transplantation Model.

Uncomplicated healing of grafts for tendon reconstruction remains an unsolved problem in hand surgery. Results are limited by adhesion formation and decreased strength properties, especially within the tight fibro-osseous sheath of the digits. This is especially problematic when an extra synovial tendon graft is used to replace an intra synovial flexor tendon. Compositional differences are likely to play an important role in these processes. The aim of this study was, therefore, to compare protein expression in pair-matched intra synovial tendon grafts with extra synovial tendon grafts, using a rabbit tendon injury model. We hypothesized that there would be significant differences in proteins critical for response to tensile loading and adhesion formation between the two groups. Using mass spectrometry and multivariate statistical data analysis, we found tissue-specific differences in 22 proteins, where 7 explained 93% (R2) of the variation, with a prediction of 81% (Q2). Among the highest discriminating proteins were Galectin, Histone H2A, and Periostin, which were found in a substantially larger amount in the extra synovial tendons compared to the intra synovial tendons. These findings may contribute to improved understanding of the differences in outcome seen after tendon reconstruction using tendon grafts with intra synovial and extra synovial grafts.

Flavonoids with Inhibitory Effects on NLRP3 Inflammasome Activation from Millettia velutina.

Eight new flavonoids, including two β-hydroxy/methoxychalcones, velutones A and B (1 and 2), two 1,3-diarylpropan-1-ols, velutols C and D (3 and 4), a dihydroxychalcone, velutone E (5), a chalcone, velutone F (6), a furanoflavanone, velutone G (7), and a furanoflavonol, velutone H (8), and 14 known compounds were isolated from Millettia velutina. Their structures were determined by high-resolution electrospray ionisation mass spectrometry (HR-ESIMS) and spectroscopic data analyses and time-dependent density functional theory electronic circular dichroism (TD-DFT-ECD) calculations. Among the isolated constituents, compound 6 exhibited the most potent inhibitory effect (IC50: 1.3 μM) against nigericin-induced IL-1β release in THP-1 cells. The initial mechanism of action study revealed that compound 6 suppressed NLRP3 inflammasome activation via blocking ASC oligomerization without affecting the priming step, which subsequently inhibited caspase-1 activation and IL-1β secretion. Most importantly, compound 6 exerted potent protective effects in the LPS-induced septic shock mice model by improving the survival rate of mice and suppressing serum IL-1β release. These results demonstrated that compound 6 had the potential to be developed as a broad-spectrum NLRP3 inflammasome inhibitor for the treatment of NLRP3-related disease.

Principal Component Analysis from Mass Spectrometry Data Combined to a Sensory Evaluation as a Suitable Method for Assessing Bitterness of Enzymatic Hydrolysates Produced from Micellar Casein Proteins.

Enzymatic hydrolysis of food proteins generally changes the techno-functional, nutritional, and organoleptic properties of hydrolyzed proteins. As a result, protein hydrolysates have an important interest in the food industries. However, they tend to be characterized by a bitter taste and some off-flavors, which limit their use in the food industry. These tastes and aromas come from peptides, amino acids, and volatile compounds generated during hydrolysis. In this article, sixteen more or less bitter enzymatic hydrolysates produced from a milk protein liquid fraction enriched in micellar caseins using commercially available, food-grade proteases were subjected to a sensory analysis using a trained and validated sensory panel combined to a peptidomics approach based on the peptide characterization by reverse-phase high-performance liquid chromatography, high-resolution mass spectrometry, and bioinformatics software. The comparison between the sensory characteristics and the principal components of the principal component analysis (PCA) of mass spectrometry data reveals that peptidomics constitutes a convenient, valuable, fast, and economic intermediate method to evaluating the bitterness of enzymatic hydrolysates, as a trained sensory panel can do it.

Lineage-Specific Proteomic Signatures in the Mycobacterium tuberculosis Complex Reveal Differential Abundance of Proteins Involved in Virulence, DNA Repair, CRISPR-Cas, Bioenergetics and Lipid Metabolism.

Despite the discovery of the tubercle bacillus more than 130 years ago, its physiology and the mechanisms of virulence are still not fully understood. A comprehensive analysis of the proteomes of members of the human-adapted Mycobacterium tuberculosis complex (MTBC) lineages 3, 4, 5, and 7 was conducted to better understand the evolution of virulence and other physiological characteristics. Unique and shared proteomic signatures in these modern, pre-modern and ancient MTBC lineages, as deduced from quantitative bioinformatics analyses of high-resolution mass spectrometry data, were delineated. The main proteomic findings were verified by using immunoblotting. In addition, analysis of multiple genome alignment of members of the same lineages was performed. Label-free peptide quantification of whole cells from MTBC lineages 3, 4, 5, and 7 yielded a total of 38,346 unique peptides derived from 3092 proteins, representing 77% coverage of the predicted proteome. MTBC lineage-specific differential expression was observed for 539 proteins. Lineage 7 exhibited a markedly reduced abundance of proteins involved in DNA repair, type VII ESX-3 and ESX-1 secretion systems, lipid metabolism and inorganic phosphate uptake, and an increased abundance of proteins involved in alternative pathways of the TCA cycle and the CRISPR-Cas system as compared to the other lineages. Lineages 3 and 4 exhibited a higher abundance of proteins involved in virulence, DNA repair, drug resistance and other metabolic pathways. The high throughput analysis of the MTBC proteome by super-resolution mass spectrometry provided an insight into the differential expression of proteins between MTBC lineages 3, 4, 5, and 7 that may explain the slow growth and reduced virulence, metabolic flexibility, and the ability to survive under adverse growth conditions of lineage 7.

Zoanthamine Alkaloid Derivatives from the Zoantharian Zoanthus vietnamensis with Antimetastatic Activity.

A novel compound zoanone A (1), together with eight new alkaloids, 3β,14α-dihydroxy-28-deoxyzoanthenamine (2), 7α-hydroxy-28-deoxyzoanthenamine (3), 3α-hydroxyzoanthenamine (4), 7β-hydroxyzoanthenamine (5), 28α-methoxyzoanthenamine (6), 28α-methoxykuroshine A (7), 30-hydroxykuroshine A (8), and 3β-hydroxy-11-deketo-kuroshine B (9), was isolated from the zoantharian Zoanthus vietnamensis. Their structures were elucidated by the comprehensive analyses of IR, mass spectrometry, NMR, and UV spectroscopic data. The absolute configuration of 1 was established by single-crystal X-ray crystallographic analysis using Cu Kα radiation. A plausible biosynthetic pathway of 1 was proposed. Antiangiogenic and antilymphangiogenic activities of the isolated metabolites were tested and evaluated. The results showed that all isolated compounds had weak antimetastatic activity.

Elucidation of PLK1 Linked Biomarkers in Oesophageal Cancer Cell Lines: A Step Towards Novel Signaling Pathways by p53 and PLK1-Linked Functions Crosstalk.

Oesophgeal adenocarcinoma (OAC) is the most frequent cause of cancer death. POLO-like kinase 1 (PLK1) is overexpressed in broad spectrum of tumors and has prognostic value in many cancers including esophageal cancer, suggesting its potential as a therapeutic target. p53, the guardian of genome is the most important tumor suppressors that represses the promoter of PLK1, whereas tumor cells with inactive p53 are arrested in mitosis due to DNA damage. PLK1 expression has been linked to the elevated p53 expression and has been shown to act as a biomarker that predicts poor prognosis in OAC. The aim of the present study was identification of PLK1 associated phosphorylation targets in p53 mutant and p53 normal cells to explore the downstream signaling evets. Here we develop a proof-of-concept phospho-proteomics approach to identify possible biomarkers that can be used to identify mutant p53 or wild-type p53 pathways. We treated PLK1 asynchronously followed by mass spectrometry data analysis. Protein networking and motif analysis tools were used to identify the significant clusters and potential biomarkers. We investigated approximately 1300 potential PLK1-dependent phosphopeptides by LCMS/ MS. In total, 2216 and 1155 high confidence phosphosites were identified in CP-A (p53+) and OE33 (p53-) cell lines owing to PLK1 inhibition. Further clustering and motif assessment uncovered many significant biomarkers with known and novel link to PLK1. Taken together, our study suggests that PLK1 may serve as a potential therapeutic target in human OAC. The data highlight the efficacy and specificity of small molecule PLK1 kinase inhibitors to identify novel signaling pathways in vivo.

UPLC quantitative fingerprint research on Gardeniae Fructus based on chemical pattern recognition technology

Twenty-six batches of Gardeniae Fructus from different producing area were collected for the development of the fingerprint, and the main components of Gardeniae Fructus were identified by liquid chromatography-mass spectrometry. The producing areas of Gardeniae Fructus were distinguished by chemical pattern recognition technology, and the index components of Gardeniae Fructus were quantitated. An UPLC wavelength switching method was adopted, and the separation was carried out on a Waters Acquity UPLC HASS C_(18)(2.1 mm×100 mm, 1.7 μm) column using the mobile phase of acetonitrile-0.5% formic acid water for gradient elution. Principal component analysis(PCA) and orthogonal partial least square discriminant analysis(OPLS-DA) were used for the data ana-lysis. The results showed that the similarity of 26 batches of Gardeniae Fructus was more than 0.89, and ten common peaks were defined. Sixteen compounds including monoterpenes, iridoids and diterpenoids were identified by reference identification, literature comparison and high-resolution mass spectrometry data analysis. The distinguishment of origin of Gardeniae Fructus was realized by PCA and OPLS-DA analysis, and two quality differential markers were screened as geniposide and crocin Ⅰ. The contents of crocin Ⅰ, crocin Ⅱ and geniposide in Gardeniae Fructus from different places were different. These results will provide reference for the geographical origin traceability of Gardeniae Fructus.

Insight into Unprecedented Diversity of Cyanopeptides in Eutrophic Ponds Using an MS/MS Networking Approach.

Man-made shallow fishponds in the Czech Republic have been facing high eutrophication since the 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physicochemical properties of water and its aquatic community composition, leading to harmful algal bloom formation. In our current study, we characterized the phytoplankton community across three eutrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprising non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data coupled with a dereplication strategy. This MS networking approach, coupled with dereplication, on the online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin, and epidolastatin 12. We applied the binary logistic regression to estimate the CNPs producers by correlating the GNPS data with the species abundance. The usage of the GNPS web platform proved a valuable approach for the rapid and simultaneous detection of a large number of peptides and rapid risk assessments for harmful blooms.

Three New Oleanane-Type Triterpenoidal Glycosides from Impatiens balsamina and Their Biological Activity.

Three new oleanane-type triterpenoidal glycosides, imbalosides A–C (1–3), were isolated from the white flowers of Impatiens balsamina. The structures of these phytochemical constituents (1–3) were elucidated through 1D and 2D Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) data analyses followed by chemical methods. All the characterized compounds (1–3) were evaluated for their antiproliferative activity against four human tumor cell lines (A549, SK-OV-3, SK-MEL-2, and BT549) and their anti-neuroinflammatory activity on the basis of inhibition levels of nitric oxide (NO) in the lipopolysaccharide (LPS)-stimulated murine microglia BV-2 cell lines.

Abstract 6147: Myc-driven osteosarcoma murine model for dissecting the molecular genetics and novel therapeutic strategies for OS

Abstract Osteosarcoma (OS) is the most common bone tumor in pediatric patients, particularly in adolescents and young adults. Unfortunately, over the past four decades, there has been minimal progress towards improving patient outcomes, especially for those with metastatic disease. Currently, no effective molecular targeted therapies are available for the OS, however many OS patients possess genetically defined somatic DNA copy number alterations (SCNAs), including amplification of chromosome 8q24. 8q24.2 region harbors the oncogene c-Myc, and the SCNA is associated with decreased overall survival rates. Furthermore, enhanced c-Myc transcript levels are significantly correlated with worse prognosis. There is a scarcity of reliable and reproducible in vivo animal models that mimic SCNAs and can be used to provide both intrinsic and extrinsic molecular insights into c-Myc-osteosarcoma biology. We have developed a novel conditional c- Myc knock-in mouse model to understand the mechanisms that underlie the ability of c-Myc to drive development and progression of OS and utilize for identification of c-Myc-dependent molecular vulnerabilities. A tissue-specific GEMM for osteosarcoma was developed by altering p53 (f/+) and c-Myc (c- Myc-in) genes in the osteoblast derived cells. Either c-Myc WT or the c-Myc T58A phosphorylation mutant was knocked-in from the constitutively active ROSA26 locus in response to Cre-recombinase. Differential gene expression in c-Myc-driven and non-driven-tumor tissue samples were quantified by RNA sequencing. The protein expression profiles were evaluated for both groups by comprehensive mass spectrometry (MS) analysis and reverse phase proteome analysis (RPPA). Osteosarcoma was confirmed for GEMM tumors by histopathological (H&E) staining. The spontaneous tumor development was significantly accelerated and more aggressive in c-Myc- driven GEMMs as compared to the non-c-Myc-driven. c-Myc expression was significantly higher both at the transcriptional and protein levels in the c-Myc-driven tissue/cells. Expression of 1479 genes (out of 13781 genes) were significantly altered at transcriptional levels in c-Myc driven tumor tissue samples, analyzed by RNA sequencing. Myc-target, E2F, G2M check-point, KRAS and DNA repair associated genes were significantly upregulated whereas the protein secretion, oxidative phosphorylation, adipognenesis, myhogenesis, and E2M transition genes were significantly downregulated when compared with the non-c-Myc-driven group. A significant alteration was observed in the protein expression levels of ribosome biogenesis, DNA replication, nucleotide excision repair, mismatch repair and RNA degradation associated genes in MS data analysis. Our mouse model has high penetrance, short latency, closely resembles human OS and will provide new opportunities for dissecting the molecular genetics of OS and developing novel therapeutic strategies. This model has also provided syngeneic cell lines for future in vivo studies. Citation Format: Bikesh Kumar Nirala, Kurenbekova Lyazat, Tajhal Patel, Ryan Lane Shuck, Rajapakshe Kimal, Jason T. Yustein. Myc-driven osteosarcoma murine model for dissecting the molecular genetics and novel therapeutic strategies for OS [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6147.

Identification of the Cryptic HLA-I Immunopeptidome.

The success of cancer immunotherapy relies on the ability of cytotoxic T cells to specifically recognize and eliminate tumor cells based on peptides presented by HLA-I. Although the peptide epitopes that elicit the corresponding immune response often remain unidentified, it is generally assumed that neoantigens, due to tumor-specific mutations, are the most common targets. Here, we used a mass spectrometric approach to show an underappreciated class of epitopes that accounts for up to 15% of HLA-I peptides for certain HLA alleles in various tumors and patients. These peptides are translated from cryptic open reading frames in supposedly noncoding regions in the genome and are mostly unidentifiable with conventional computational analyses of mass spectrometry (MS) data. Our approach, Peptide-PRISM, identified thousands of such cryptic peptides in tumor immunopeptidomes. About 20% of these HLA-I peptides represented the C-terminus of the corresponding translation product, suggesting frequent proteasome-independent processing. Our data also revealed HLA-I allele-dependent presentation of cryptic peptides, with HLA-A*03 and HLA-A*11 presenting the highest percentage of cryptic peptides. Our analyses refute the reported frequent presentation of HLA peptides generated by proteasome-catalyzed peptide splicing. Thus, Peptide-PRISM represents an important step toward comprehensive identification of HLA-I immunopeptidomes and reveals cryptic peptides as an abundant class of epitopes with potential relevance for novel immunotherapeutic approaches.

A quantitative thiol reactivity profiling platform to analyze redox and electrophile reactive cysteine proteomes.

Cysteine is unique among all protein-coding amino acids, owing to its intrinsically high nucleophilicity. The cysteinyl thiol group can be covalently modified by a broad range of redox mechanisms or by various electrophiles derived from exogenous or endogenous sources. Measuring the response of protein cysteines to redox perturbation or electrophiles is critical for understanding the underlying mechanisms involved. Activity-based protein profiling based on thiol-reactive probes has been the method of choice for such analyses. We therefore adapted this approach and developed a new chemoproteomic platform, termed 'QTRP' (quantitative thiol reactivity profiling), that relies on the ability of a commercially available thiol-reactive probe IPM (2-iodo-N-(prop-2-yn-1-yl)acetamide) to covalently label, enrich and quantify the reactive cysteinome in cells and tissues. Here, we provide a detailed and updated workflow of QTRP that includes procedures for (i) labeling of the reactive cysteinome from cell or tissue samples (e.g., control versus treatment) with IPM, (ii) processing the protein samples into tryptic peptides and tagging the probe-modified peptides with isotopically labeled azido-biotin reagents containing a photo-cleavable linker via click chemistry reaction, (iii) capturing biotin-conjugated peptides with streptavidin beads, (iv) identifying and quantifying the photo-released peptides by mass spectrometry (MS)-based shotgun proteomics and (v) interpreting MS data by a streamlined informatic pipeline using a proteomics software, pFind 3, and an automatic post-processing algorithm. We also exemplified here how to use QTRP for mining H2O2-sensitive cysteines and for determining the intrinsic reactivity of cysteines in a complex proteome. We anticipate that this protocol should find broad applications in redox biology, chemical biology and the pharmaceutical industry. The protocol for sample preparation takes 3 d, whereas MS measurements and data analyses require 75 min and <30 min, respectively, per sample.

Mapping Coeliac Toxic Motifs in the Prolamin Seed Storage Proteins of Barley, Rye, and Oats Using a Curated Sequence Database.

Wheat gluten, and related prolamin proteins in rye, barley and oats cause the immune-mediated gluten intolerance syndrome, coeliac disease. Foods labelled as gluten-free which can be safely consumed by coeliac patients, must not contain gluten above a level of 20 mg/Kg. Current immunoassay methods for detection of gluten can give conflicting results and may underestimate levels of gluten in foods. Mass spectrometry methods have great potential as an orthogonal method, but require curated protein sequence databases to support method development. The GluPro database has been updated to include avenin-like sequences from bread wheat (n = 685; GluPro v1.1) and genes from the sequenced wheat genome (n = 699; GluPro v 1.2) and Triticum turgidum ssp durum (n = 210; GluPro v 2.1). Companion databases have been developed for prolamin sequences from barley (n = 64; GluPro v 3.0), rye (n = 41; GluPro v 4.0), and oats (n = 27; GluPro v 5.0) and combined to provide a complete cereal prolamin database, GluPro v 6.1 comprising 1,041 sequences. Analysis of the coeliac toxic motifs in the curated sequences showed that they were absent from the minor avenin-like proteins in bread and durum wheat and barley, unlike the related avenin proteins from oats. A comparison of prolamin proteins from the different cereal species also showed α- and γ-gliadins in bread and durum wheat, and the sulphur poor prolamins in all cereals had the highest density of coeliac toxic motifs. Analysis of ion-mobility mass spectrometry data for bread wheat (cvs Chinese Spring and Hereward) showed an increased number of identifications when using the GluPro v1.0, 1.1 and 1.2 databases compared to the limited number of verified sequences bread wheat sequences in reviewed UniProt. This family of databases will provide a basis for proteomic profiling of gluten proteins from all the gluten containing cereals and support identification of specific peptide markers for use in development of new methods for gluten quantitation based on coeliac toxic motifs found in all relevant cereal species.

Lipidomics profiling of skin surface lipids in senile pruritus

BackgroundSenile pruritus is common, yet its etiology remains unknown. Aging-associated skin barrier defects and skin surface lipid (SSL) alterations have been postulated to play important roles in its occurrence. In the present study, the lipidomic profiles of SSLs in elderly patients were examined to better understand the potential causes of senile pruritus.MethodsTransepidermal water loss (TEWL) was evaluated to assess the skin barrier function. The Ameliorated Kawashima Itch Scale score was used to measure the pruritus severity. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and multivariate data analysis were employed to investigate SSL alterations.ResultsThe results showed that senile pruritus patients had higher TEWL values than control subjects (13.13 ± 4.28 versus 6.71 ± 2.45, p < 0.01). LC-MS/MS revealed significant differences in the lipidomic profiles and identified 81 species of SSLs that differed between the two groups. Compared with control subjects, senile pruritus patients had increased levels of ceramides (Cers), diacylglycerols, fatty acids, phosphatidylcholines, phosphatidylethanolamines, phytosphingosines, sphingosines, diacylceryl-3-O-carboxyhydroxymethylcholine, diacylglyceryl trimethylhomoserine, and unsaturated free fatty acids, but decreased levels of triacylglycerol. Cer-EOS, Cer-NDS, and Cer-NS were positively correlated with TEWL value (p < 0.05). Pruritus severity score was positively correlated with sphingomyelin, Cer-NP, Cer-AS, Cer-NDS, and Cer-NS, but negatively correlated with Cer-BS, Cer-EODS, Cer-EOS, and Cer-AP.ConclusionsThe present study indicated that patients with senile pruritus have impaired skin barrier function and altered SSL composition. Certain SSL species identified in this study may be potential targets for future studies on the pathogenesis of senile pruritus.Trial registrationPeking University International Hospital (Number: YN2018QN04; date: January 2019).

Software Requirements for the Analysis and Interpretation of Native Ion Mobility Mass Spectrometry Data.

The past few years have seen a dramatic increase in applications of native mass and ion mobility spectrometry, especially for the study of proteins and protein complexes. This increase has been catalyzed by the availability of commercial instrumentation capable of carrying out such analyses. As in most fields, however, the software to process the data generated from new instrumentation lags behind. Recently, a number of research groups have started addressing this by developing software, but further improvements are still required in order to realize the full potential of the data sets generated. In this perspective, we describe practical aspects as well as challenges in processing native mass spectrometry (MS) and ion mobility-MS data sets and provide a brief overview of currently available tools. We then set out our vision of future developments that would bring the community together and lead to the development of a common platform to expedite future computational developments, provide standardized processing approaches, and serve as a location for the deposition of data for this emerging field. This perspective has been written by members of the European Cooperation in Science and Technology Action on Native MS and Related Methods for Structural Biology (EU COST Action BM1403) as an introduction to the software tools available in this area. It is intended to serve as an overview for newcomers and to stimulate discussions in the community on further developments in this field, rather than being an in-depth review. Our complementary perspective (http://dx.doi.org/10.1021/acs.analchem.9b05791) focuses on computational approaches used in this field.

A new acyclic peroxide from Aspergillus nidulans SD-531, a fungus obtained from deep-sea sediment of cold spring in the South China Sea

A new acyclic peroxide derivative asperoxide A (1), along with 13 known compounds, namely, microperfuranone (2), 9-hydroxymicroperfuranone (3), gibellulin A (4), lecanoric acid (5), terrequinone A (6), sterigmatocystin (7), isosecosterigmatocystin (8), arugosin C (9), curvularin (10), 3,3′-diindolylmethane (11), austinol (12), austin (13), and dehydroaustin (14), were isolated and identified from the culture extract of Aspergillus nidulans SD-531, a fungus obtained from the deep-sea sediment of cold spring in the South China Sea. Their structures were determined based on detailed interpretation of nuclear magnetic resonance (NMR) spectroscopic and mass spectrometry data analysis. All the isolated compounds were evaluated for antimicrobial activities against human and aquatic bacteria as well as plant pathogenic fungi. Compounds 1-28, 10, and 11 exhibited antimicrobial activities against some of the tested strains with minimum inhibitory concentration (MIC) values ranging from 2 to 64 μg/mL. Compounds 4 and 6 displayed strongest activities among the tested samples and might be used as promising molecules for the development of natural antimicrobial agents.

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry.

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the “gold” standard for tissue classification. “All peaks” and “significantly different peaks” datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Natural pyrrolysine-biased translation of stop codons in mitochondrial peptides entirely coded by expanded codons

During the noncanonical deletion transcription, k nucleotides are systematically skipped/deleted after each transcribed trinucleotide producing deletion-RNAs (delRNAs). Peptides matching delRNAs either result from (a) canonical translation of delRNAs; or (b) noncanonical translation of regular transcripts along expanded codons. Only along frame "0" (start site) (a) and (b) produce identical peptides. Here, mitochondrial mass spectrometry data analyses assume expanded codon/del-transcription with 3 + k (k from 0 to 12) nucleotides. Detected peptides map preferentially on previously identified delRNAs. More peptides were detected for k (1–12) when del-transcriptional and expanded codon translations start sites coincide (i.e. the 0th frame) than for frames +1 or +2. Hence, both (a) and (b) produced peptides identified here. Biases for frame 0 decrease for k > 2, reflecting codon/anticodon expansion limits. Further analyses find preferential pyrrolysine insertion at stop codons, suggesting Pyl-specific mitochondrial suppressor tRNAs loaded by Pyl-specific tRNA synthetases with unknown origins. Pyl biases at stops are stronger for regular than expanded codons suggesting that Pyl-tRNAs are less competitive with near-cognate tRNAs in expanded codon contexts. Statistical biases for these findings exclude that detected peptides are experimental and/or bioinformatic artefacts implying both del-transcription and expanded codons translation occur in human mitochondria.

Comparison of dimension reduction techniques in the analysis of mass spectrometry data

Abstract. Online analysis with mass spectrometers produces complex data sets, consisting of mass spectra with a large number of chemical compounds (ions). Statistical dimension reduction techniques (SDRTs) are able to condense complex data sets into a more compact form while preserving the information included in the original observations. The general principle of these techniques is to investigate the underlying dependencies of the measured variables by combining variables with similar characteristics into distinct groups, called factors or components. Currently, positive matrix factorization (PMF) is the most commonly exploited SDRT across a range of atmospheric studies, in particular for source apportionment. In this study, we used five different SDRTs in analysing mass spectral data from complex gas- and particle-phase measurements during a laboratory experiment investigating the interactions of gasoline car exhaust and α-pinene. Specifically, we used four factor analysis techniques, namely principal component analysis (PCA), PMF, exploratory factor analysis (EFA) and non-negative matrix factorization (NMF), as well as one clustering technique, partitioning around medoids (PAM). All SDRTs were able to resolve four to five factors from the gas-phase measurements, including an α-pinene precursor factor, two to three oxidation product factors, and a background or car exhaust precursor factor. NMF and PMF provided an additional oxidation product factor, which was not found by other SDRTs. The results from EFA and PCA were similar after applying oblique rotations. For the particle-phase measurements, four factors were discovered with NMF: one primary factor, a mixed-LVOOA factor and two α-pinene secondary-organic-aerosol-derived (SOA-derived) factors. PMF was able to separate two factors: semi-volatile oxygenated organic aerosol (SVOOA) and low-volatility oxygenated organic aerosol (LVOOA). PAM was not able to resolve interpretable clusters due to general limitations of clustering methods, as the high degree of fragmentation taking place in the aerosol mass spectrometer (AMS) causes different compounds formed at different stages in the experiment to be detected at the same variable. However, when preliminary analysis is needed, or isomers and mixed sources are not expected, cluster analysis may be a useful tool, as the results are simpler and thus easier to interpret. In the factor analysis techniques, any single ion generally contributes to multiple factors, although EFA and PCA try to minimize this spread. Our analysis shows that different SDRTs put emphasis on different parts of the data, and with only one technique, some interesting data properties may still stay undiscovered. Thus, validation of the acquired results, either by comparing between different SDRTs or applying one technique multiple times (e.g. by resampling the data or giving different starting values for iterative algorithms), is important, as it may protect the user from dismissing unexpected results as “unphysical”.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS.

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.