Combining selected reaction monitoring with discovery proteomics in limited biological samples

Simultaneous quantification of multiple proteins by selected reaction monitoring (SRM) has several applications in cell signaling studies including embryo proteomics. However, concerns have recently been raised over the specificity of SRM assays due to possible ion redundancy and/or sequence similarity of selected peptide with multiple non‐related proteins. In this Viewpoint article, we discuss some simple measures that can increase our confidence in the accuracy of SRM scans used in proteomic experiments. At least in embryonic samples from porcine species, these measures were found to be useful in validating MS‐identified differentially expressed proteins. Among the nine proteins analyzed by SRM assay, all the proteins that were found to be up‐ or down‐regulated in MS experiment were also faithfully up‐ or down‐regulated in SRM assay.     

Unique Ion Signature Mass Spectrometry, a Deterministic Method to Assign Peptide Identity

The growing use of selected reaction monitoring (SRM) mass spectrometry in proteomic analyses led us to investigate how to identify peptides by SRM using only a minimal number of fragment ions. By using a computational model of the SRM work flow we computed the potential interferences from other peptides in a given proteome. From these results, we selected the deterministic SRM addresses that contained sufficient information to confer peptide and protein identity that we termed unique ion signatures (UIS). We computationally showed that UIS comprised of only two transitions are diagnostic for >99% of Escherichia coli proteins and >96% of human proteins that possess a sequence-unique peptide. We demonstrated an example of experimental use of UIS using a modified SRM methodology to profile the E. coli tricarboxylic acid cycle from a single injection of cell lysate. In addition, we showed the potential of UIS to form the first functionally orthogonal approach to validate peptide assignments obtained from conventional analyses of MS/MS spectra. The UIS methodology is a novel deterministic peptide identification method for MS/MS spectra based on information content. These robust theoretical assays will have widespread use when integrated with previously collected MS/MS data and conventional proteomics technologies.Shotgun proteomic analyses using multidimensional LC/MS/MS show great capacity for rapid protein analysis. This is arguably the most prevalent work flow for high throughput comparative proteomics, utilizing information-dependent acquisition (IDA)¹ to acquire MS/MS triggered by the signals generated from incoming peptides (1–3). Despite the utility and widespread use of this approach, there remain inherent problems including a relatively high level of ambiguous and false peptide assignments (∼5%) as well as high numbers of unassigned mass spectra (4–6). The reason for this level of ambiguity stems in part from the non-deterministic nature of the identification algorithms. Without the use of reference standards the only way to know a spectrum was generated by a given peptide with absolute certainty is for the spectrum to contain a fragment pattern that conclusively demonstrates the presence of each amino acid. Unfortunately this level of coverage is extremely rare in proteomics data.More recently, selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) mass spectrometry methods have been deployed for proteomic analyses (7–20). This has occurred as proteomics has matured from a discovery-oriented discipline into a more targeted and quantitative field. The method is conventionally conducted using triple quadrupole mass spectrometers where two rounds of mass selection provide excellent fidelity and sensitivity to monitor one or more predetermined target peptides generally in the context of a complex sample such as a cell lysate. Using this approach the mass spectrometer continually monitors the selected precursor ion m/z (Q1) and a subsequent product ion m/z (Q3) from the target analyte. SRM experiments can be used to conduct several rounds of these scans targeting different product ions in an attempt to bolster the confidence that the Q1 → Q3 transitions monitor the intended analyte with fidelity. A key point of contrast with IDA experiments is the need to preselect target analytes for monitoring. This can be achieved by harvesting data from previous discovery-based experiments or by in silico predictions such as MRM-initiated detection and sequencing (MIDAS) (10, 12). Regardless the key underlying principle of SRM in proteomics applications is that the selected set of precursor and product ions contain sufficient information to proxy for the target peptide and thereby its protein of origin. Given that proteomics SRM experiments are conducted with a minimal set of transitions, one must accept that a degree of uncertainty resides in any such assay. To date, the magnitude of this uncertainty has not been studied. This remains a key point even with MS instruments capable of conducting subsequent full MS/MS scans triggered by SRM (e.g. QTrap) as these are lower sensitivity scans that may contain insufficient fragmentation data to conclusively confer peptide identity.The problem of interference is also present in SRM experiments. To achieve acceptable sensitivity a large Q1 m/z window (±0.3–1.0 m/z) is needed. This in turn allows other peptides with similar Q1 m/z and elution properties to interfere with detection of the desired target. The frequency of these interferences would likely increase as the complexity of the sample increases creating a greater likelihood of false positives. Clearly this is not an unexpected result as conventional peptide identification strategies utilizing tandem MS result in some false assignments. Therefore, it would be unreasonable to expect that SRM assays that typically utilize fewer product ions than MS/MS experiments would not also encounter similar interference (21).In this study we investigated the information content of SRM assays and in doing so exposed the potential redundancy. Computational simulations of the experiment enabled us to demonstrate that directed selection of SRM precursor and product ions can avoid the pitfalls of interference by selecting ion combinations that uniquely map to target peptides within the context of the simulation. We used these unique ion signatures (UIS) in a proof of concept study to direct SRM data acquisition for the exclusive detection of enzymes in the Escherichia coli tricarboxylic acid cycle. In addition, given that UIS have been calculated to uniquely define target peptides in the experimental context, we demonstrated the applicability of UIS as an orthogonal validation of peptide identity for traditional MS/MS experiments.     

Improving SRM assay development: a global comparison between triple quadrupole, ion trap, and higher energy CID peptide fragmentation spectra

In proteomics, selected reaction monitoring (SRM) is rapidly gaining importance for targeted protein quantification. The triple quadrupole mass analyzers used in SRM assays allow for levels of specificity and sensitivity hard to accomplish by more standard shotgun proteomics experiments. Often, an SRM assay is built by in silico prediction of transitions and/or extraction of peptide precursor and fragment ions from a spectral library. Spectral libraries are typically generated from nonideal ion trap based shotgun proteomics experiments or synthetic peptide libraries, consuming considerable time and effort. Here, we investigate the usability of beam type CID (or "higher energy CID" (HCD)) peptide fragmentation spectra, as acquired using an Orbitrap Velos, to facilitate SRM assay development. Therefore, peptide fragmentation spectra, obtained by ion-trap CID, triple-quadrupole CID (QqQ-CID) and Orbitrap HCD, originating from digested cellular lysates, were compared. Spectral comparison and a dedicated correlation algorithm indicated significantly higher similarity between QqQ-CID and HCD fragmentation spectra than between QqQ-CID and ion trap-CID spectra. SRM transitions generated using a constructed HCD spectral library increased SRM assay sensitivity up to 2-fold, when compared to the use of a library created from more conventionally used ion trap-CID spectra, showing that HCD spectra can assist SRM assay development.     

How specific is my SRM?: The issue of precursor and product ion redundancy

Selected reaction monitoring (SRM) MS is proving to be a popular approach for targeted quantitative proteomics. The use of proteotypic peptides as candidates for SRM analysis is a wise first step in SRM method design. The obvious reason for this is the need to avoid redundancy at the sequence level, however this is incidental. The true reason is that homologous peptides result in redundancy in the mass‐to‐charge domain. This may seem like a trivial subtlety, however, we believe this is an issue of far greater significance than the proteomic community is aware. This VIEWPOINT article serves to highlight the complexity associated with designing SRM assays in light of potential ion redundancy.     

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40–85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.Targeted proteomics using selected reaction monitoring (SRM)¹ and parallel reaction monitoring (PRM) is increasingly becoming the gold-standard method for peptide quantitation within complex biological matrices (1, 2). By focusing on monitoring only a handful of transitions (associated precursor and fragment ions) for targeted peptides, SRM experiments filter out background signals, which in turn increases the signal to noise ratio. SRM experiments are almost exclusively performed on triple-quadrupole instruments. These instruments can isolate single transitions as an ion beam and measure that beam with extremely sensitive ion-striking detectors. As a result, SRM experiments generally exhibit significantly more accurate quantitation when compared with similarly powered discovery based proteomics experiments, and frequently benefit from a much wider linear range of quantitation (3). SRM experiments often require less fractionation and can be run in shorter time on less expensive instrumentation. These factors allow researchers to greatly scale up the number of samples they can run, which in turn increases the power of their experiment.However, the process of developing an effective SRM assay is often cumbersome, as subtle differences in peptide sequence can have a profound impact on the physiochemical properties and subsquent SRM responses of a peptide. To successfully develop an SRM assay for a protein of interest, unique peptide sequences must be chosen that also produce a high SRM signal (e.g. high-responding peptides). Once identified, these high-responding peptides are often synthesized or purchased, and independently analyzed to determine the most sensitive transition pairs. Finally, the selected peptide and transition pairs must be tested in complex mixtures to screen for transitions with chemical noise interference and to validate the sensitivity of the assay within a particular sample matrix. Peptides and transitions that survive this lengthy screening process can then undergo absolute quantitation by calibrating the signal intensity against standards of known quantity.Although experimental methods have been developed to empirically determine a set of best responding peptides (4), these strategies can be time consuming and require analytical standards, which are currently unavailable for all proteins. More often than not, representative peptides are essentially chosen at random, using only a small number of criteria, such as having a reasonable length for detection in the mass spectrometer, a lack of methionine, and a preference for peptides containing proline (5). It is not uncommon for SRM assays to fail at the final validation steps simply because the peptides chosen in the first assay creation step happened to be unexpectedly poor responding peptides.In an effort to speed up the process of generating robust assays, several groups (6–9) have designed approaches to predict sets of proteotypic peptides using machine-learning algorithms. Proteotypic peptides are peptides commonly identified in shotgun proteomics experiments for a variety of reasons including high signal, low interference, and search engine compatible fragmentation. Enhanced Signature Peptide (ESP) Predictor (7) was the first successful modification of this prediction approach to use proteotypic peptides as a proxy for high-responding peptides for SRM-based quantitation. In brief, Fusaro et al. built a training data set from data-dependent acquired (DDA) yeast peptides and a proxy for their response was quantitated using extracted precursor ion chromatograms (XICs). The authors calculated 550 physiochemical properties for each peptide based on sequence alone and built a random forest classifier to differentiate between the high and low response groups. Other peptide prediction tools follow the same general methodology for developing training data sets. CONSeQuence (8) applies several machine learning strategies and a pared down list of 50 distinct peptide properties. Alternately, Peptide Prediction with Abundance (9) (PPA) uses a back-propagation neural network (10) trained with 15 distinct peptide properties selected from ESP Predictor''s 550. The authors of CONSeQuence and PPA found that their approaches outperformed the ESP Predictor on a variety of data sets.As with most machine learning-based tools, the generality of the training set to real-world data is key to the effectiveness of the resulting prediction tool. Although MS1 intensities extracted from DDA data can be useful for predicting high-responding peptides (11, 12), several factors make them less than ideal for generalizing to SRM and PRM experiments. In particular, DDA peptides must be identified before being quantified and key biochemical features beneficial for targeted analysis of transitions can reduce overall identification rates by producing fragment spectra that are difficult to interpret with typical search engines. By building training data sets on precursor intensities alone these tools ignore the fact that targeted assays actually use fragment ions for quantification. We propose that constructing training sets from DIA fragment intensities (13) will produce machine-learning tools that are more effective at modeling peptides that produce detectible transitions, rather than just proteotypic peptides.The use of digested proteins in training sets presents additional concerns. The observed variance in peptide intensities is confounded by variation in protein abundance. Converting peptide intensities to ranks can remove the dependence on varying protein levels at the cost of corrupting the training set with proteins that biochemically contain no high-responding peptides. PPA attempts to ease this concern by training with Intensity Based Absolute Quantitation values (14) for DDA peptides estimated from XICs. We hypothesize that constructing a training set from equimolar synthetic peptides removes most adverse effects of digestion from the training set, making it possible to construct a more generalizable tool.     

Selected reaction monitoring for quantitative proteomics: a tutorial

Systems biology relies on data sets in which the same group of proteins is consistently identified and precisely quantified across multiple samples, a requirement that is only partially achieved by current proteomics approaches. Selected reaction monitoring (SRM)—also called multiple reaction monitoring—is emerging as a technology that ideally complements the discovery capabilities of shotgun strategies by its unique potential for reliable quantification of analytes of low abundance in complex mixtures. In an SRM experiment, a predefined precursor ion and one of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted peptide can constitute a definitive assay. Typically, a large number of peptides are quantified during a single LC‐MS experiment. This tutorial explains the application of SRM for quantitative proteomics, including the selection of proteotypic peptides and the optimization and validation of transitions. Furthermore, normalization and various factors affecting sensitivity and accuracy are discussed.     

MRMaid 2.0: Mining PRIDE for Evidence-Based SRM Transitions

Abstract Selected reaction monitoring (SRM) is becoming the tool of choice for targeted quantitative proteomics. The fundamental principle of proteomic SRM is that, for a given protein of interest, there is a set of peptides that are unique to that protein. The characteristic retention time (RT), and intact peptide m/z of these so-called proteotypic peptides are then programmed into the mass spectrometer, along with the m/z of high-intensity product ions for targeted quantitation. The particular combination of RT, peptide m/z, and product m/z for a given peptide is referred to as a transition. Selection of the most appropriate set of transitions for a given set of proteins is crucial to any SRM experiment. We previously developed the web-based MRMaid tool, which suggested the optimal transitions for a given human protein by mining spectral evidence from a small in-house database. In this article we present a completely new implementation of MRMaid, which offers substantial improvements over the original. The new version, MRMaid 2.0, uses spectra from the EBI's PRIDE database, which massively increases the coverage and quality of transitions. Transition lists can now be generated for multiple proteins simultaneously, edited within the web browser, and exported for laboratory use.     

Increased Selectivity, Analytical Precision, and Throughput in Targeted Proteomics

Proteomics is gradually complementing large shotgun qualitative studies with hypothesis-driven quantitative experiments. Targeted analyses performed on triple quadrupole instruments in selected reaction monitoring mode are characterized by a high degree of selectivity and low limit of detection; however, the concurrent analysis of multiple analytes occurs at the expense of sensitivity because of reduced dwell time and/or selectivity due to limitation to a few transitions. A new data acquisition paradigm is presented in which selected reaction monitoring is performed in two ways to simultaneously quantify and confirm the identity of the targeted peptides. A first set of primary transitions is continuously monitored during a predetermined elution time window to precisely quantify each peptide. In addition, a set of six to eight transitions is acquired in a data-dependent event, triggered when all the primary transitions exceed a preset threshold. These additional transitions are used to generate composite tandem mass spectra to formally confirm the identity of the targeted peptides. This technique was applied to analyze the tryptic digest of a yeast lysate to demonstrate the performance of the technique. We showed a limit of detection down to tens of attomoles injected and a throughput exceeding 6000 transitions in one 60-min experiment. The technique was integrated into a linear work flow, including experimental design, data acquisition, and data evaluation, enabling large scale proteomic studies.Proteomics is gradually complementing qualitative studies focused on protein identification relying on shotgun strategies (1, 2) with large scale quantitative experiments. This change was prompted by the growing demand for qualification and verification of putative protein biomarkers through analysis of larger cohorts of clinical samples on one hand and the need for consistent quantitative data sets to facilitate modeling in systems biology studies on the other. In either case, the number of proteins under target is quite large (tens to hundreds), and traditional immunoassay approaches are not suited for use because of the cost, time, and difficulty of developing multiplexed assays. In this context, the selected reaction monitoring (SRM)¹ technique performed on a triple quadrupole mass spectrometer is increasingly applied to quantitative proteomics because of its sensitivity, selectivity, and wide dynamic range (3–6). Mass spectrometry assays can be developed very rapidly via the use of commodity synthetic reference peptides libraries (7), and large resources of peptide MS/MS data (MRMAtlas) (8) are available to design the initial assays. However, developing a robust and precise SRM-based multiple assay remains demanding for proteomics experiments.One of the main challenges is that most proteomics samples are highly complex, and several interfering signals are detected within a given time window that require systematic verification of the target peptide identity first to ensure its accurate quantification. Using isotopically labeled counterparts of the targeted analytes is a common way to confirm the target peptide identity (9, 10). As this may not always be practical for large scale quantitative proteomics studies, an alternative way to verify the peptide identity is to use SRM-initiated full MS/MS scans (11). However, the disadvantage of this method is a lower sensitivity and selectivity compared with SRM as it uses a broader mass selection window, which results in MS/MS spectra often containing signals from multiple components co-eluting in the case of biological samples with a complex background. Furthermore, SRM-initiated MS/MS scans require much longer duty cycle times that will disrupt the predefined SRM sequence of events in the case of complex multiplexed assays even when performed on a triple quadrupole instrument equipped with a linear ion trap. Recently, instead of using MS/MS spectra, a composite MS/MS spectrum that is generated by measuring multiple fragments ions (8–10 ions) from one specific peptide has been proven to provide sufficient information for peptide identification.² The peptide is verified both by the overlay of the chromatographic elution profiles of the fragment ions and by matching the composite MS/MS spectrum that comprises multiple SRM transitions to the MS/MS spectral library entry (12, 13). Because it is based on SRM acquisition, this method provides a rapid, sensitive, and selective way to perform peptide verification, which is desirable for large scale screening experiments. The drawback of this approach is that only a limited number of compounds can be practically analyzed in one HPLC-MS run because the instrument is continuously monitoring 8–10 transitions for each peptide no matter whether the peptide is detected from the sample or not, resulting in the waste of a significant portion of the instrument time. To overcome this issue and thus increase the throughput, we propose that the instrument constantly monitor only a small subset of SRM transitions for each peptide for the actual quantification and in addition confirm the peak identity using the full set of fragment ions, which are acquired in a data-dependent mode.To provide this instrument capability, we developed an innovative instrument control software, called intelligent selected reaction monitoring (iSRM), that can use the specificity of a small subset of SRM transitions to quantify and intelligently trigger the full list for confirmation of target peptides, thereby allowing the simultaneous qualitative and quantitative analysis of up to 1000 peptides in a single LC-MS experiment. Here we describe the concept of iSRM and the work flow associated to it, and we demonstrate the increased throughput facilitating the development of SRM assays and its ability to perform large scale screens targeting a respectable number of proteins.     

An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology

Selected reaction monitoring (SRM) is an accurate quantitative technique, typically used for small-molecule mass spectrometry (MS). SRM has emerged as an important technique for targeted and hypothesis-driven proteomic research, and is becoming the reference method for protein quantification in complex biological samples. SRM offers high selectivity, a lower limit of detection and improved reproducibility, compared to conventional shot-gun-based tandem MS (LC-MS/MS) methods. Unlike LC-MS/MS, which requires computationally intensive informatic postanalysis, SRM requires preacquisition bioinformatic analysis to determine proteotypic peptides and optimal transitions to uniquely identify and to accurately quantitate proteins of interest. Extensive arrays of bioinformatics software tools, both web-based and stand-alone, have been published to assist researchers to determine optimal peptides and transition sets. The transitions are oftentimes selected based on preferred precursor charge state, peptide molecular weight, hydrophobicity, fragmentation pattern at a given collision energy (CE), and instrumentation chosen. Validation of the selected transitions for each peptide is critical since peptide performance varies depending on the mass spectrometer used. In this review, we provide an overview of open source and commercial bioinformatic tools for analyzing LC-MS data acquired by SRM.     

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.     

Protein significance analysis in selected reaction monitoring (SRM) measurements

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.     

Direct quantitation of MHC-bound peptide epitopes by selected reaction monitoring

We describe a cell-free approach that employs selected reaction monitoring (SRM) in tandem mass spectrometry to identify and quantitate T-cell epitopes. This approach utilises multiple epitope-specific SRM transitions to identify known T-cell epitopes and an absolute quantitation (AQUA) peptide strategy to afford AQUA. The advantage of a mass spectrometry-based approach over more traditional cell-based assays resides in the robustness and transferability of an SRM approach between laboratories and the ability of this strategy to detect multiple peptides simultaneously without the requirement of epitope-specific reagents such as T-cell lines. Thus, the SRM strategy for epitope quantitation will find application in studies of antigen density, the link between epitope abundance and immunogenicity, the dynamic range of epitope presentation and the abundance of T-cell epitopes in disease.     

Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay

To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted proteomics assay allowed for reproducible quantification of 76 proteins in four different growth conditions after 24 and 48 h of perturbation. Differential expression of a number of control and metabolic pathway proteins in response to the change of growth conditions was found. Elevated expression of the majority of glycolytic enzymes was observed in hypoxia. Cancer cells, as opposed to near-normal MCF-10A cells, exhibited significantly increased expression of key energy metabolic pathway enzymes (FBP1, IDH2, and G6PD) that are known to redirect cellular metabolism and increase carbon flux through the pentose phosphate pathway. Our quantitative proteomic protocol is based on a mass spectrometry-compatible acid-labile detergent and is described in detail. Optimized parameters of a multiplex selected reaction monitoring (SRM) assay for 76 proteins, 134 proteotypic peptides, and 401 transitions are included and can be downloaded and used with any SRM-compatible mass spectrometer. The presented workflow is an integrated tool for hypothesis-driven studies of mammalian cells as well as functional studies of proteins, and can greatly complement experimental methods in systems biology, metabolic engineering, and metabolic transformation of cancer cells.     

Mass spectrometry-based detection and quantification of plasma glycoproteins using selective reaction monitoring

Mass spectrometry-based targeted proteomics is a rapidly expanding method for quantifying proteins in complex clinical samples such as plasma. In conjunction with the stable isotope dilution method, selected reaction monitoring (SRM) assays provide unparalleled sensitivity and selectivity for detection and quantification. A crucial factor for robust SRM assays is the reduction of interference by lowering the background. This can be achieved by the selective isolation of a subproteome, such as N-glycosylated proteins, from the original sample. The present protocol includes the development and optimization of SRM assays associated with each peptide of interest and the qualification of assays in the biological matrix to establish the limits of detection and quantification. The protocol also describes the enrichment of formerly N-glycosylated peptides relying on periodate oxidation of glycan moieties attached to the proteins, their immobilization on solid supports through hydrazide chemistry, proteolysis and enzymatic release of the formerly N-glycosylated peptides.     

Targeted quantitative analysis of superoxide dismutase 1 in cisplatin-sensitive and cisplatin-resistant human ovarian cancer cells

Protein quantification in a complex protein mixture presents a daunting task in biochemical analysis. Antibody-based immunoassays are traditional methods for protein quantification. However, there are issues associated with accuracy and specificity in these assays, especially when the changes are small (e.g., <2-fold). With recent developments in mass spectrometry, monitoring a selected peptide, thus protein, in a complex biological sample has become possible. In this study, we demonstrate a simple mass spectrometry-based method for selective measurement of a moderately low abundant protein, superoxide dismutase 1 (SOD1), in cisplatin-sensitive and cisplatin-resistant human ovarian cancer cells. Selected-reaction-monitoring (SRM) technology was employed to specifically analyze the target peptides in a pair of human ovarian cancer cell lines: 2008/2008-C13*5.25 (cisplatin-sensitive/cisplatin-resistant, respectively). The observed 1.47-fold higher expression in the resistant cell line is consistent with findings by other approaches. This robust liquid chromatography/mass spectrometry (LC/MS) method provides a powerful tool for targeted proteomic verification and/or validation studies.     

Evaluation of blood collection tubes using selected reaction monitoring MS: Implications for proteomic biomarker studies

An emphasis of current proteomic research is the validation of plasma protein biomarkers. The process of blood collection itself is critical to the accuracy and reproducibility of quantitative biomarker assays. We have developed selected reaction monitoring (SRM) assays to analyse thirteen abundant plasma proteins and evaluated the impact of three different blood collection tubes on the levels of these proteins. We also assessed the implications of the time taken to analyse plasma samples by evaluating the recovery of these proteins. We showed that SRM detects minor differences in the levels of some proteins which can be attributed to collection tube type. The average recovery for 12 of 18 assays was higher for proteins that were collected in tubes containing protease inhibitors compared to conventional collection tubes. For five of the assays, the differential recovery was statistically significant. Delaying MS analysis of a freeze‐thawed sample for 1 hour showed greatly reduced recovery of these analytes; however differences attributed to tube type were only evident at the baseline timepoint. Finally, we assessed the natural variation of circulating levels of these proteins in a cohort of seven healthy individuals. This study provides useful information for researchers contemplating blood collection for undertaking protein biomarker studies.     

Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition

Data-independent acquisition (DIA) methods have become increasingly popular in mass spectrometry–based proteomics because they enable continuous acquisition of fragment spectra for all precursors simultaneously. However, these advantages come with the challenge of correctly reconstructing the precursor–fragment relationships in these highly convoluted spectra for reliable identification and quantification. Here, we introduce a scan mode for the combination of trapped ion mobility spectrometry with parallel accumulation—serial fragmentation (PASEF) that seamlessly and continuously follows the natural shape of the ion cloud in ion mobility and peptide precursor mass dimensions. Termed synchro-PASEF, it increases the detected fragment ion current several-fold at sub-second cycle times. Consecutive quadrupole selection windows move synchronously through the mass and ion mobility range. In this process, the quadrupole slices through the peptide precursors, which separates fragment ion signals of each precursor into adjacent synchro-PASEF scans. This precisely defines precursor–fragment relationships in ion mobility and mass dimensions and effectively deconvolutes the DIA fragment space. Importantly, the partitioned parts of the fragment ion transitions provide a further dimension of specificity via a lock-and-key mechanism. This is also advantageous for quantification, where signals from interfering precursors in the DIA selection window do not affect all partitions of the fragment ion, allowing to retain only the specific parts for quantification. Overall, we establish the defining features of synchro-PASEF and explore its potential for proteomic analyses.     

Platform for establishing interlaboratory reproducibility of selected reaction monitoring-based mass spectrometry peptide assays

Mass spectrometry (MS) is an attractive alternative to quantification of proteins by immunoassays, particularly for protein biomarkers of clinical relevance. Reliable quantification requires that the MS-based assays are robust, selective, and reproducible. Thus, the development of standardized protocols is essential to introduce MS into clinical research laboratories. The aim of this study was to establish a complete workflow for assessing the transferability and reproducibility of selected reaction monitoring (SRM) assays between clinical research laboratories. Four independent laboratories in North America, using identical triple-quadrupole mass spectrometers (Quantum Ultra, Thermo), were provided with standard protocols and instrumentation settings to analyze unknown samples and internal standards in a digested plasma matrix to quantify 51 peptides from 39 human proteins using a multiplexed SRM assay. The interlaboratory coefficient of variation (CV) was less than 10% for 25 of 39 peptides quantified (12 peptides were not quantified based upon hydrophobicity) and exhibited CVs less than 20% for the remaining peptides. In this report, we demonstrate that previously developed research platforms for SRM assays can be improved and optimized for deployment in clinical research environments.     

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.     

基于质谱的选择反应监测技术相关策略和方法的研究进展

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