Utility of accurate mass tags for proteome-wide protein identification

Adding Color to Mass Spectra of Biopolymers: Charge Determination Analysis (CHARDA) Assigns Charge State to Every Ion Peak

Traditionally, mass spectrometry (MS) output is the ion abundance plotted versus the ionic mass-to-charge ratio m/z. While employing only commercially available equipment, Charge Determination Analysis (CHARDA) adds a third dimension to MS, estimating for individual peaks their charge states z starting from z = 1 and color coding z in m/z spectra. CHARDA combines the analysis of ion signal decay rates in the time-domain data (transients) in Fourier transform (FT) MS with the interrogation of mass defects (fractional mass) of biopolymers. Being applied to individual isotopic peaks in a complex protein tandem (MS/MS) data set, CHARDA aids peptide mass spectra interpretation by facilitating charge-state deconvolution of large ionic species in crowded regions, estimating z even in the absence of an isotopic distribution (e.g., for monoisotopic mass spectra). CHARDA is fast, robust, and consistent with conventional FTMS and FTMS/MS data acquisition procedures. An effective charge-state resolution Rz ≥ 6 is obtained with the potential for further improvements.

Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support

RATIONALE

One of the important steps in initial data processing of peptide mass spectra is the detection of peptide features in full-range mass spectra. Ion mobility offers advantages over previous methods performing this detection by providing an additional structure-specific separation dimension. However, there is a lack of open-source software that utilizes these advantages and detects peptide features in mass spectra acquired along with ion mobility data using new instruments such as timsTOF and/or FAIMS-Orbitrap.

METHODS

Recently, a utility called Dinosaur was presented, which provides an efficient way for feature detection in peptide ion mass spectra. In this work we extended its functionality by developing Biosaur software to fully employ the additional information provided by ion mobility data. Biosaur was developed using the Python 3.8 programming language.

RESULTS

Biosaur supports the processing of data acquired using mass spectrometers with ion mobility capabilities, specifically timsTOF and FAIMS. In addition, it processes mass spectra obtained in negative ion mode and reports cosine correlation table for peptide features which is useful for differentiation between in-source fragments and semi-tryptic peptides.

CONCLUSIONS

Biosaur is a utility for detecting peptide features in liquid chromatography-mass spectra with ion mobility and negative ion supports. The software is distributed with an open-source APACHE 2.0 license and is freely available on Github: https://github.com/abdrakhimov1/Biosaur.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Retention Time Prediction and Protein Identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

Accurate mass and retention time library of serum lipids for type 1 diabetes research

Dysregulated lipid species are linked to various disease pathologies and implicated as potential biomarkers for type 1 diabetes (T1D). However, it is challenging to comprehensively profile the blood specimen lipidome with full structural details of every lipid molecule. The commonly used reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS)-based lipidomics approach is powerful for the separation of individual lipid species, but lipids belonging to different classes may still co-elute and result in ion suppression and misidentification of lipids. Using offline mixed-mode and RPLC-based two-dimensional separations coupled with MS/MS, a comprehensive lipidomic profiling was performed on human sera pooled from healthy and T1D subjects. The elution order of lipid molecular species on RPLC showed good correlations to the total number of carbons in fatty acyl chains and total number of double bonds. This observation together with fatty acyl methyl ester analysis was used to enhance the confidence of identified lipid species. The final T1D serum lipid library database contains 753 lipid molecular species with accurate mass and RPLC retention time uniquely annotated for each of the species. This comprehensive human serum lipid library can serve as a database for high-throughput RPLC-MS-based lipidomic analysis of blood samples related to T1D and other childhood diseases. Graphical abstract.

DART-ID increases single-cell proteome coverage

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.

Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets

Current mass spectrometry (MS)-based proteomics approaches are ineffective for mapping protein expression in tissue sections with high spatial resolution because of the limited overall sensitivity of conventional workflows. Here we report an integrated and automated method to advance spatially resolved proteomics by seamlessly coupling laser capture microdissection (LCM) with a recently developed nanoliter-scale sample preparation system termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples). The workflow is enabled by prepopulating nanowells with DMSO, which serves as a sacrificial capture liquid for microdissected tissues. The DMSO droplets efficiently collect laser-pressure catapulted LCM tissues as small as 20 μm in diameter with success rates >87%. We also demonstrate that tissue treatment with DMSO can significantly improve proteome coverage, likely due to its ability to dissolve lipids from tissue and enhance protein extraction efficiency. The LCM-nanoPOTS platform was able to identify 180, 695, and 1827 protein groups on average from 12-μm-thick rat brain cortex tissue sections having diameters of 50, 100, and 200 μm, respectively. We also analyzed 100-μm-diameter sections corresponding to 10-18 cells from three different regions of rat brain and comparatively quantified ∼1000 proteins, demonstrating the potential utility for high-resolution spatially resolved mapping of protein expression in tissues.

Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-μm-i.d. columns increase signal intensity by >3-fold relative to those using 75-μm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos MS significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ~3-fold increase in peptide identifications and 1.7-fold increase in identified protein groups for 2 ng tryptic digests of the bacterium S. oneidensis. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ~ 95% for 0.5 ng samples and by ~42% for 2 ng samples. Using the best combination of the above variables, we were able to identify >3,000 proteins from 10 ng tryptic digests from both HeLa and THP-1 mammalian cell lines. We also identified >950 proteins from subnanogram archaeal/bacterial cocultures. The present ultrasensitive LC-MS platform achieves a level of proteome coverage not previously realized for ultra-small sample loadings, and is expected to facilitate the analysis of subnanogram samples, including single mammalian cells.

Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM-DIA, which combines conventional DIA with wide-SIM (wide selected-ion monitoring) windows to partition the precursor m/z space to produce high-quality precursor ion chromatograms. However, WiSIM-DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM-DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse-enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM-DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.

Targeted In-Depth Quantification of Signaling Using Label-Free Mass Spectrometry

Protein phosphorylation encodes information on the activity of kinase-driven signaling pathways that regulate cell biology. This chapter discusses an approach, named TIQUAS (targeted in-depth quantification of signaling), to quantify cell signaling comprehensively and without bias. The workflow-based on mass spectrometry (MS) and computational science-consists of targeting the analysis of phosphopeptides previously identified by shotgun liquid chromatography tandem MS (LC-MS/MS) across the samples that are being compared. TIQUAS therefore takes advantage of concepts derived from both targeted (data-independent) and data-dependent acquisition methods; phosphorylation sites are quantified in all experimental samples regardless of whether or not these phosphopeptides were identified by MS/MS in all runs. As a result, datasets are obtained containing quantitative information on several thousand phosphorylation sites in as many samples and replicates as required in the experimental design, and these rich datasets are devoid of a significant number of missing data points. This chapter discussed the biochemical, analytical, and computational procedures required to apply the approach and for obtaining a biological interpretation of the data in the context of our understanding of cell signaling regulation and kinase-substrate relationships.

Top-Down Mass Spectrometry: Proteomics to Proteoforms

This chapter highlights many of the fundamental concepts and technologies in the field of top-down mass spectrometry (TDMS), and provides numerous examples of contributions that TD is making in biology, biophysics, and clinical investigations. TD workflows include variegated steps that may include non-specific or targeted preparative strategies, orthogonal liquid chromatography techniques, analyte ionization, mass analysis, tandem mass spectrometry (MS/MS) and informatics procedures. This diversity of experimental designs has evolved to manage the large dynamic range of protein expression and diverse physiochemical properties of proteins in proteome investigations, tackle proteoform microheterogeneity, as well as determine structure and composition of gas-phase proteins and protein assemblies.

Role of phosphoproteomics in the development of personalized cancer therapies

Cell signalling pathways driven by protein and lipid kinases contribute to the onset and progression of virtually all cancer types. Consequently, several inhibitors against these enzymes have clinical utility for the treatment of different forms of cancer. A problem that hampers further development is that not all patients respond equally well to kinase inhibitors and a significant proportion of those that initially respond eventually develop resistance. This review considers how an integrative analysis of kinase signalling may be used to address this issue. Advances in the biophysics of mass spectrometry, in biochemical procedures for phosphopeptide enrichment, and in computational approaches for label-free quantification have contributed to the development of phosphoproteomics workflows compatible with the analysis of clinical material. These developments, together with new bioinformatics tools to derive information on signalling circuitry from phosphoproteomics data, allow investigating kinase networks with unprecedented depth. Phosphoproteomics technology is starting to be used in translational research and, with further developments, such methods may also be able to measure the circuitry of cancer signalling networks in routine clinical assays. This review reflects on how this information could be used to accurately predict the best kinase inhibitor for each individual cancer patient.

Changes in protein expression across laboratory and field experiments in Geobacter bemidjiensis

Bacterial extracellular metal respiration, as carried out by members of the genus Geobacter, is of interest for applications including microbial fuel cells and bioremediation. Geobacter bemidjiensis is the major species whose growth is stimulated during groundwater amendment with acetate. We have carried out label-free proteomics studies of G. bemidjiensis grown with acetate as the electron donor and either fumarate, ferric citrate, or one of two hydrous ferric oxide mineral types as electron acceptor. The major class of proteins whose expression changes across these conditions is c-type cytochromes, many of which are known to be involved in extracellular metal reduction in other, better-characterized Geobacter species. Some proteins with multiple homologues in G. bemidjiensis (OmcS, OmcB) had different expression patterns than observed for their G. sulfurreducens homologues under similar growth conditions. We also compared the proteome from our study to a prior proteomics study of biomass recovered from an aquifer in Colorado, where the microbial community was dominated by strains closely related to G. bemidjiensis. We detected an increased number of proteins with functions related to motility and chemotaxis in the Colorado field samples compared to the laboratory samples, suggesting the importance of motility for in situ extracellular metal respiration.

Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis

Data-dependent acquisition (DDA) and data-independent acquisition strategies (DIA) have both resulted in improved understanding of proteomics samples. Both strategies have advantages and disadvantages that are well-published, where DDA is typically applied for deep discovery and DIA may be used to create sample records. In this paper, we present a hybrid data acquisition and processing strategy (pSMART) that combines the strengths of both techniques and provides significant benefits for qualitative and quantitative peptide analysis. The performance of pSMART is compared to published DIA strategies in an experiment that allows the objective assessment of DIA performance with respect to interrogation of previously acquired MS data. The results of this experiment demonstrate that pSMART creates fewer decoy hits than a standard DIA strategy. Moreover, we show that pSMART is more selective, sensitive, and reproducible than either standard DIA or DDA strategies alone.

Metabolomic profiling of ovarian carcinomas using mass spectrometry

Most of the research on tumor cell metabolism has focused on glucose utilization. However, when glucose is limited, solid tumors are forced to catabolize alternative substrates such as fatty acids and amino acids as an energy source. Measuring these alternations in tumor cell metabolism enables us to track neoplastic changes in the tissue to lead towards a more reliable diagnostic outcome. Although a very small number of elements are used in biochemistry, the metabolome is structurally diverse for the production of simple compounds such as phosphate and amino acids as well as more structurally complex compounds such as nucleotides, oligosaccharides, and complex lipids. Characterization of the metabolome, therefore, requires analytical methods that can handle a wide range of molecular structures and physicochemical properties, including solubility, polarity, and molecular weight. A further factor for consideration in the selection of technology for metabolomics is the wide range of concentrations of biochemical typically present in biological systems. MS has established itself as the high-throughput, information-rich, industrially stable approach to assess both the composition of diverse sample types as well as changes to that composition following perturbation.

Protein primary structure using orthogonal fragmentation techniques in Fourier transform mass spectrometry

Proteomics analysis using tandem mass spectrometry requires informative backbone fragmentation of peptide ions. Collision-activated dissociation (CAD) of cations alone is not sufficiently informative to satisfy all requirements. Thus, there is a need to supplement CAD with a complementary fragmentation technique. Electron capture dissociation (ECD) is complementary to collisional excitation in terms of the cleavage of a different bond (N-Calpha versus C-N bond) and other properties. CAD-ECD combination improves protein identification and enables high-throughput de novo sequencing of peptides. ECD and its variants are also useful in mapping labile post-translational modifications in proteins and isomer differentiation; for example, distinguishing Ile from Leu, iso-Asp from Asp and even D- from L-amino acid residues.

Peptide profiling of cerebrospinal fluid by mass spectrometry

The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2-4 microg/l; and CA125: 20-35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.

Increasing Confidence of LC-MS Identifications by Utilizing Ion Mobility Spectrometry

Ion mobility spectrometry in conjunction with liquid chromatography separations and mass spectrometry offers a range of new possibilities for analyzing complex biological samples. To fully utilize the information obtained from these three measurement dimensions, informatics tools based on the accurate mass and time tag methodology were modified to incorporate ion mobility spectrometry drift times for peptides observed in human serum. In this work a reference human serum database was created for 12,139 peptides and populated with the monoisotopic mass, liquid chromatography normalized elution time, and ion mobility spectrometry drift time(s) for each. We demonstrate that the use of three dimensions for peak matching during the peptide identification process resulted in an increased numbers of identifications and a lower false discovery rate relative to only using the mass and normalized elution time dimensions.

Getting intimate with trypsin, the leading protease in proteomics

Nowadays, mass spectrometry-based proteomics is carried out primarily in a bottom-up fashion, with peptides obtained after proteolytic digest of a whole proteome lysate as the primary analytes instead of the proteins themselves. This experimental setup crucially relies on a protease to digest an abundant and complex protein mixture into a far more complex peptide mixture. Full knowledge of the working mechanism and specificity of the used proteases is therefore crucial, both for the digestion step itself as well as for the downstream identification and quantification of the (fragmentation) mass spectra acquired for the peptides in the mixture. Targeted protein analysis through selected reaction monitoring, a relative newcomer in the specific field of mass spectrometry-based proteomics, even requires a priori understanding of protease behavior for the proteins of interest. Because of the rapidly increasing popularity of proteomics as an analytical tool in the life sciences, there is now a renewed demand for detailed knowledge on trypsin, the workhorse protease in proteomics. This review addresses this need and provides an overview on the structure and working mechanism of trypsin, followed by a critical analysis of its cleavage behavior, typically simply accepted to occur exclusively yet consistently after Arg and Lys, unless they are followed by a Pro. In this context, shortcomings in our ability to understand and predict the behavior of trypsin will be highlighted, along with the downstream implications. Furthermore, an analysis is carried out on the inherent shortcomings of trypsin with regard to whole proteome analysis, and alternative approaches will be presented that can alleviate these issues. Finally, some reflections on the future of trypsin as the workhorse protease in mass spectrometry-based proteomics will be provided.

Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times

In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.

Characterization of a high field Orbitrap mass spectrometer for proteome analysis

The field of proteomics continues to be driven by improvements in analytical technology, notably in peptide separation, quantitative MS, and informatics. In this study, we have characterized a hybrid linear ion trap high field Orbitrap mass spectrometer (Orbitrap Elite) for proteomic applications. The very high resolution available on this instrument allows 95% of all peptide masses to be measured with sub-ppm accuracy that in turn improves protein identification by database searching. We further confirm again that mass accuracy in tandem mass spectra is a valuable parameter for improving the success of protein identification. The new CID rapid scan type of the Orbitrap Elite achieves similar performance as higher energy collision induced dissociation fragmentation and both allow the identification of hundreds of proteins from as little as 0.1 ng of protein digest on column. The new instrument outperforms its predecessor the Orbitrap Velos by a considerable margin on each metric assessed that makes it a valuable and versatile tool for MS-based proteomics.

Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA)

Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA.

Definitions of terms relating to mass spectrometry (IUPAC Recommendations 2013)

This document contains recommendations for terminology in mass spectrometry. Development of standard terms dates back to 1974 when the IUPAC Commission on Analytical Nomenclature issued recommendations on mass spectrometry terms and definitions. In 1978, the IUPAC Commission on Molecular Structure and Spectroscopy updated and extended the recommendations and made further recommendations regarding symbols, acronyms, and abbreviations. The IUPAC Physical Chemistry Division Commission on Molecular Structure and Spectroscopy’s Subcommittee on Mass Spectroscopy revised the recommended terms in 1991 and appended terms relating to vacuum technology. Some additional terms related to tandem mass spectrometry were added in 1993 and accelerator mass spectrometry in 1994. Owing to the rapid expansion of the field in the intervening years, particularly in mass spectrometry of biomolecules, a further revision of the recommendations has become necessary. This document contains a comprehensive revision of mass spectrometry terminology that represents the current consensus of the mass spectrometry community.

Improving qualitative and quantitative performance for MS(E)-based label-free proteomics

Label-free quantitation by data independent methods (for instance MS(E)) is growing in popularity due to the high technical reproducibility of mass spectrometry analysis. The recent introduction of Synapt hybrid instruments capable of incorporating ion mobility separation within mass spectrometry analysis now allows acquisition of high definition MS(E) data (HDMS(E)). HDMS(E) enables deeper proteome coverage and more confident peptide identifications when compared to MS(E), while the latter offers a higher dynamic range for quantitation. We have developed synapter as, a versatile tool to better evaluate the results of data independent acquisitions on Waters instruments. We demonstrate that synapter can be used to combine HDMS(E) and MS(E) data to achieve deeper proteome coverage delivered by HDMS(E) and more accurate quantitation for high intensity peptides, delivered by MS(E). For users who prefer to run samples exclusively in one mode, synapter allows other useful functionality like false discovery rate estimation, filtering on peptide match type and mass error, and filling missing values. Our software integrates with existing tools, thus permitting us to easily combine peptide quantitation information into protein quantitation by a range of different approaches.

Combination of Edman degradation of peptides with liquid chromatography/mass spectrometry workflow for peptide identification in bottom-up proteomics

RATIONALE

High-throughput methods of proteomics are essential for identification of proteins in a cell or tissue under certain conditions. Most of these methods require tandem mass spectrometry (MS/MS). A multidimensional approach including predictive chromatography and partial chemical degradation could be a valuable alternative and/or addition to MS/MS.

METHODS

In the proposed strategy peptides are identified in a three-dimensional (3D) search space consisting of retention time (RT), mass, and reduced mass after one-step partial Edman degradation. The strategy was evaluated in silico for two databases: baker's yeast and human proteins. Rates of unambiguous identifications were estimated for mass accuracies from 0.001 to 0.05 Da and RT prediction accuracies from 0.1 to 5 min. Rates of Edman reactions were measured for test peptides.

RESULTS

A 3D description of proteolytic peptides allowing unambiguous identification without employing MS/MS of up to 95% and 80% of tryptic peptides from the yeast and human proteomes, respectively, was considered. Further extension of the search space to a four-dimensional one by incorporating the second N-terminal amino acid residue as the fourth dimension was also considered and was shown to result in up to 90% of human peptides being identified unambiguously.

CONCLUSIONS

The proposed 3D search space can be a useful alternative to MS/MS-based peptide identification approach. Experimental implementations of the proposed method within the on-line liquid chromatography/mass spectrometry (LC/MS) and off-line matrix-assisted laser desorption/ionization (MALDI) workflows are in progress.

Introduction to glycosylation and mass spectrometry

Glycosylation is increasingly recognized as a common and biologically significant post-translational modification of proteins. Modern mass spectrometry methods offer the best ways to characterize the glycosylation state of proteins. Both glycobiology and mass spectrometry rely on specialized nomenclature, techniques, and knowledge, which pose a barrier to entry by the nonspecialist. This introductory chapter provides an overview of the fundamentals of glycobiology, mass spectrometry methods, and the intersection of the two fields. Foundational material included in this chapter includes a description of the biological process of glycosylation, an overview of typical glycoproteomics workflows, a description of mass spectrometry ionization methods and instrumentation, and an introduction to bioinformatics resources. In addition to providing an orientation to the contents of the other chapters of this volume, this chapter cites other important works of potential interest to the practitioner. This overview, combined with the state-of-the-art protocols contained within this volume, provides a foundation for both glycobiologists and mass spectrometrists seeking to bridge the two fields.

Retention time prediction and protein identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.

An ultra-fast metabolite prediction algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

Label-free quantitative LC-MS proteomics of Alzheimer's disease and normally aged human brains

Quantitative proteomics analysis of cortical samples of 10 Alzheimer's disease (AD) brains versus 10 normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values <0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty-seven of these proteins were reported as differentially abundant or modified in AD in previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

Identification of low-abundance cancer biomarker candidate TIMP1 from serum with lectin fractionation and peptide affinity enrichment by ultrahigh-resolution mass spectrometry

As investigating a proteolytic target peptide originating from the tissue inhibitor of metalloproteinase 1 (TIMP1) known to be aberrantly glycosylated in patients with colorectal cancer (CRC), we first confirmed that TIMP1 is to be a CRC biomarker candidate in human serum. For this, we utilized matrix-assisted laser desorption/ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) showing ultrahigh-resolution and high mass accuracy. This investigation used phytohemagglutinin-L(4) (L-PHA) lectin, which shows binding affinity to the β-1,6-N-acetylglucosamine moiety of N-linked glycan on a protein, to compare fractionated aberrant protein glycoforms from both noncancerous control and CRC serum. Each lectin-captured fraction containing aberrant glycoforms of TIMP1 was digested by trypsin, resulting in the tryptic target peptide, representative of the serum glycoprotein TIMP1. The resulting target peptide was enriched using a stable isotope standard and capture by the antipeptide antibody (SISCAPA) technique and analyzed by a 15 T MALDI FTICR mass spectrometer with high mass accuracy (Δ < 0.5 ppm to the theoretical mass value of the target peptide). Since exact measurement of multiplex isotopic peaks of the target peptide could be accomplished by virtue of high mass resolution (Rs > 400,000), robust identification of the target peptide is only achievable with 15 T FTICR MS. Also, MALDI data obtained in this study showed that the L-PHA-captured glycoforms of TIMP1 were measured in the pooled CRC serum with about 5 times higher abundance than that in the noncancerous serum, and were further proved by MRM mass analysis. These results confirm that TIMP1 in human serum is a potent CRC biomarker candidate, demonstrating that ultrahigh-resolution MS can be a powerful tool toward identifying and verifying potential protein biomarker candidates.

Detection and characterization of low abundance glycopeptides via higher-energy C-trap dissociation and orbitrap mass analysis

Broad-scale mass spectrometric analyses of glycopeptides are constrained by the considerable complexity inherent to glycoproteomics, and techniques are still being actively developed to address the associated analytical difficulties. Here we apply Orbitrap mass analysis and higher-energy C-trap dissociation (HCD) to facilitate detailed insights into the compositions and heterogeneity of complex mixtures of low abundance glycopeptides. By generating diagnostic oxonium product ions at mass measurement errors of <5 ppm, highly selective glycopeptide precursor ion detections are made at sub-fmol limits of detection: analyses of proteolytic digests of a hen egg glycoprotein mixture detect 88 previously uncharacterized glycopeptides from 666 precursor ions selected for MS/MS, with only one false positive due to co-fragmentation of a non-glycosylated peptide with a glycopeptide. We also demonstrate that by (1) identifying multiple series of glycoforms using high mass accuracy single stage MS spectra, and (2) performing product ion scans at optimized HCD collision energies, the identification of peptide + N-acetylhexosamine (HexNAc) ions (Y1 ions) can be readily achieved at <5 ppm mass measurement errors. These data allow base peptide sequences and glycan compositional information to be attained with high confidence, even for glycopeptides that produce weak precursor ion signals and/or low quality MS/MS spectra. The glycopeptides characterized from low fmol abundances using these methods allow two previously unreported glycosylation sites on the Gallus gallus protein ovoglycoprotein (amino acids 82 and 90) to be confirmed; considerable glycan heterogeneities at amino acid 90 of ovoglycoprotein, and amino acids 34 and 77 of Gallus gallus ovomucoid are also revealed.

An improved calibration method for the matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resononance analysis of 15N-metabolically- labeled proteome digests using a mass difference approach

High mass measurement accuracy of peptides in enzymatic digests is critical for confident protein identification and characterization in proteomics research. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) can provide low or sub-ppm mass accuracy and ultrahigh resolving power. While for ESI-FT-ICR-MS, the mass accuracy is generally 1 ppm or better, with matrix-assisted laser desorption/ionization (MALDI)-FT-ICR-MS, the mass errors can vary from sub-ppm with internal calibration to over 100 ppm with conventional external calibration. A novel calibration method for (15)N-metabolically labeled peptides from a batch digest of a proteome is described which corrects for space charge induced frequency shifts in FT-ICR spectra without using an internal calibrant. This strategy utilizes the information from the mass difference between the (14)N/(15)N peptide peak pairs to correct for space charge induced mass shifts after data collection. A procedure for performing the mass correction has been written into a computer program and has been successfully applied to high-performance liquid chromatography-MALDI-FT- ICR-MS measurement of (15)N-metabolic labeled proteomes. We have achieved an average measured mass error of 1.0 ppm and a standard deviation of 3.5 ppm for 900 peptides from 68 MALDI-FT-ICR mass spectra of the proteolytic digest of a proteome from Methanococcus maripaludis.

Identification of Phosphorylated Human Peptides by Accurate Mass Measurement Alone

At sufficiently high mass accuracy, it is possible to distinguish phosphorylated from unmodified peptides by mass measurement alone. We examine the feasibility of that idea, tested against a library of all possible in silico tryptic digest peptides from the human proteome database. The overlaps between in silico tryptic digest phosphopeptides generated from known phosphorylated proteins (1-12 sites) and all possible unmodified human peptides are considered for assumed mass error ranges of ±10, ±50, ±100, ±1,000, and ±10,000 ppb. We find that for mass error ±50 ppb, 95% of all phosphorylated human tryptic peptides can be distinguished from nonmodified peptides by accurate mass alone through the entire nominal mass range. We discuss the prospect of on-line LC MS/MS to identify phosphopeptide precursor ions in MS1 for selected dissociation in MS2 to identify the peptide and site(s) of phosphorylation.

SILACtor: software to enable dynamic SILAC studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods.

Periodicity of monoisotopic mass isomers and isobars in proteomics

We report trends in the theoretically derived number of compositionally distinct peptides (i.e., peptides made up of different amino acid residues) up to a nominal mass of 1000. A total of 21 amino acid residues commonly found in proteomics studies are included in this study, 19 natural, nonisomeric amino acid residues as well as oxidated methione and acetamidated cysteine. The number of possibilities is found to increase in an exponential fashion with increasing nominal mass, and the data show a periodic oscillation that starts at mass ~200 and continues throughout to 1000. Note that similar effects are reported in the companion article on fragment ions from electron capture/transfer dissociation (ECD/ETD) (Mao et al. Anal. Chem.2011, DOI: 10.1021/ac201619t). The spacing of this oscillation is ~15 mass units at lower masses and ~14 mass units at higher nominal masses. This correlates with the most common mass differences between the amino acid building blocks. In other words, some mass differences are more common than others, thus determining the periodicity in this data. From an analytical point of view, nominal masses with a larger number of compositionally distinct peptides include a substantial number of isomers, which cannot be separated based on mass. Consequently, even ultrahigh mass accuracy (i.e., 0.5 ppm) does not lead to a substantially enhanced rate of identification. Conversely, for adjacent nominal masses with a lower number of isomers, moderately accurate mass (i.e., 10 ppm) gives a higher degree of certainty in identification. These effects are limited to the mass range between 200 and 500 Da. At higher masses, the percentage of uniquely identified peptides drops off to close to zero, independent of nominal mass, due the inherently high number of isomers. While the exact number of isobars/isomers at each nominal mass depends on the amino acid building blocks that are considered, the periodicity in the data is found to be remarkably robust; for instance, inclusion of phosphorylated residues barely affects the pattern at lower masses (i.e., <500 Da).

Differential proteomic analysis of Rickettsia prowazekii propagated in diverse host backgrounds

The obligate intracellular growth of Rickettsia prowazekii places severe restrictions on the analysis of rickettsial gene expression. With a small genome, predicted to code for 835 proteins, identifying which proteins are differentially expressed in rickettsiae that are isolated from different hosts or that vary in virulence is critical to an understanding of rickettsial pathogenicity. We employed a liquid chromatography (LC)-linear trap quadrupole (LTQ)-Orbitrap mass spectrometer for simultaneous acquisition of quantitative mass spectrometry (MS)-only data and tandem mass spectrometry (MS-MS) sequence data. With the use of a combination of commercially available algorithms and in-house software, quantitative MS-only data and comprehensive peptide coverage generated from MS-MS were integrated, resulting in the assignment of peptide identities with intensity values, allowing for the differential comparison of complex protein samples. With the use of these protocols, it was possible to directly compare protein abundance and analyze changes in the total proteome profile of R. prowazekii grown in different host backgrounds. Total protein extracted from rickettsiae grown in murine, tick, and insect cell lines or hen egg yolk sacs was analyzed. Here, we report the fold changes, including an upregulation of shock-related proteins, in rickettsiae cultivated in tissue culture compared to the level for rickettsiae harvested from hen yolk sacs. The ability to directly compare, in a complex sample, differential rickettsial protein expression provides a snapshot of host-specific proteomic profiles that will help to identify proteins important in intracellular growth and virulence.

Simultaneous quantitation of amino acid mixtures using clustering agents

A method that uses the abundances of large clusters formed in electrospray ionization to determine the solution-phase molar fractions of amino acids in multi-component mixtures is demonstrated. For solutions containing either four or 10 amino acids, the relative abundances of protonated molecules differed from their solution-phase molar fractions by up to 30-fold and 100-fold, respectively. For the four-component mixtures, the molar fractions determined from the abundances of larger clusters consisting of 19 or more molecules were within 25% of the solution-phase molar fractions, indicating that the abundances and compositions of these clusters reflect the relative concentrations of these amino acids in solution, and that ionization and detection biases are significantly reduced. Lower accuracy was obtained for the 10-component mixtures where values determined from the cluster abundances were typically within a factor of three of their solution molar fractions. The lower accuracy of this method with the more complex mixtures may be due to specific clustering effects owing to the heterogeneity as a result of significantly different physical properties of the components, or it may be the result of lower S/N for the more heterogeneous clusters and not including the low-abundance more highly heterogeneous clusters in this analysis. Although not as accurate as using traditional standards, this clustering method may find applications when suitable standards are not readily available.

A simple and robust method for determining the number of basic sites in peptides and proteins using electrospray ionization mass spectrometry

A solution additive has been discovered that can be used to measure the number of basic sites in a peptide or protein using electrospray ionization (ESI) mass spectrometry. Addition of millimolar amounts of perchloric acid (HClO(4)) to aqueous solutions that contain peptides or proteins results in the noncovalent adduction of HClO(4) molecules to the multiply charged ions formed by ESI. For 18 oligopeptides and proteins, ranging in molecular weight from 0.5 to 18.3 kDa, the sum of the number of protons plus maximum number of HClO(4) molecules adducted to the lower charge state ions is equal to the number of basic sites in the molecule. This method provides a rapid means of obtaining information about the composition of a peptide or protein and does not require high-resolution measurements or any instrumental or experimental modifications.