Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 2009;27:190-198. [PMID: 19169245 DOI: 10.1038/nbt.1524]

In Vitro Transcription/Translation System: A Versatile Tool in the Search for Missing Proteins

Approximately 18% of all human genes purported to encode proteins have not been directly evidenced at the protein level, according to the validation criteria established by neXtProt, and are considered to be "missing" proteins. One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to identify as many of these missing proteins as possible in human samples using mass spectrometry-based methods. To further this goal, a consortium of C-HPP teams (chromosomes 5, 10, 16, and 19) has joined forces to devise new strategies to identify missing proteins by use of a cell-free in vitro transcription/translation system (IVTT). The proposed strategy employs LC-MS/MS data-dependent acquisition (DDA) and targeted selective reaction monitoring (SRM) methods to scrutinize low-complexity samples derived from IVTT. The optimized assays are then applied to identify missing proteins in human cells and tissues. We describe the approach and show proof-of-concept results for development of LC-SRM assays for identification of 18 missing proteins. We believe that the IVTT system, when coupled with downstream mass spectrometric identification, can be applied to identify proteins that have eluded more traditional methods of detection.

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 for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.

Quantifying reversible oxidation of protein thiols in photosynthetic organisms

Photosynthetic organisms use dynamic post-translational modifications to survive and adapt, which include reversible oxidative modifications of protein thiols that regulate protein structure, function, and activity. Efforts to quantify thiol modifications on a global scale have relied upon peptide derivatization, typically using isobaric tags such as TMT, ICAT, or iTRAQ that are more expensive, less accurate, and provide less proteome coverage than label-free approaches--suggesting the need for improved experimental designs for studies requiring maximal coverage and precision. Herein, we present the coverage and precision of resin-assisted thiol enrichment coupled to label-free quantitation for the characterization of reversible oxidative modifications on protein thiols. Using C. reinhardtii and Arabidopsis as model systems for algae and plants, we quantified 3662 and 1641 unique cysteinyl peptides, respectively, with median coefficient of variation (CV) of 13% and 16%. Further, our method is extendable for the detection of protein abundance changes and stoichiometries of cysteine oxidation. Finally, we demonstrate proof-of-principle for our method, and reveal that exogenous hydrogen peroxide treatment regulates the C. reinhardtii redox proteome by increasing or decreasing the level of oxidation of 501 or 67 peptides, respectively. As protein activity and function is controlled by oxidative modifications on protein thiols, resin-assisted thiol enrichment coupled to label-free quantitation can reveal how intracellular and environmental stimuli affect plant survival and fitness through oxidative stress.

Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.

Building high-quality assay libraries for targeted analysis of SWATH MS data

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

Studying macromolecular complex stoichiometries by peptide-based mass spectrometry

A majority of cellular functions are carried out by macromolecular complexes. A host of biochemical and spectroscopic methods exists to characterize especially protein/protein complexes, however there has been a lack of a universal method to determine protein stoichiometries. Peptide‐based MS, especially as a complementary method to the MS analysis of intact protein complexes, has now been developed to a point where it can be employed to assay protein stoichiometries in a routine manner. While the experimental demands are still significant, peptide‐based MS has been successfully applied to analyze stoichiometries for a variety of protein complexes from very different biological backgrounds. In this review, we discuss the requirements especially for targeted MS acquisition strategies to be used in this context, with a special focus on the interconnected experimental aspects of sample preparation, protein digestion, and peptide stability. In addition, different strategies for the introduction of quantitative peptide standards and their suitability for different scenarios are compared.

Targeted mass spectrometry for the analysis of nutritive modulation of catalase and heme oxygenase-1 expression

Comprehensive physiological food assessment requires recording of activity profiles. To elucidate the nutritive regulation of antioxidant enzymes, a generally applicable targeted MS method was established for the expression analysis of catalase and then adapted to heme oxygenase-1. Before tryptic digestion, target proteins were prefractionated by off-gel IEF of stimulated and control cell lysate. Targeted proteome analysis was achieved by LC coupled with scheduled selected reaction monitoring MS using 2 proteotypic peptides per protein and 3-4 transitions per peptide. Relative quantification was performed by stable isotope labeling by amino acids in cell culture (SILAC). The assay showed good correlation with results by Western blot. Linearity, precision, and sensitivity were even improved (LC/SRM vs. Western blot: 3 vs. 1 orders of magnitude, RSD 3.7-13.7% vs. 18.4%, LOD 0.2 vs. 1.6μg/mL). The developed method indicated that coffee does not modulate catalase expression in macrophages (T7cat 103±22%, T17cat 103±16%, p>0.05 vs. control), but leads to a highly significant increase of heme oxygenase-1 expression (T15Ho-1 420±24%, T22Ho-1 364±37%, p<0.001 vs. control, p>0.05 T15Ho-1 vs. T22Ho-1). In regard to multiplex options of the method, targeted proteome analysis can be a valuable tool for the comprehensive analysis of cellular effects of food components.

BIOLOGICAL SIGNIFICANCE

In the present study, targeted mass spectrometry was applied to determine the influence of food components on the expression of antioxidative enzymes. The results implicate that targeted proteomics may develop into a valuable tool in food science and nutrition to determine the physiological effects of nutrients. In contrast to conventional methods for expression analysis, targeted proteome analysis can be applied to monitor the effects of a food component on a broad range of cellular targets in parallel. Additionally, proteins or protein modifications can be addressed which elude immunochemical methods.

[A plain method of prediction of visibility of peptides in mass spectrometry with electrospray ionization]

A new method for screening of essential peptides for protein detection and quantification analysis in the direct positive electrospray mass spectrometry has been proposed. Our method is based on the prediction of the normalized abundance of the mass spectrometric peaks using a linear regression model. This method has the following limitations: (i) selected peptides should be taken so that at pH 2.5 the tested peptides must be presented mainly as the 2+ and 3+ ions; (ii) only peptides having C-terminal lysine or arginine residues are considered. The amino acid composition of the peptide, the peptide concentration, the ratio of the polar surface of peptide to common surface and ratio of the polar volume to common volume are used as independent variables in equation. Several combinations of variables were considered and the best linear regression model had a determination coefficient in leave-one-out validation procedure equal 0.54. This model confidently discriminates peptides with high response ability and peptides with low response ability, and therefore it allows to select only the most promising peptides. This screening method, a plain and fast, can be successfully applied to reduce the list of observed peptides.

Antibody-based capture of target peptides in multiple reaction monitoring experiments

Targeted quantitative mass spectrometry of immunoaffinity-enriched peptides, termed immuno-multiple reaction monitoring (iMRM), is a powerful method for determining the relative abundance of proteins in complex mixtures, like plasma or whole tissue. This technique combines 1,000-fold enrichment potential of antibodies for target peptides with the selectivity of multiple reaction monitoring mass spectrometry (MRM-MS). Using heavy isotope-labeled peptide counterparts as internal standards ensures high levels of precision. Further, LC-MRM-MS selectivity allows for multiplexing; antibodies recognizing different peptides can be added directly to a single mixture without subjecting to interferences common to other multiple antibody protein assays. Integrated extracted ion chromatograms (XIC) of product ions from endogenous unlabeled "light" peptide and stable isotope-labeled internal standard "heavy" peptides are used to generate a light/heavy peak area ratio. This ratio is proportional to the amount of peptide in the digestion mixture and can be used to estimate the concentration of protein in the sample.

Systematic Analysis of Yeast Proteome Reveals Peptide Detectability Factors for Mass Spectrometry

Here we used a data-independent acquisition (DIA) method, Precursor Acquisition Independent from Ion Count (PAcIFIC), to systematically profile the S. cerevisiae proteome. Direct PAcIFIC analysis of a yeast whole cell lysate (WCL) yielded 90% reproducibility between replicates and detected approximately 2000 proteins. When combined with sub-cellular fractionation, reproducibility was equally high and the number of detected yeast proteins approached 5000. As noted previously, this unbiased DIA approach identified so-called "orphan" peptides that could only be detected by tandem mass spectra because there was no detectable precursor ion. Using this unique dataset we examined features associated with peptide detectability and demonstrated that orphans were more likely to arise from low copy number proteins than proteins with median or high copy number. Finally, an investigation into why some orphans also arose from high copy number proteins found that, aside from protein copy number, there was a bias toward physiochemical factors associated with regions flanking the proteolytic cleavage sites of orphan peptides. This suggested that those orphan peptides originating from high abundance proteins were likely the result of inefficient protease release, which has implications for quantitative bottom-up proteomics.

Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA)

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

PeptideManager: a peptide selection tool for targeted proteomic studies involving mixed samples from different species

The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.

The selected reaction monitoring/multiple reaction monitoring-based mass spectrometry approach for the accurate quantitation of proteins: clinical applications in the cardiovascular diseases

Selected reaction monitoring, also known as multiple reaction monitoring, is a powerful targeted mass spectrometry approach for a confident quantitation of proteins/peptides in complex biological samples. In recent years, its optimization and application have become pivotal and of great interest in clinical research to derive useful outcomes for patient care. Thus, selected reaction monitoring/multiple reaction monitoring is now used as a highly sensitive and selective method for the evaluation of protein abundances and biomarker verification with potential applications in medical screening. This review describes technical aspects for the development of a robust multiplex assay and discussing its recent applications in cardiovascular proteomics: verification of promising disease candidates to select only the highest quality peptides/proteins for a preclinical validation, as well as quantitation of protein isoforms and post-translational modifications.

Quantitative proteomics at different depths in human articular cartilage reveals unique patterns of protein distribution

The articular cartilage of synovial joints ensures friction-free mobility and attenuates mechanical impact on the joint during movement. These functions are mediated by the complex network of extracellular molecules characteristic for articular cartilage. Zonal differences in the extracellular matrix (ECM) are well recognized. However, knowledge about the precise molecular composition in the different zones remains limited. In the present study, we investigated the distribution of ECM molecules along the surface-to-bone axis, using quantitative non-targeted as well as targeted proteomics.\ In a discovery approach, iTRAQ mass spectrometry was used to identify all extractable ECM proteins in the different layers of a human lateral tibial plateau full thickness cartilage sample. A targeted MRM mass spectrometry approach was then applied to verify these findings and to extend the analysis to four medial tibial plateau samples. In the lateral tibial plateau sample, the unique distribution patterns of 70 ECM proteins were identified, revealing groups of proteins with a preferential distribution to the superficial, intermediate or deep regions of articular cartilage. The detailed analysis of selected 29 proteins confirmed these findings and revealed similar distribution patterns in the four medial tibial plateau samples. The results of this study allow, for the first time, an overview of the zonal distribution of a broad range of cartilage ECM proteins and open up further investigations of the functional roles of matrix proteins in the different zones of articular cartilage in health and disease.

Deep proteomics of the Xenopus laevis egg using an mRNA-derived reference database

BACKGROUND

Mass spectrometry-based proteomics enables the global identification and quantification of proteins and their posttranslational modifications in complex biological samples. However, proteomic analysis requires a complete and accurate reference set of proteins and is therefore largely restricted to model organisms with sequenced genomes.

RESULTS

Here, we demonstrate the feasibility of deep genome-free proteomics by using a reference proteome derived from heterogeneous mRNA data. We identify more than 11,000 proteins with 99% confidence from the unfertilized Xenopus laevis egg and estimate protein abundance with approximately 2-fold precision. Our reference database outperforms the provisional gene models based on genomic DNA sequencing and references generated by other methods. Surprisingly, we find that many proteins in the egg lack mRNA support and that many of these proteins are found in blood or liver, suggesting that they are taken up from the blood plasma, together with yolk, during oocyte growth and maturation, potentially contributing to early embryogenesis.

CONCLUSION

To facilitate proteomics in nonmodel organisms, we make our platform available as an online resource that converts heterogeneous mRNA data into a protein reference set. Thus, we demonstrate the feasibility and power of genome-free proteomics while shedding new light on embryogenesis in vertebrates.

Using PeptideAtlas, SRMAtlas, and PASSEL: Comprehensive Resources for Discovery and Targeted Proteomics

PeptideAtlas, SRMAtlas, and PASSEL are Web-accessible resources to support discovery and targeted proteomics research. PeptideAtlas is a multi-species compendium of shotgun proteomic data provided by the scientific community; SRMAtlas is a resource of high-quality, complete proteome SRM assays generated in a consistent manner for the targeted identification and quantification of proteins; and PASSEL is a repository that compiles and represents selected reaction monitoring data, all in an easy-to-use interface. The databases are generated from native mass spectrometry data files that are analyzed in a standardized manner including statistical validation of the results. Each resource offers search functionalities and can be queried by user-defined constraints; the query results are provided in tables or are graphically displayed. PeptideAtlas, SRMAtlas, and PASSEL are publicly available freely via the Web site http://www.peptideatlas.org. In this protocol, we describe the use of these resources, we highlight how to submit, search, collate and download data.

Tandem mass spectral libraries of peptides in digests of individual proteins: Human Serum Albumin (HSA)

This work presents a method for creating a mass spectral library containing tandem spectra of identifiable peptide ions in the tryptic digestion of a single protein. Human serum albumin (HSA(1)) was selected for this purpose owing to its ubiquity, high level of characterization and availability of digest data. The underlying experimental data consisted of ∼3000 one-dimensional LC-ESI-MS/MS runs with ion-trap fragmentation. In order to generate a wide range of peptides, studies covered a broad set of instrument and digestion conditions using multiple sources of HSA and trypsin. Computer methods were developed to enable the reliable identification and reference spectrum extraction of all peptide ions identifiable by current sequence search methods. This process made use of both MS2 (tandem) spectra and MS1 (electrospray) data. Identified spectra were generated for 2918 different peptide ions, using a variety of manually-validated filters to ensure spectrum quality and identification reliability. The resulting library was composed of 10% conventional tryptic and 29% semitryptic peptide ions, along with 42% tryptic peptide ions with known or unknown modifications, which included both analytical artifacts and post-translational modifications (PTMs) present in the original HSA. The remaining 19% contained unexpected missed-cleavages or were under/over alkylated. The methods described can be extended to create equivalent spectral libraries for any target protein. Such libraries have a number of applications in addition to their known advantages of speed and sensitivity, including the ready re-identification of known PTMs, rejection of artifact spectra and a means of assessing sample and digestion quality.

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide-based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide-based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an "empirical rule for linearly correlated peptide selection (ERLPS)" in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label-free to O18/O16-labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide-based quantitative proteomics over a large dynamic range.

Quantitative targeted proteomics for membrane transporter proteins: method and application

Although global proteomics has shown promise for discovery of many new proteins, biomarkers, protein modifications, and polymorphisms, targeted proteomics is emerging in the proteomics research field as a complement to untargeted shotgun proteomics, particularly when a determined set of low-abundance functional proteins need to be measured. The function and expression of proteins related to drug absorption, distribution, metabolism, and excretion (ADME) such as cytochrome P450 enzymes and membrane transporters are of great interest in biopharmaceutical research. Since the variation in ADME-related protein expression is known to be a major complicating factor encountered during in vitro-in vivo and in vivo-in vivo extrapolations (IVIVE), the accurate quantification of the ADME proteins in complex biological systems becomes a fundamental element in establishing IVIVE for pharmacokinetic predictions. In this review, we provide an overview of relevant methodologies followed by a summary of recent applications encompassing mass spectrometry-based targeted quantifications of membrane transporters.

High-throughput absolute quantification of proteins using an improved two-dimensional reversed-phase separation and quantification concatemer (QconCAT) approach

Stable isotope dilution-selective reaction monitoring-mass spectrometry (SID-SRM-MS) has been widely used for the absolute quantitative analysis of proteins. However, when performing the large-scale absolute quantification of proteins from a more complex tissue sample, such as mouse liver, in addition to a high-throughput approach for the preparation and calibration of large amounts of stable-isotope-labelled internal standards, a more powerful separation method prior to SRM analysis is also urgently needed. To address these challenges, a high-throughput absolute quantification strategy based on an improved two-dimensional reversed-phase (2D RP) separation and quantification concatemer (QconCAT) approach is presented in this study. This strategy can be used to perform the simultaneous quantification of hundreds of proteins from mouse liver within one week of total MS measurement time. By using calibrated synthesised peptides from the protein glutathione S-transferase (GST), large amounts of GST-tagged QconCAT internal standards corresponding to hundreds of proteins can be accurately and rapidly quantified. Additionally, using an improved 2D RP separation method, a mixture containing a digested sample and QconCAT standards can be efficiently separated and absolutely quantified. When a maximum gradient of 72 min is employed in the first LC dimension, resulting in 72 fractions, identification and absolute quantification experiments for all fractions can be completed within one week of total MS measurement time. The quantification approach developed here can further extend the dynamic range and increase the analytical sensitivity of SRM analysis of complex tissue samples, thereby helping to increase the coverage of absolute quantification in a whole proteome.

Levels of germination proteins in Bacillus subtilis dormant, superdormant, and germinating spores

Bacterial endospores exhibit extreme resistance to most conditions that rapidly kill other life forms, remaining viable in this dormant state for centuries or longer. While the majority of Bacillus subtilis dormant spores germinate rapidly in response to nutrient germinants, a small subpopulation termed superdormant spores are resistant to germination, potentially evading antibiotic and/or decontamination strategies. In an effort to better understand the underlying mechanisms of superdormancy, membrane-associated proteins were isolated from populations of B. subtilis dormant, superdormant, and germinated spores, and the relative abundance of 11 germination-related proteins was determined using multiple-reaction-monitoring liquid chromatography-mass spectrometry assays. GerAC, GerKC, and GerD were significantly less abundant in the membrane fractions obtained from superdormant spores than those derived from dormant spores. The amounts of YpeB, GerD, PrkC, GerAC, and GerKC recovered in membrane fractions decreased significantly during germination. Lipoproteins, as a protein class, decreased during spore germination, while YpeB appeared to be specifically degraded. Some protein abundance differences between membrane fractions of dormant and superdormant spores resemble protein changes that take place during germination, suggesting that the superdormant spore isolation procedure may have resulted in early, non-committal germination-associated changes. In addition to low levels of germinant receptor proteins, a deficiency in the GerD lipoprotein may contribute to heterogeneity of spore germination rates. Understanding the reasons for superdormancy may allow for better spore decontamination procedures.

Practical Considerations and Current Limitations in Quantitative Mass Spectrometry-based Proteomics

Quantitative mass spectrometry (MS)-based proteomics continues to evolve through advances in sample preparation, chemical and biochemical reagents, instrumentation, and software. The breadth of proteomes and biological applications combined with unique experimental goals makes optimizing MS-based proteomics workflows a daunting task. Several MS-based instrument platforms are commercially available with LC-MS/MS being the most common for quantitative proteomics studies. Although the direction of LC-MS/MS instrumentation development is toward more user-friendly interfaces, there remain fundamental aspects of the technology that can be optimized for improving data quality. The intent of this chapter is to provide an introductory framework for understanding some of the more significant LC-MS/MS experimental conditions that can influence quantitative MS-based proteomics measurements, including electrospray ionization (ESI) bias and ion transmission efficiency. Because each commercial LC-MS/MS system is unique with regard to ESI source, transmission optics, ion isolation and trapping, ion fragmentation, and mass analysis, the use of design of experiments (DoE) is discussed as a potential approach for efficiently optimizing multiple inter-related factors.

Machine learning applications in proteomics research: how the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.

Proteomics for breast cancer urine biomarkers

Although the survival of breast cancer (BC) patients has increased over the last two decades due to improved screening programs and postoperative adjuvant systemic therapies, many patients die from metastatic relapse. Current biomarkers used in the clinic are not useful for the early detection of BC, or monitoring its progression, and have limited value in predicting response to treatment. The development of proteomic techniques has sparked new searches for novel protein markers for many diseases including BC. Proteomic techniques allow for a high-throughput analysis of samples with the visualization and quantification of thousands of potential protein and peptide markers. Human urine is one of the most interesting and useful biofluids for routine testing and provides an excellent resource for the discovery of novel biomarkers, with the advantage over tissue biopsy samples due to the ease and less invasive nature of collection. In this review, we summarize the results from studies where urine was used as a source for BC biomarker research and discuss urine sample preparation, its advantage, challenges, and limitation. We focus on the gel-based proteomic approaches as well as the recent development of quantitative techniques in BC urine biomarker detection. Finally, the future use of modern proteomic techniques in BC biomarker identification will be discussed.

Selected reaction monitoring mass spectrometry: a methodology overview

Moving past the discovery phase of proteomics, the term targeted proteomics combines multiple approaches investigating a certain set of proteins in more detail. One such targeted proteomics approach is the combination of liquid chromatography and selected or multiple reaction monitoring mass spectrometry (SRM, MRM). SRM-MS requires prior knowledge of the fragmentation pattern of peptides, as the presence of the analyte in a sample is determined by measuring the m/z values of predefined precursor and fragment ions. Using scheduled SRM-MS, many analytes can robustly be monitored allowing for high-throughput sample analysis of the same set of proteins over many conditions. In this chapter, fundaments of SRM-MS are explained as well as an optimized SRM pipeline from assay generation to data analyzed.

MOPED enables discoveries through consistently processed proteomics data

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.

Quantifying the effect of competition for detection between coeluting peptides on detection probabilities in mass-spectrometry-based proteomics

There are many factors that contribute to the variation in detection probabilities of proteins in LC-MS/MS experiments, and currently little is known about their relative importance. In this study, we analyze the effect of competition for detection between coeluting peptides on peptide detection probability. Using a novel method for estimating peptide detection probabilities, we show that these probabilities can vary by an order of magnitude between peptides that elute from the liquid chromatograph at the same time as many other peptides and those that elute with fewer other peptides. To explore these results, we use a mathematical model to show that competition for detection between peptides is expected to be a major source of missed detections in complex mixtures because there will be many MS/MS scanning intervals that contain more coeluting peptides than can be subjected to MS/MS analysis. Our data and simulation results show that the number of coeluting peptides is a primary determinant of whether a peptide will be detected. In our data, this had a several-fold larger effect on peptide detection probability than did peptide abundance. Furthermore, the distribution of elution times for the most frequently detected peptides was strongly shifted toward values where there were few coeluting peptides, indicating that the number of coeluting peptides is a major determinant of whether a peptide is proteotypic.

Accounting for population variation in targeted proteomics

Individual proteomes typically differ from the reference human proteome at ∼10,000 single amino acid variants. When viewed on the population scale, this individual variation results in a wide variety of protein sequences. In targeted proteomics experiments, such variability can confound accurate protein quantification. To assist researchers in identifying target peptides with high variability within the human population, we have created the Population Variation plug-in for Skyline, which provides easy access to the polymorphisms stored in dbSNP. Given a set of peptides, the tool reports minor allele frequency for common polymorphisms. We highlight the importance of considering genetic variation by applying the tool to public data sets.

A highly sensitive targeted mass spectrometric assay for quantification of AGR2 protein in human urine and serum

Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase liquid chromatography (LC) separations to fractionate and select target fractions for follow-on LC-selected reaction monitoring (LC-SRM) analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of <10% CV and limit of quantification (LOQ) values of ∼130 pg/mL in serum and ∼10 pg per 100 μg of total protein mass in urine, respectively. A good correlation (R(2) = 0.91) was observed for the measurable AGR2 concentrations in urine between SRM and enzyme-linked immunosorbent assay (ELISA). On the basis of an initial cohort of 37 subjects, urinary AGR2/PSA concentration ratios showed a significant difference (P = 0.026) between noncancer and cancer. Large clinical cohort studies are needed for the validation of AGR2 as a useful diagnostic biomarker for prostate cancer. Our work validated the approach of identifying candidate secreted protein biomarkers through genomics and measurement by targeted proteomics, especially for proteins where no immunoassays are available.

Comparison of protein immunoprecipitation-multiple reaction monitoring with ELISA for assay of biomarker candidates in plasma

Quantitative analysis of protein biomarkers in plasma is typically done by ELISA, but this method is limited by the availability of high-quality antibodies. An alternative approach is protein immunoprecipitation combined with multiple reaction monitoring mass spectrometry (IP-MRM). We compared IP-MRM to ELISA for the analysis of six colon cancer biomarker candidates (metalloproteinase inhibitor 1 (TIMP1), cartilage oligomeric matrix protein (COMP), thrombospondin-2 (THBS2), endoglin (ENG), mesothelin (MSLN) and matrix metalloproteinase-9 (MMP9)) in plasma from colon cancer patients and noncancer controls. Proteins were analyzed by multiplex immunoprecipitation from plasma with the ELISA capture antibodies, further purified by SDS-PAGE, digested and analyzed by stable isotope dilution MRM. IP-MRM provided linear responses (r = 0.978–0.995) between 10 and 640 ng/mL for the target proteins spiked into a “mock plasma” matrix consisting of 60 mg/mL bovine serum albumin. Measurement variation (coefficient of variation at the limit of detection) for IP-MRM assays ranged from 2.3 to 19%, which was similar to variation for ELISAs of the same samples. IP-MRM and ELISA measurements for all target proteins except ENG were highly correlated (r = 0.67–0.97). IP-MRM with high-quality capture antibodies thus provides an effective alternative method to ELISA for protein quantitation in biological fluids.

Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences

Each individual carries thousands of nonsynonymous single nucleotide variants (nsSNVs) in their genome, each corresponding to a single amino acid polymorphism (SAP) in the encoded proteins. It is important to be able to directly detect and quantify these variations at the protein level to study post-transcriptional regulation, differential allelic expression, and other important biological processes. However, such variant peptides are not generally detected in standard proteomic analyses due to their absence from the generic databases that are employed for mass spectrometry searching. Here we extend previous work that demonstrated the use of customized SAP databases constructed from sample-matched RNA-Seq data. We collected deep-coverage RNA-Seq data from the Jurkat cell line, compiled the set of nsSNVs that are expressed, used this information to construct a customized SAP database, and searched it against deep-coverage shotgun MS data obtained from the same sample. This approach enabled the detection of 421 SAP peptides mapping to 395 nsSNVs. We compared these peptides to peptides identified from a large generic search database containing all known nsSNVs (dbSNP) and found that more than 70% of the SAP peptides from this dbSNP-derived search were not supported by the RNA-Seq data and thus are likely false positives. Next, we increased the SAP coverage from the RNA-Seq derived database by utilizing multiple protease digestions, thereby increasing variant detection to 695 SAP peptides mapping to 504 nsSNV sites. These detected SAP peptides corresponded to moderate to high abundance transcripts (30+ transcripts per million, TPM). The SAP peptides included 192 allelic pairs; the relative expression levels of the two alleles were evaluated for 51 of those pairs and were found to be comparable in all cases.

Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring

Background

Serum biomarkers can improve diagnosis and treatment of malignant pleural mesothelioma (MPM). However, the evaluation of potential new serum biomarker candidates is hampered by a lack of assay technologies for their clinical evaluation. Here we followed a hypothesis-driven targeted proteomics strategy for the identification and clinical evaluation of MPM candidate biomarkers in serum of patient cohorts.

Results

Based on the hypothesis that cell surface exposed glycoproteins are prone to be released from tumor-cells to the circulatory system, we screened the surfaceome of model cell lines for potential MPM candidate biomarkers. Selected Reaction Monitoring (SRM) assay technology allowed for the direct evaluation of the newly identified candidates in serum. Our evaluation of 51 candidate biomarkers in the context of a training and an independent validation set revealed a reproducible glycopeptide signature of MPM in serum which complemented the MPM biomarker mesothelin.

Conclusions

Our study shows that SRM assay technology enables the direct clinical evaluation of protein-derived candidate biomarker panels for which clinically reliable ELISA’s currently do not exist.

Applications of selected reaction monitoring (SRM)-mass spectrometry (MS) for quantitative measurement of signaling pathways

Quantitative measurement of the major regulatory proteins in signaling networks poses several technical challenges, including low abundance, the presence of post-translational modifications (PTMs), and the lack of suitable affinity detection reagents. Using the innate immune response (IIR) as a model signaling pathway, we illustrate the approach of stable isotope dilution (SID)-selected reaction monitoring (SRM)-mass spectrometry (MS) assays for quantification of low abundance signaling proteins. A work flow for SID-SRM-MS assay development is established for proteins with experimentally observed MS spectra and for those without. Using the interferon response factor (IRF)-3 transcription factor as an example, we illustrate the steps in high responding signature peptide identification, SID-SRM-MS assay optimization, and evaluation. SRM assays for normalization of IIR abundance to invariant housekeeping proteins are presented. We provide an example of SID-SRM assay development for post-translational modification (PTM) detection using an activating phospho-Ser modified NF-κB/RelA transcription factor, and describe challenges inherent in PTM-SID-SRM-MS assay development. Application of highly qualified quantitative, SID-SRM-MS assays will enable a systems-level approach to understanding the dynamics and kinetics of signaling in host cells, such as the IIR.

Targeted quantification of low ng/mL level proteins in human serum without immunoaffinity depletion

We recently reported an antibody-free targeted protein quantification strategy, termed high-pressure, high-resolution separations with intelligent selection and multiplexing (PRISM), for achieving significantly enhanced sensitivity using selected reaction monitoring (SRM) mass spectrometry. Integrating PRISM with front-end IgY14 immunoaffinity depletion, sensitive detection of targeted proteins at 50-100 pg/mL levels in human blood plasma/serum was demonstrated. However, immunoaffinity depletion is often associated with undesired losses of target proteins of interest. Herein we report further evaluation of PRISM-SRM quantification of low-abundance serum proteins without immunoaffinity depletion. Limits of quantification (LOQ) at low ng/mL levels with a median coefficient of variation (CV) of ∼12% were achieved for proteins spiked into human female serum. PRISM-SRM provided >100-fold improvement in the LOQ when compared to conventional LC-SRM measurements. PRISM-SRM was then applied to measure several low-abundance endogenous serum proteins, including prostate-specific antigen (PSA), in clinical prostate cancer patient sera. PRISM-SRM enabled confident detection of all target endogenous serum proteins except the low pg/mL-level cardiac troponin T. A correlation coefficient >0.99 was observed for PSA between the results from PRISM-SRM and immunoassays. Our results demonstrate that PRISM-SRM can successfully quantify low ng/mL proteins in human plasma or serum without depletion. We anticipate broad applications for PRISM-SRM quantification of low-abundance proteins in candidate biomarker verification and systems biology studies.

Quantitation of the dynamic profiles of the innate immune response using multiplex selected reaction monitoring-mass spectrometry

The innate immune response (IIR) is a coordinated intracellular signaling network activated by the presence of pathogen-associated molecular patterns that limits pathogen spread and induces adaptive immunity. Although the precise temporal activation of the various arms of the IIR is a critical factor in the outcome of a disease, currently there are no quantitative multiplex methods for its measurement. In this study, we investigate the temporal activation pattern of the IIR in response to intracellular double-stranded RNA stimulation using a quantitative 10-plex stable isotope dilution-selected reaction monitoring-MS assay. We were able to observe rapid activation of both NF-κB and IRF3 signaling arms, with IRF3 demonstrating a transient response, whereas NF-κB underwent a delayed secondary amplification phase. Our measurements of the NF-κB-IκBα negative feedback loop indicate that about 20% of IκBα in the unstimulated cell is located within the nucleus and represents a population that is rapidly degraded in response to double-stranded RNA. Later in the time course of stimulation, the nuclear IκBα pool is repopulated first prior to its cytoplasmic accumulation. Examination of the IRF3 pathway components shows that double-stranded RNA induces initial consumption of the RIG-I PRR and the IRF3 kinase (TBK1). Stable isotope dilution-selected reaction monitoring-MS measurements after siRNA-mediated IRF3 or RelA knockdown suggests that a low nuclear threshold of NF-κB is required for inducing target gene expression, and that there is cross-inhibition of the NF-κB and IRF3 signaling arms. Finally, we were able to measure delayed noncanonical NF-κB activation by quantifying the abundance of the processed (52 kDa) NF-κB2 subunit in the nucleus. We conclude that quantitative proteomics measurement of the individual signaling arms of the IIR in response to system perturbations is significantly enabled by stable isotope dilution-selected reaction monitoring-MS-based quantification, and that this technique will reveal novel insights into the dynamics and connectivity of the IIR.

Review of software tools for design and analysis of large scale MRM proteomic datasets

Selective or Multiple Reaction monitoring (SRM/MRM) is a liquid-chromatography (LC)/tandem-mass spectrometry (MS/MS) method that enables the quantitation of specific proteins in a sample by analyzing precursor ions and the fragment ions of their selected tryptic peptides. Instrumentation software has advanced to the point that thousands of transitions (pairs of primary and secondary m/z values) can be measured in a triple quadrupole instrument coupled to an LC, by a well-designed scheduling and selection of m/z windows. The design of a good MRM assay relies on the availability of peptide spectra from previous discovery-phase LC-MS/MS studies. The tedious aspect of manually developing and processing MRM assays involving thousands of transitions has spurred to development of software tools to automate this process. Software packages have been developed for project management, assay development, assay validation, data export, peak integration, quality assessment, and biostatistical analysis. No single tool provides a complete end-to-end solution, thus this article reviews the current state and discusses future directions of these software tools in order to enable researchers to combine these tools for a comprehensive targeted proteomics workflow.

The effect of peptide adsorption on signal linearity and a simple approach to improve reliability of quantification

Peptide quantification using MS often relies on the comparison of peptide signal intensities between different samples, which is based on the assumption that observed signal intensity has a linear relationship to peptide abundance. A typical proteomics experiment is subject to multiple sources of variance, so we focussed here on properties affecting peptide linearity under simple, well-defined conditions. Peptides from a standard protein digest were analysed by multiple reaction monitoring (MRM) MS to determine peptide linearity over a range of concentrations. We show that many peptides do not display a linear relationship between signal intensity and amount under standard conditions. Increasing the organic content of the sample solvent increased peptide linearity by increasing the accuracy and precision of quantification, which suggests that peptide non-linearity is due to concentration-dependent surface adsorption. Using multiple peptides at various dilutions, we show that peptide non-linearity is related to observed retention time and predicted hydrophobicity. Whereas the effect of adsorption on peptide storage has been investigated previously, here we demonstrate the deleterious effect of peptide adsorption on the quantification of fresh samples, highlight aspects of sample preparation that can minimise the effect, and suggest bioinformatic approaches to enhance the selection of peptides for quantification.

BIOLOGICAL SIGNIFICANCE

Accurate quantification is central to many aspects of science, especially those examining dynamic processes or comparing molecular stoichiometries. In biological research, the quantification of proteins is an important yet challenging objective. Large-scale quantification of proteins using MS often depends on the comparison of peptide intensities with only a single-level calibrant (as in stable isotope labelling and absolute quantification approaches) or no calibrants at all (as in label-free approaches). For these approaches to be reliable, it is essential that the relationship between signal intensity and concentration is linear, without a significant intercept. Here, we show that peptide adsorption can severely affect this relationship, even under controlled conditions, and we demonstrate simple methodologies that can be used to moderate and predict this effect. These findings thus enable the quantification of proteins with increased robustness and reliability.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Targeted quantitation of proteins by mass spectrometry

Quantitative measurement of proteins is one of the most fundamental analytical tasks in a biochemistry laboratory, but widely used immunochemical methods often have limited specificity and high measurement variation. In this review, we discuss applications of multiple-reaction monitoring (MRM) mass spectrometry, which allows sensitive, precise quantitative analyses of peptides and the proteins from which they are derived. Systematic development of MRM assays is permitted by databases of peptide mass spectra and sequences, software tools for analysis design and data analysis, and rapid evolution of tandem mass spectrometer technology. Key advantages of MRM assays are the ability to target specific peptide sequences, including variants and modified forms, and the capacity for multiplexing that allows analysis of dozens to hundreds of peptides. Different quantitative standardization methods provide options that balance precision, sensitivity, and assay cost. Targeted protein quantitation by MRM and related mass spectrometry methods can advance biochemistry by transforming approaches to protein measurement.

Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.