MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

Background  

Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.     

Processing methods for differential analysis of LC/MS profile data

Background  

Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.     

Proteomic analysis of the fish pathogen <Emphasis Type="Italic">Flavobacterium columnare</Emphasis>

Background  

Flavobacterium columnare causes columnaris disease in cultured and wild fish populations worldwide. Columnaris is the second most prevalent bacterial disease of commercial channel catfish industry in the United States. Despite its economic importance, little is known about the expressed proteins and virulence mechanisms of F. columnare. Here, we report the first high throughput proteomic analysis of F. columnare using 2-D LC ESI MS/MS and 2-DE MALDI TOF/TOF MS.     

An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters

Background  

Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.     

Proteome of the phytopathogen <Emphasis Type="Italic">Xanthomonas citri</Emphasis> subsp. <Emphasis Type="Italic">citri</Emphasis>: a global expression profile

Background  

Citrus canker is a disease caused by Xantomonas citri subsp.citri (Xac), and has emerged as one of the major threats to the worldwide citrus crop because it affects all commercial citrus varieties, decreases the production and quality of the fruits and can spread rapidly in citrus growing areas. In this work, the first proteome of Xac was analyzed using two methodologies, two-dimensional liquid chromatography (2D LC) and tandem mass spectrometry (MS/MS).     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.     

Rapid and Precise Measurement of Serum Branched-Chain and Aromatic Amino Acids by Isotope Dilution Liquid Chromatography Tandem Mass Spectrometry

Background

Serum branched-chain and aromatic amino acids (BCAAs and AAAs) have emerged as predictors for the future development of diabetes and may aid in diabetes risk assessment. However, the current methods for the analysis of such amino acids in biological samples are time consuming.

Methods

An isotope dilution liquid chromatography tandem mass spectrometry (ID-LC/MS/MS) method for serum BCAAs and AAAs was developed. The serum was mixed with isotope-labeled BCAA and AAA internal standards and the amino acids were extracted with acetonitrile, followed by analysis using LC/MS/MS. The LC separation was performed on a reversed-phase C₁₈ column, and the MS/MS detection was performed via the positive electronic spray ionization in multiple reaction monitoring mode.

Results

Specific analysis of the amino acids was achieved within 2 min. Intra-run and total CVs for the amino acids were less than 2% and 4%, respectively, and the analytical recoveries ranged from 99.6 to 103.6%.

Conclusion

A rapid and precise method for the measurement of serum BCAAs and AAAs was developed and may serve as a quick tool for screening serum BCAAs and AAAs in studies assessing diabetes risk.     

Control of water activity in lipase catalysed esterification of chiral alkanoic acids

An enzymatic method for ready access to d-sedoheptulose-7-phosphate on a preparative scale was developed, based on the irreversible transketolase-catalyzed reaction: β-hydroxypyruvate + d-ribose-5-phosphate → d-sedoheptulose-7-phosphate. d-Sedoheptulose-7-phosphate disodium salt was obtained in 81% overall yield determined using a standard curve obtained by LC/MS/MS.     

MS Cortical Lesions on DIR: Not Quite What They Seem?

Objective

Accurate identification and localization of cortical gray matter (CGM) lesions in MS is important when determining their clinical relevance. Double inversion recovery (DIR) scans have been widely used to detect MS CGM lesions. Phase sensitive inversion recovery (PSIR) scans have a higher signal to noise, and can therefore be obtained at a higher resolution within clinically acceptable times. This enables detection of more CGM lesions depicting a clearer cortical and juxtacortical anatomy. In this study, we systematically investigated if the use of high resolution PSIR scans changes the classification of CGM lesions, when compared with standard resolution DIR scans.

Methods

60 patients [30 RR(Relapsing remitting) and 15 each with PP(Primary progressive) and SP(Secondary progressive) MS] were scanned on a 3T Philips Achieva MRI scanner. Images acquired included DIR (1×1×3 mm resolution) and PSIR (0.5×0.5×2 mm). CGM lesions were detected and classified on DIR as intracortical (IC) or leucocortical (LC). We then examined these lesions on corresponding slices of the high resolution PSIR scans and categorized them as IC, LC, Juxtacortical white matter (JC-WM, abutting but not entering cortex) and other white matter (WM, not juxtacortical). Classifications using both scans were noted.

Results

282 IC and 483 LC were identified on DIR. Of the IC lesions, 61% were confirmed as IC on PSIR, 35.5% were reclassified as LC and 3.5% as JC-WM or other WM only. Of the LC DIR lesions, 43.9% were confirmed at LC on PSIR, 16.1% were reclassified as IC and 40% as JC-WM or other WM only. Overall, 50% (381/765) of CGM lesions seen on DIR were reclassified, and 26.5% (203/765) affected WM only.

Conclusions

When compared with higher resolution PSIR, a significant proportion of lesions classified as involving CGM on DIR appear to either contain more white matter than expected or to not involve CGM at all.     

Molecular weight assessment of proteins in total proteome profiles using 1D-PAGE and LC/MS/MS

Background  

The observed molecular weight of a protein on a 1D polyacrylamide gel can provide meaningful insight into its biological function. Differences between a protein's observed molecular weight and that predicted by its full length amino acid sequence can be the result of different types of post-translational events, such as alternative splicing (AS), endoproteolytic processing (EPP), and post-translational modifications (PTMs). The characterization of these events is one of the important goals of total proteome profiling (TPP). LC/MS/MS has emerged as one of the primary tools for TPP, but since this method identifies tryptic fragments of proteins, it has not generally been used for large-scale determination of the molecular weight of intact proteins in complex mixtures.     

BPDA - A Bayesian peptide detection algorithm for mass spectrometry

Background  

Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides.     

Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD)

Introduction

Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC–MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.

Objective

Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using design of experiments (DoE).

Methods

We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD). Multivariate statistical analysis guided our decision process in the method optimizations.

Results

LC–MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5 % (p < 0.0001) over initial conditions with a 13.3 % increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8 and 57.3 %, with median metabolite response increases of 106.1 and 10.3 % (p < 0.0001 and p < 0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8 % response increase (p < 0.0001) over initial conditions.

Conclusions

The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.

     

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis

Background  

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods.     

Development and validation of an LC–MS/MS method for quantification of cyclic guanosine 3′,5′-monophosphate (cGMP) in clinical applications: A comparison with a EIA method

An LC–MS/MS method was developed and validated to quantify endogenous cyclic guanosine 3′,5′-monophosphate (cGMP) in human plasma. The LC–MS/MS and competitive enzyme immunoassay (EIA) assays were compared. cGMP concentrations of 20 human plasma samples were measured by both methods. For the MS-based assay, plasma samples were subjected to a simple protein precipitation procedure by acetonitrile prior to analysis by electrospray ionization LC–MS/MS. De-protonated analytes generated in negative ionization mode were monitored through multiple reaction monitoring (MRM). A stable isotope-labeled internal standard, ¹³C₁₀,¹⁵N₅-cGMP, which was biosynthesized in-house, was used in the LC–MS/MS method. The competitive EIA was validated using a commercially available cGMP fluorescence assay kit. The intra-assay accuracy and precision for MS-based assay for cGMP were 6–10.1% CV and ?3.6% to 7.3% relative error (RE), respectively, while inter-assay precision and accuracy were 5.6–8.1% CV and ?2.1% to 6.3% RE, respectively. The intra-assay accuracy and precision for EIA were 17.9–27.1% CV and ?4.9% to 24.5% RE, respectively, while inter-assay precision and accuracy were 15.1–39.5% CV and ?30.8% to 4.37% RE, respectively. Near the lower limits of detection, there was little correlation between the cGMP concentration values in human plasma generated by these two methods (R² = 0.197, P = 0.05). Overall, the MS-based assay offered better selectivity, recovery, precision and accuracy over a linear range of 0.5–20 ng/mL. The LC–MS/MS method provides an effective tool for the quantitation of cGMP to support clinical mechanistic studies of curative pharmaceuticals.     

Urinary amino acid analysis: A comparison of iTRAQ®–LC–MS/MS,GC–MS,and amino acid analyzer

Urinary amino acid analysis is typically done by cation-exchange chromatography followed by post-column derivatization with ninhydrin and UV detection. This method lacks throughput and specificity. Two recently introduced stable isotope ratio mass spectrometric methods promise to overcome those shortcomings. Using two blinded sets of urine replicates and a certified amino acid standard, we compared the precision and accuracy of gas chromatography/mass spectrometry (GC–MS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) of propyl chloroformate and iTRAQ^® derivatized amino acids, respectively, to conventional amino acid analysis. The GC–MS method builds on the direct derivatization of amino acids in diluted urine with propyl chloroformate, GC separation and mass spectrometric quantification of derivatives using stable isotope labeled standards. The LC–MS/MS method requires prior urinary protein precipitation followed by labeling of urinary and standard amino acids with iTRAQ^® tags containing different cleavable reporter ions distinguishable by MS/MS fragmentation. Means and standard deviations of percent technical error (%TE) computed for 20 amino acids determined by amino acid analyzer, GC–MS, and iTRAQ^®–LC–MS/MS analyses of 33 duplicate and triplicate urine specimens were 7.27 ± 5.22, 21.18 ± 10.94, and 18.34 ± 14.67, respectively. Corresponding values for 13 amino acids determined in a second batch of 144 urine specimens measured in duplicate or triplicate were 8.39 ± 5.35, 6.23 ± 3.84, and 35.37 ± 29.42. Both GC–MS and iTRAQ^®–LC–MS/MS are suited for high-throughput amino acid analysis, with the former offering at present higher reproducibility and completely automated sample pretreatment, while the latter covers more amino acids and related amines.     

Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma

Introduction

Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence.

Methods

Using ¹H NMR and DI–LC–MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls.

Results

We accurately quantified 181 metabolites in serum using a combination of ¹H NMR and DI–LC–MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value?=?0.002). Using the concentrations of PC-aa-C40:3, dl-carnitine, octanoyl-l-carnitine, ethanol, and methylmalonyl-l-carnitine we developed a diagnostic algorithm with an AUC (95% CI)?=?0.822 (0.665–0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p?<?0.05).

Conclusion

Targeted metabolomic analysis demonstrated significant differences in metabolic profiles of advanced stage (III and IV) melanoma patients as compared to controls. These differences may represent a potential avenue for the development of multi-marker serum-based assays for earlier detection of recurrences, allow for newer, more effective targeted therapy when tumor burden is less, and further elucidate the pathophysiologic changes that occur in melanoma.

     

Simultaneous determination of l-tryptophan impurities in meat products

L-tryptophan has been used as a feed additive for swine and poultry and as a nutrient supplement for humans. However, some impurities in l-tryptophan have been reported as causative components of eosinophilia-myalgia syndrome. Therefore, from a safety perspective, it is important to analyze meat samples for these impurities. This study aims to develop an analytical method for the simultaneous detection of l-tryptophan impurities in meat products using LC–MS/MS. Among the various impurities, detection methods for (S)-2-amino-3-(5-hydroxy-1H-indol-3-yl)propanoic acid (5-hydroxytryptophan) (HTP), 1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid (MTCA), 3a-hydroxy-1,2,3,3a,8,8a-hexahydropyrrolo-[2,3-b]-indole-2-carboxylic acid (PIC), and 1,1′-ethylidenebistryptophan (EBT) and 2-(3-indoylmethyl)-l-tryptophan (IMT) were developed. The developed method allowed simultaneous determination of these four impurities in 5 min. No interferences from the matrix were observed, and the method showed good sensitivity to each analyte. The method detection limit and limit of quantification in meat matrices were below 11.2 and 35.7 μg/kg, respectively.

     

Laboratory methods to improve SELDI peak detection and quantitation

Background  

Protein profiling with surface-enhanced laser desorption-ionisation time-of-flight mass spectrometry (SELDI-TOF MS) is a promising approach for biomarker discovery. Some candidate biomarkers have been identified using SELDI-TOF, but validation of these can be challenging because of technical parameters that effect reproducibility. Here we describe steps to improve the reproducibility of peak detection.     

Chemical and immunochemical identification of propanoyllysine derived from oxidized n-3 polyunsaturated fatty acid

It is known that n-3 polyunsaturated fatty acids (PUFAs), such as docosahexaenoic acid and eicosapentaenoic acid, are rapidly oxidized in vitro. N?-(propanoyl)lysine (propionyllysine, or PRL) is formed from the reaction of the oxidized products of n-3 PUFAs and lysine. To evaluate the oxidized n-3 PUFA-derived protein modifications in vivo, we have developed detection methods using a novel monoclonal antibody against PRL as well as liquid chromatography–mass spectrometry (LC/MS/MS). The antibody obtained specifically recognized PRL. A strong positive staining in atherosclerotic lesions of hypercholesterolemic rabbits was observed. We have also simultaneously identified and quantified both urinary PRL and urinary N?-(hexanoyl)lysine, using LC/MS/MS using isotope dilution methods. The level of urinary PRL (21.6 ± 10.6 μmol/mol of creatinine) significantly correlated with the other oxidative stress markers, 8-oxo-deoxyguanosine, dityrosine, and isoprostanes. The increase in the excretion of amide adducts into the urine of diabetic patients was also confirmed compared to healthy subjects. These results suggest that PRL may be good marker for n-3 PUFA-derived oxidative stress in vivo.