Assigning precursor–product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies

Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small molecules. In non-targeted metabolite profiling studies, instrument duty cycle limitations and experimental costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor–product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor–product relationships in idMS/MS data by appending a dataset-wide correlational analysis to existing tools. The utility of the approach was demonstrated using a dataset of standard compounds spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to standard MS/MS spectra of authentic standards, even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable molecular feature, which will streamline the identification process for non-targeted metabolite profiling studies.     

Charting molecular composition of phosphatidylcholines by fatty acid scanning and ion trap MS3 fragmentation

The molecular composition of phosphatidylcholines (PCs) in total lipid extracts was characterized by a combination of multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer and MS3 fragmentation on an ion trap mass spectrometer. Precursor ion spectra for 50 acyl anion fragments of fatty acids (fatty acid scanning) acquired in parallel increased the specificity and the dynamic range of the detection of PCs and identified the fatty acid moieties in individual PC species. Subsequent analysis of detected PC peaks by MS3 fragmentation on an ion trap mass spectrometer quantified the relative amount of their positional isomers, thus providing the most detailed and comprehensive characterization of the molecular composition of the pool of PCs at the low-picomole level. The method is vastly simplified, compared with conventional approaches, and does not require preliminary separation of lipid classes or of individual molecular species, enzymatic digestion, or chemical derivatization. The approach was validated by the comparative analysis of the molecular composition of PCs from human red blood cells. In the total lipid extract of Madin-Darby canine kidney II cells, we detected 46 PC species with unique fatty acid composition and demonstrated that the presence of positional isomers almost doubled the total number of individual molecular species.     

Low mass cutoff evasion with qz value optimization in ion trap

An ion trap is a powerful analyzer because of its high resolution, high sensitivity, and multistage mass analysis (MSⁿ) capabilities. Multiple fragmentation analysis provides useful information regarding peptide sequence and biomolecular structure; however, this approach is limited by an inherent low mass cutoff (LMCO) derived from collision-induced dissociation (CID). To avoid the LMCO for application of an ion trap to iTRAQ, we optimized the q_z value, which is a parameter that is proportional to the applied fundamental AC radio frequency voltage of a tandem mass spectrometry (MS/MS) event. Considering that many ion trap MS analyses employ CID as the MS/MS method, this method can be a practical one without any instrumental changes.     

Determination of clenbuterol in human urine by GC-MS-MS-MS: confirmation analysis in antidoping control

This work presents a GC-MS-MS-MS method for the direct determination of clenbuterol in human urine. The method comprises a pretreatment procedure and the instrumental analysis of the derivatives performed by GC-MS(3) (ion trap) with electron impact ionization. The GC-MS(3) analysis allows isolation and characterization of specific fragments from the original (MS(1)) molecular structure, and in particular, those fragments originating from the precursor ion cluster (m/z=335-337) characteristic of clenbuterol. The MS(2) product fragment m/z=300 is in turn used as a further precursor fragment giving rise to a MS(3) spectrum specific for clenbuterol. MS(4) fragmentation spectra were also investigated. However, further fragmentation of MS(3) product ions does not lead to functional MS(4) spectra nor to any significant increase in the signal-to-noise ratio. The sensitivity limit of the MS(3) technique is lower than 0.2 microg/l, with a linear range between 0.5 and 5 microg/l, thus matching the basic requirements for antidoping analysis according to the guidelines of the International Olympic Committee. Due to its overall analytical performance, the method is presently being evaluated as a confirmation protocol to be followed to detect illicit clenbuterol administration to the athletes, and compared with reference GC-MS and GC-MS-MS techniques.     

Automated comparative proteomics based on multiplex tandem mass spectrometry and stable isotope labeling

Comparative proteomic approaches using isotopic labeling and MS have become increasingly popular. Conventionally quantification is based on MS or extracted ion chromatogram (XIC) signals of differentially labeled peptides. However, in these MS-based experiments, the accuracy and dynamic range of quantification are limited by the high noise levels of MS/XIC data. Here we report a quantitative strategy based on multiplex (derived from multiple precursor ions) MS/MS data. One set of proteins was metabolically labeled with [13C6]lysine and [15N4]arginine; the other set was unlabeled. For peptide analysis after tryptic digestion of the labeled proteins, a wide precursor window was used to include both the light and heavy versions of each peptide for fragmentation. The multiplex MS/MS data were used for both protein identification and quantification. The use of the wide precursor window increased sensitivity, and the y ion pairs in the multiplex MS/MS spectra from peptides containing labeled and unlabeled lysine or arginine offered more information for, and thus the potential for improving, protein identification. Protein ratios were obtained by comparing intensities of y ions derived from the light and heavy peptides. Our results indicated that this method offers several advantages over the conventional XIC-based approach, including increased sensitivity for protein identification and more accurate quantification with more than a 10-fold increase in dynamic range. In addition, the quantification calculation process was fast, fully automated, and independent of instrument and data type. This method was further validated by quantitative analysis of signaling proteins in the EphB2 pathway in NG108 cells.     

A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns

Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.     

Gas chromatography/mass spectrometry of catechol estrogens.

Catecholestrogens (CCEs), namely 2- or 4-hydroxyestradiol and hydroxyestrone, are highly polar, reactive, and extremely labile estrogen metabolites in many experimental conditions. For these reasons, indirect assay methods mainly have been used. Some experimental evidence suggests that CCEs are synthesized and biologically active mostly in target cells. At this level, unfortunately, the indirect assays cannot be used. We present a method of gas chromatographic/mass spectral (GC/MS) analysis for the identification of individual CCEs; the major fragmentation ions of authentic estrogen standards as trimethylsilylether derivatives, and the MS patterns of the major CCEs, namely, 2-hydroxyestradiol and hydroxyestrone, are included. Few examples of CCEs detected in human breast cancer tissues and in breast cyst fluids are reported. Sample extracts were submitted to reversed-phase, high-performance liquid chromatography (RP-HPLC) and were quantified by "on line" electrochemical (EC) detection; thereafter, either crude extracts or single eluted peaks were submitted to GC/MS, by which detection limits of less than 5 pmol were attained. As expected, the molecular ion was the most relevant molecule in all but one case. On the contrary, the other relative intensities of major fragmentation ions M -15, M -30, M -90, and M -15 + (-90) were unevenly distributed, although represented in the majority of cases. In all cases, the GC/MS of peak fractions, purified by RP-HPLC and UV detection, confirmed the results of liquid chromatographic analysis combined with EC detection. In contrast, GC/MS of crude extracts was not equally satisfactory. Comparison of a liquid chromatography system with EC detection and the GC/MS approach revealed some inconsistency in quantitation of individual CCEs.(ABSTRACT TRUNCATED AT 250 WORDS)     

Automated identification and quantification of protein phosphorylation sites by LC/MS on a hybrid triple quadrupole linear ion trap mass spectrometer

Complete phosphorylation mapping of protein kinases was successfully undertaken using an automated LC/MS/MS approach. This method uses the direct combination of triple quadrupole and ion trapping capabilities in a hybrid triple quadrupole linear ion trap to selectively identify and sequence phosphorylated peptides. In particular, the use of a precursor ion scan of m/z -79 in negative ion mode followed by an ion trap high resolution scan (an enhanced resolution scan) and a high sensitivity MS/MS scan (enhanced product ion scan) in positive mode is a very effective method for identifying phosphorylation sites in proteins at low femtomole levels. Coupling of this methodology with a stable isotope N-terminal labeling strategy using iTRAQtrade mark reagents enabled phosphorylation mapping and relative protein phosphorylation levels to be determined between the active and inactive forms of the protein kinase MAPKAPK-1 in the same LC/MS run.     

A serum glycomics approach to breast cancer biomarkers

Because the glycosylation of proteins is known to change in tumor cells during the development of breast cancer, a glycomics approach is used here to find relevant biomarkers of breast cancer. These glycosylation changes are known to correlate with increasing tumor burden and poor prognosis. Current antibody-based immunochemical tests for cancer biomarkers of ovarian (CA125), breast (CA27.29 or CA15-3), pancreatic, gastric, colonic, and carcinoma (CA19-9) target highly glycosylated mucin proteins. However, these tests lack the specificity and sensitivity for use in early detection. This glycomics approach to find glycan biomarkers of breast cancer involves chemically cleaving oligosaccharides (glycans) from glycosylated proteins that are shed or secreted by breast cancer tumor cell lines. The resulting free glycan species are analyzed by MALDI-FT-ICR MS. Further structural analysis of the glycans can be performed in FTMS through the use of tandem mass spectrometry with infrared multiphoton dissociation. Glycan profiles were generated for each cell line and compared. These methods were then used to analyze sera obtained from a mouse model of breast cancer and a small number of serum samples obtained from human patients diagnosed with breast cancer or patients with no known history of breast cancer. In addition to the glycosylation changes detected in mice as mouse mammary tumors developed, glycosylation profiles were found to be sufficiently different to distinguish patients with cancer from those without. Although the small number of patient samples analyzed so far is inadequate to make any legitimate claims at this time, these promising but very preliminary results suggest that glycan profiles may contain distinct glycan biomarkers that may correspond to glycan "signatures of cancer."     

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.     

Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification

Fourier transform-all reaction monitoring (FT-ARM) is a novel approach for the identification and quantification of peptides that relies upon the selectivity of high mass accuracy data and the specificity of peptide fragmentation patterns. An FT-ARM experiment involves continuous, data-independent, high mass accuracy MS/MS acquisition spanning a defined m/z range. Custom software was developed to search peptides against the multiplexed fragmentation spectra by comparing theoretical or empirical fragment ions against every fragmentation spectrum across the entire acquisition. A dot product score is calculated against each spectrum to generate a score chromatogram used for both identification and quantification. Chromatographic elution profile characteristics are not used to cluster precursor peptide signals to their respective fragment ions. FT-ARM identifications are demonstrated to be complementary to conventional data-dependent shotgun analysis, especially in cases where the data-dependent method fails because of fragmenting multiple overlapping precursors. The sensitivity, robustness, and specificity of FT-ARM quantification are shown to be analogous to selected reaction monitoring-based peptide quantification with the added benefit of minimal assay development. Thus, FT-ARM is demonstrated to be a novel and complementary data acquisition, identification, and quantification method for the large scale analysis of peptides.     

SP19A Phosphoprotein profiling by negative mode precursor ion scanning

An important goal in cancer research is to monitor phosphoprotein changes in order to identify downstream targets of dysregulated signaling pathways. We used a precursor ion scanning approach described by Carr et al,¹ which identifies phosphopeptides in negative ion mode by their loss of a −79-Da signature ion (PO_3⁻). We first compared three methods for phosphopeptide detection in the protein kinase, Mps1. Using a 4000 QTrap mass spectrometer, standard analysis by LC/MS/MS in positive mode identified 27 phosphopeptides containing 22 phosphosites. Precursor ion scanning in negative mode using the same instrument identified 47 phosphopeptides containing 34 phosphosites, with detection sensitivity ~10 fmol. Using a LTQ-Orbitrap mass spectrometer, MS³ on peptide ions that underwent neutral loss of H₃PO₄ during MS/MS identified 30 phosphopeptides and 28 phosphosites. Thus, precursor ion scanning showed the highest performance in identifying phosphopeptides in simple mixtures.Next, we examined human melanoma cells treated with and without U0126, a drug that inhibits the constitutively activated B-Raf/MAPK pathway. Cytosolic proteins were resolved by SAX-FPLC, and proteins in each fraction were proteolyzed. Peptides in each fraction were separated by nanoflow RP-HPLC and phosphopeptides monitored by precursor ion scanning, triggering MS/MS upon detection of the −79-Da signature ion. In parallel, peptides were analyzed by positive mode LC/MS/MS in order to monitor protein abundance changes by spectral counting. In-house algorithms utilizing OpenMS modules were developed to detect phosphopeptide peaks, match them to MS/MS spectra, group peaks over consecutive fractions, and quantify and sum intensities. More than 20,000 peaks could be detected over all SAX fractions, representing ~5000 grouped phosphopeptide candidates. About 350 phosphopeptides were manually validated, of which ~10% were responsive to drug treatment. Thus, targets of dysregulated B-Raf/MAPK signaling in melanoma can be identified using precursor ion scanning and detection of phosphopeptides in complex samples.     

Collision energy optimization of b- and y-ions for multiple reaction monitoring mass spectrometry

Multiple reaction monitoring (MRM) is a highly sensitive and increasingly popular method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides and their corresponding proteins of interest within biological samples. The sensitivity of MRM-MS is highly dependent upon the tuning of transition-specific parameters, especially the collision energy (CE) applied during peptide fragmentation. Currently, empirical equations for CE work best for y-type ions and are much less effective for other types of transitions, such as b-type ions and small y-type transitions across particular amide bonds, which could also be useful for MRM-MS if optimized for maximum signal transmission. In this work, we have performed a CE optimization of all transitions for 80 doubly charged peptides, the results of which were used to define separate CE equations for b-ions and y-ions, as well as for small y-type ions derived from the fragmentation of amide bonds bounded on the amino-terminal side by aspartic or glutamic acid residues (D/E-X transitions). This analysis yielded four major observations: (1) b-ions tend to require lower collision energies than y-ions for optimal fragmentation, while D/E-X transitions tend to require more; (2) CE equations predict the optimal CEs more closely when product ion m/z dependence is included, in addition to the current standard of precursor ion m/z dependence; (3) separate CE equations for y-ions, b-ions, and D/E-X transitions are more effective than the previous one-size-fits-all equations, but best results are achieved by optimizing transitions individually; and (4) while b-ions gain substantial signal from CE optimization-often increases of several-fold-they still tend to rank lower than y-ions from the same peptide. These results confirm the notion that y-ions are usually the first-choice transitions for MRM experiments but also demonstrate, for the first time, that b-ions can be viable targets as well, if the proper collision energies are used.     

In situ Identification and Localization of IGHA2 in the Breast Tumor Microenvironment by Mass Spectrometry

Modifications in the tumor microenvironment (TME) play a major role in the establishment, progression, and metastasis of cancer. Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is a powerful technique that enables the simultaneous identification and localization of biological compounds within tissues. To detect markers of early TME remodeling in invasive breast cancer, we used MALDI-MSI to compare the molecular profiles of tissues from the breast cancer interface zone, tumor zone, and normal-tissue zone. Using direct-tissue MALDI tandem mass spectrometry (MS/MS), we identified immunoglobulin heavy constant alpha 2 (IGHA2) as a new, zone-specific protein in the breast TME. The zone-specific expression of IGHA2 was verified by immunoblotting and immunohistochemical analysis. IGHA2 expression was consistently positive in tumor cells that were metastatic to regional nodes, with intense expression along the cytoplasmic borders. As a factor related to an increased percentage of nodes with tumor metastasis, IGHA2 expression was upregulated 3.745-fold in cases with an increased number of cancerous nodes (p = 0.0468). Our results provide the first evidence of IGHA2 as a marker of the early process of TME remodeling in invasive breast cancer. Furthermore, IGHA2 may be a novel marker for regional metastases in the lymph nodes of patients with breast cancer.