In-depth Characterization of the Cerebrospinal Fluid (CSF) Proteome Displayed Through the CSF Proteome Resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.Cerebrospinal fluid (CSF)¹ surrounds and supports the central nervous system (CNS), including the ventricles and subarachnoid space (1). About 80% of the total protein amount in CSF derives from size-dependent filtration of blood across the blood-brain barrier (BBB), and the rest originate from drainage of interstitial fluid from the CNS (2–4). Because CSF is in direct contact with the CNS, it should be a promising source for finding biomarkers for diseases in the CNS (5).Mapping studies characterizing the human CSF proteome and peptidome has previously been carried out using various experimental designs, including both healthy and disease-affected individuals (5–16). A total of 2630 proteins were detected in normal CSF by immunoaffinity depletion of high abundant proteins followed by strong cation exchange fractionation and LC-MS (5), whereas proteome and peptidome analyses of human CSF (collected for diagnostic purposes and turned out normal) by gel separation and trypsin digestion followed by LC-MS analysis have shown 798 proteins and 563 peptide products (derived from 91 precursor proteins) (6). In another publication, Pan et al. combined several proteomics studies in CSF from both normal subjects and subjects with neurological diseases and created a dataset of 2594 identified proteins (16). But in general, the availability and usefulness of published data from proteome mapping experiments is scarce, and the format of the data often makes searching and comparison across datasets difficult. Thus, organizing the data in online databases would greatly benefit the scientific community by making the data more accessible and easier to query. Current online databases containing MS data for CSF include the Sys-BodyFluid, with a total of 1286 CSF proteins from six studies (17). The proteome identifications database (PRIDE) (18) includes 19 studies on human CSF, but none reporting more than 103 identified proteins.Glycosylation is one of the most common post-translational modifications (PTMs), and many known clinical biomarkers as well as therapeutic targets are glycoproteins (19–25). Furthermore, glycosylation plays important roles in cell communication, signaling, aging, and cell adhesion (26, 27). Nevertheless, there are few studies on glycoprotein identification in CSF. One study identified 216 glycoproteins in CSF using both lectin affinity and hydrazide chemistry (8), and another reported 36 N-linked and 44 O-linked glycosylation sites, from 23 and 22 glycoproteins respectively, by enriching for sialic-acid containing glycopeptides (28).Considering the sparse information about the CSF proteome available in public repositories, we have combined several proteomics approaches to create a map of the global CSF proteome, the CSF glycoproteome, and the respective plasma proteome from a pool of 21 (20 for the plasma pool) neurologically healthy individuals. The large amount of data generated through these four datasets (with linked and complementary information) would not easily be accessible through existing repositories. We therefore developed the open access CSF Proteome Resource (CSF-PR, www.probe.uib.no/csf-pr), an online database including the detailed data from the four different proteomics experiments described in this study. CSF-PR will be particularly useful in guiding the selection of appropriate signature peptides for the development of targeted CSF protein assays.     

Proteomics of Microparticles with SILAC Quantification (PROMIS-Quan): A Novel Proteomic Method for Plasma Biomarker Quantification

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan—PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.Biomarker discovery in plasma is one of the holy grails of the proteomic field toward the development of noninvasive diagnostic/prognostic tests (1). To achieve this goal, proteomics necessitates a comprehensive view of the plasma proteome, accurate proteome quantification, combined with relatively short analytical times to enable multiple sample comparisons. However, MS-based biomarker discovery is limited by the vast dynamic range of the plasma, over 11 orders of magnitude (2, 3), which leads to the masking of “tissue leakage” proteins that comprise of potential biomarkers by the core plasma proteins. Two main complementary strategies have been employed to reach identification of low abundance proteins: (i) Targeted proteomics, in which the MS identifies and quantifies only predetermined peptides, thereby circumventing the system''s inherent tendency to preferentially detect abundant proteins. This approach is utilized for validation of preselected candidate markers (4–6). (ii) Plasma fractionation, which biochemically reduces the complexity of the proteomes, and enables discovery of novel biomarkers (7, 8).Targeted MS analysis is dominated by the selected reaction monitoring approach, often in combination with antibody-based enrichment of proteins or peptides and stable isotope labeled standards for quantification (9). This approach benefits from the sensitivity and quantitative capabilities of the triple-quadrupole instruments. Its major limitation is that it relies on prior discovery of candidates within the plasma samples using extensive tissue/cell-line-based analysis and prediction of potential biomarkers. The fractionation strategy reduces both the complexity and the dynamic range of the plasma through depletion of the most abundant plasma proteins, and/or through extensive biochemical separation of proteins and peptides. Although these fractionation approaches enabled identification of thousands of plasma proteins (7), they dramatically reduce the throughput of the method, and thus, the applicability to clinical studies.A distinct fractionation approach involves the isolation of plasma microparticles and exosomes. Microparticles are large vesicles (100 nm–1 μm), which protrude directly from the plasma membrane, whereas exosomes are smaller (40–100 nm) and originate from endocytic compartments known as the multivesicular endosomes. These microvesicles are constitutively shed from all cell types into the blood, carrying a proteomic signature of their cells of origin (10). Microparticles mediate local and systemic communication in various conditions, in particularly in cancer, where they can promote metastasis, immune evasion of cancer cells and angiogenesis (10–13), but also in other conditions including autoimmune diseases (14) and cardiovascular disorders (15). Therefore, circulating plasma microparticle proteomics can reveal biomarkers of various diseases as the basis for further diagnostic test development.The profiling of plasma microparticle proteomes initiated by Jin et al. in 2005, with the analysis of 16 samples using two-dimensional (2D)-gels followed by matrix assisted laser desorption ionization- time of flight (MALDI-TOF) MS analysis, which resulted in the identification of 83 proteins (16). In the following years, low resolution MS analysis of plasma microparticles reached up to 229 plasma microparticle proteins and high resolution MS analysis reached 458 proteins (all without false discovery rate (FDR)¹ correction)(17, 18). The latest and most comprehensive study of plasma microparticles proteome profiling was published in 2012 by Ostergaard et al., who analyzed 12 samples on the LTQ Orbitrap XL mass spectrometer and identified 536 proteins in total, after 1% FDR correction (19). Other studies have profiled the proteomes of microparticles and exosomes derived from various body fluids other than plasma, including urine (20), saliva (21), cerebral spinal fluid (22), breast milk (23), amniotic fluid (24), seminal fluid (25), and more. However, despite the dramatic reduction of the dynamic range of the analytes, so far it has not yet provided sufficient depth for biomarker discovery. Nevertheless, it has a good prospective for discovering biomarkers. For example, biochemical analysis of breast cancer patient leukocytes-derived microparticles correlated between increased tumor size and increased levels of carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3), two well-known prognostic markers for colon and breast cancer, respectively (26).Combining all of the plasma proteomics approaches mentioned above, several prominent surveys of the human plasma proteome have been reported. The first large-scale collaborative study was conducted by the Human Proteome Organization (HuPO) group, which collectively identified 3020 proteins (7). These were later condensed to a list of 889 nonredundant proteins, after taking into account multiple hypotheses control with at least 95% confidence in protein identification (27). The Peptide Atlas team initially combined 91 studies, including the one conducted by HuPO, and altogether produced a list of 1929 proteins (28). Recently this team has elaborated their survey by assembling 127 studies (29) and reached the largest high-confidence list published so far of overall 3677 plasma proteins.In the current work we applied state of the art proteomics to study the microparticle proteome and developed the PROteomics of MIcroparticles with Super-SILAC Quantification (PROMIS-Quan) method, which combines deep plasma microparticle coverage of more than 3200 proteins in a single run, with dual-mode relative and absolute Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) quantification. We demonstrated its utilization on samples of prostate cancer patients, and calculated the absolute amount of PSA, a well-known prostate cancer biomarker.     

Unbiased Quantitation of Escherichia coli Membrane Proteome Using Phase Transfer Surfactants

We developed a sample preparation protocol for rapid and unbiased analysis of the membrane proteome using an alimentary canal-mimicking system in which proteases are activated in the presence of bile salts. In this rapid and unbiased protocol, immobilized trypsin is used in the presence of deoxycholate and lauroylsarcosine to increase digestion efficiency as well as to increase the solubility of the membrane proteins. Using 22.5 μg of Escherichia coli whole cell lysate, we quantitatively demonstrated that membrane proteins were extracted and digested at the same level as soluble proteins without any solubility-related bias. The recovery of membrane proteins was independent of the number of transmembrane domains per protein. In the analysis of the membrane-enriched fraction from 22.5 μg of E. coli cell lysate, the abundance distribution of the membrane proteins was in agreement with that of the membrane protein-coding genes when this protocol, coupled with strong cation exchange prefractionation prior to nano-LC-MS/MS analysis, was used. Because this protocol allows unbiased sample preparation, protein abundance estimation based on the number of observed peptides per protein was applied to both soluble and membrane proteins simultaneously, and the copy numbers per cell for 1,453 E. coli proteins, including 545 membrane proteins, were successfully obtained. Finally, this protocol was applied to quantitative analysis of guanosine tetra- and pentaphosphate-dependent signaling in E. coli wild-type and relA knock-out strains.Despite the importance of cell surface biology, the conventional shotgun proteomics strategy generally underrepresents the membrane proteome because of inadequate solubilization and protease digestion (1, 2). The ageless gel strategy, consisting of SDS-PAGE followed by in-gel digestion, can partially solve this problem (3–5), but the recovery from in-gel digestion is generally lower than that from in-solution digestion, and this approach is far from suitable for a rapid, simple, and high throughput automated system. Numerous approaches have been reported to overcome the difficulties in membrane proteome analysis, such as the use of surfactants (2, 6–11), organic solvents (6, 7, 12–15), or chaotropic reagents (2, 6, 16). Acid-labile surfactants, such as RapiGest SF, are among the most promising additives to enhance protein solubilization without interfering with LC-MS performance (6, 10, 17–19). However, the cleavage step at acidic pH causes loss of hydrophobic peptides because of coprecipitation with the hydrophobic part of RapiGest SF (20). Recently, we developed a new protocol to dissolve and digest membrane proteins with the aid of a removable phase transfer surfactant (PTS),1 such as sodium deoxycholate (SDC) (20). The solubility of membrane proteins with SDC was comparable to that with sodium dodecyl sulfate. In addition, the activity of trypsin was enhanced ∼5-fold in the presence of 1% SDC because this rapid PTS method mimics conditions in the alimentary canal in which bile salts such as cholate and deoxycholate are secreted together with trypsin. After tryptic digestion, SDC is removed prior to LC-MS/MS analysis by adding an organic solvent followed by pH-induced transfer of the surfactant to the organic phase, whereas tryptic peptides remain in the aqueous phase. This protocol offers a significant improvement in identifying membrane proteins by increasing the recovery of hydrophobic tryptic peptides compared with the protocols using urea and RapiGest SF.The goal of this study is to establish a membrane proteomics method that is unbiased with respect to protein solubility, hydrophobicity, and protein abundance; i.e. membrane proteins can be as efficiently extracted and digested as soluble proteins. So far, to our knowledge, little information about the recovery of the membrane proteome has been reported. Instead, the number of identified membrane proteins or the content of membrane proteins identified in the membrane-enriched fraction has been used as an indicator of the efficiency of procedures for membrane proteome analysis (4, 5, 21–23). However, these parameters usually depend on the experimental conditions, including the sample preparation procedure and LC-MS instrument used. Therefore, it is difficult to compare data obtained with these protocols except in the case of direct comparison. Furthermore, there has been no report quantitatively comparing the recovery of membrane proteome with that of soluble proteins.In this study, we used a modified version of our PTS protocol with immobilized trypsin columns to reduce the digestion time and evaluated its suitability for unbiased quantitation of the membrane proteome. In addition, we applied this protocol to estimate the copy numbers per cell of 1,453 proteins, including 545 membrane proteins, using the exponentially modified protein abundance index (emPAI). Finally, this rapid and unbiased PTS protocol was applied to the quantitative analysis of Escherichia coli BW25113 wild-type and relA knock-out (KO) strains.     

Online Peptide Fractionation Using a Multiphasic Microfluidic Liquid Chromatography Chip Improves Reproducibility and Detection Limits for Quantitation in Discovery and Targeted Proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH^TM data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.Mass spectrometry based proteomic quantitation is an essential technique used for contemporary, integrative biological studies. Whether used in discovery experiments or for targeted biomarker applications, quantitative proteomic studies require high reproducibility at many levels. It requires reproducible run-to-run peptide detection, reproducible peptide quantitation, reproducible depth of proteome coverage, and ideally, a high degree of cross-laboratory analytical reproducibility. Mass spectrometry centered proteomics has evolved steadily over the past decade, now mature enough to derive extensive draft maps of the human proteome (1, 2). Nonetheless, a key requirement yet to be realized is to ensure that quantitative proteomics can be carried out in a timely manner while satisfying the aforementioned challenges associated with reproducibility. This is especially important for recent developments using data independent MS quantitation and multiple reaction monitoring on high-resolution MS (MRM-HR)¹ as they are both highly dependent on LC peptide retention time reproducibility and precursor detectability, while attempting to maximize proteome coverage (3). Strategies usually employed to increase the depth of proteome coverage utilize various sample fractionation methods including gel-based separation, affinity enrichment or depletion, protein or peptide chemical modification-based enrichment, and various peptide chromatography methods, particularly ion exchange chromatography (4–10). In comparison to an unfractionated “naive” sample, the trade-off in using these enrichments/fractionation approaches are higher risk of sample losses, introduction of undesired chemical modifications (e.g. oxidation, deamidation, N-terminal lactam formation), and the potential for result skewing and bias, as well as numerous time and human resources required to perform the sample preparation tasks. Online-coupled approaches aim to minimize those risks and address resource constraints. A widely practiced example of the benefits of online sample fractionation has been the decade long use of combining strong cation exchange chromatography (SCX) with C18 reversed-phase (RP) for peptide fractionation (known as MudPIT – multidimensional protein identification technology), where SCX and RP is performed under the same buffer conditions and the SCX elution performed with volatile organic cations compatible with reversed phase separation (11). This approach greatly increases analyte detection while avoiding sample handling losses. The MudPIT approach has been widely used for discovery proteomics (12–14), and we have previously shown that multiphasic separations also have utility for targeted proteomics when configured for selected reaction monitoring MS (SRM-MS). We showed substantial advantages of MudPIT-SRM-MS with reduced ion suppression, increased peak areas and lower limits of detection (LLOD) compared with conventional RP-SRM-MS (15).To improve the reproducibility of proteomic workflows, increase throughput and minimize sample loss, numerous microfluidic devices have been developed and integrated for proteomic applications (16, 17). These devices can broadly be classified into two groups: (1) microfluidic chips for peptide separation (18–25) and; (2) proteome reactors that combine enzymatic processing with peptide based fractionation (26–30). Because of the small dimension of these devices, they are readily able to integrate into nanoLC workflows. Various applications have been described including increasing proteome coverage (22, 27, 28) and targeting of phosphopeptides (24, 31, 32), glycopeptides and released glycans (29, 33, 34).In this work, we set out to take advantage of the benefits of multiphasic peptide separations and address the reproducibility needs required for high-throughput comparative proteomics using a variety of workflows. We integrated a multiphasic SCX and RP column in a “plug-and-play” microfluidic chip format for online fractionation, eliminating the need for users to make minimal dead volume connections between traps and columns. We show the flexibility of this format to provide robust peptide separation and reproducibility using conventional and topical mass spectrometry workflows. This was undertaken by coupling the multiphase liquid chromatography (LC) chip to a fast scanning Q-ToF mass spectrometer for data dependent MS/MS, data independent MS (SWATH) and for targeted proteomics using MRM-HR, showing clear advantages for repeatable analyses compared with conventional proteomic workflows.     

Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.Access to proteomics performance standards is essential for several reasons. First, to generate the highest quality data possible, proteomics laboratories routinely benchmark and perform quality control (QC)1 monitoring of the performance of their instrumentation using standards. Second, appropriate standards greatly facilitate the development of improvements in technologies by providing a timeless standard with which to evaluate new protocols or instruments that claim to improve performance. For example, it is common practice for an individual laboratory considering purchase of a new instrument to require the vendor to run “demo” samples so that data from the new instrument can be compared head to head with existing instruments in the laboratory. Third, large scale proteomics studies designed to aggregate data across laboratories can be facilitated by the use of a performance standard to measure reproducibility across sites or to compare the performance of different LC-MS configurations or sample processing protocols used between laboratories to facilitate development of optimized standard operating procedures (SOPs).Most individual laboratories have adopted their own QC standards, which range from mixtures of known synthetic peptides to digests of bovine serum albumin or more complex mixtures of several recombinant proteins (1). However, because each laboratory performs QC monitoring in isolation, it is difficult to compare the performance of LC-MS platforms throughout the community.Several standards for proteomics are available for request or purchase (2, 3). RM8327 is a mixture of three peptides developed as a reference material in collaboration between the National Institute of Standards and Technology (NIST) and the Association of Biomolecular Resource Facilities. Mixtures of 15–48 purified human proteins are also available, such as the HUPO (Human Proteome Organisation) Gold MS Protein Standard (Invitrogen), the Universal Proteomics Standard (UPS1; Sigma), and CRM470 from the European Union Institute for Reference Materials and Measurements. Although defined mixtures of peptides or proteins can address some benchmarking and QC needs, there is an additional need for more complex reference materials to fully represent the challenges of LC-MS data acquisition in complex matrices encountered in biological samples (2, 3).Although it has not been widely distributed as a reference material, the yeast Saccharomyces cerevisiae proteome has been extensively used by the proteomics community to characterize the capabilities of a variety of LC-MS-based approaches (4–15). Yeast provides a uniquely attractive complex performance standard for several reasons. Yeast encodes a complex proteome consisting of ∼4,500 proteins expressed during normal growth conditions (7, 16–18). The concentration range of yeast proteins is sufficient to challenge the dynamic range of conventional mass spectrometers; the abundance of proteins ranges from fewer than 50 to more than 10⁶ molecules per cell (4, 15, 16). Additionally, it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins (5, 9, 16, 17, 19, 20) as well as LC-MS/MS data sets showing good correlation between LC-MS/MS detection efficiency and the protein abundance estimates (4, 11, 12, 15). Finally, it is inexpensive and easy to produce large quantities of yeast protein extract for distribution.In this study, we describe large scale production of a yeast S. cerevisiae performance standard, which we offer to the community through NIST. Through a series of interlaboratory studies, we created a reference data set characterizing the yeast performance standard and defining reasonable performance of ion trap-based LC-MS platforms in expert laboratories using a series of performance metrics. This publicly available data set provides a basis for additional laboratories using the yeast standard to benchmark their own performance as well as to improve upon the current status by evolving protocols, improving instrumentation, or developing new technologies. Finally, we demonstrate how the yeast performance standard, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix.     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

Large-scale Top-down Proteomics of the Human Proteome: Membrane Proteins,Mitochondria, and Senescence

Top-down proteomics is emerging as a viable method for the routine identification of hundreds to thousands of proteins. In this work we report the largest top-down study to date, with the identification of 1,220 proteins from the transformed human cell line H1299 at a false discovery rate of 1%. Multiple separation strategies were utilized, including the focused isolation of mitochondria, resulting in significantly improved proteome coverage relative to previous work. In all, 347 mitochondrial proteins were identified, including ∼50% of the mitochondrial proteome below 30 kDa and over 75% of the subunits constituting the large complexes of oxidative phosphorylation. Three hundred of the identified proteins were found to be integral membrane proteins containing between 1 and 12 transmembrane helices, requiring no specific enrichment or modified LC-MS parameters. Over 5,000 proteoforms were observed, many harboring post-translational modifications, including over a dozen proteins containing lipid anchors (some previously unknown) and many others with phosphorylation and methylation modifications. Comparison between untreated and senescent H1299 cells revealed several changes to the proteome, including the hyperphosphorylation of HMGA2. This work illustrates the burgeoning ability of top-down proteomics to characterize large numbers of intact proteoforms in a high-throughput fashion.Although traditional bottom-up approaches to mass-spectrometry-based proteomics are capable of identifying thousands of protein groups from a complex mixture, proteolytic digestion can result in the loss of information pertaining to post-translational modifications and sequence variants (1, 2). The recent implementation of top-down proteomics in a high-throughput format using either Fourier transform ion cyclotron resonance (3–5) or Orbitrap instruments (6, 7) has shown an increasing scale of applicability while preserving information on combinatorial modifications and highly related sequence variants. For example, the identification of over 500 bacterial proteins helped researchers find covalent switches on cysteines (7), and over 1,000 proteins were identified from human cells (3). Such advances have driven the detection of whole protein forms, now simply called proteoforms (8), with several laboratories now seeking to tie these to specific functions in cell and disease biology (9–11).The term “proteoform” denotes a specific primary structure of an intact protein molecule that arises from a specific gene and refers to a precise combination of genetic variation, splice variants, and post-translational modifications. Whereas special attention is required in order to accomplish gene- and variant-specific identifications via the bottom-up approach, top-down proteomics routinely links proteins to specific genes without the problem of protein inference. However, the fully automated characterization of whole proteoforms still represents a significant challenge in the field. Another major challenge is to extend the top-down approach to the study of whole integral membrane proteins, whose hydrophobicity can often limit their analysis via LC-MS (5, 12–16). Though integral membrane proteins are often difficult to solubilize, the long stretches of sequence information provided from fragmentation of their transmembrane domains in the gas phase can actually aid in their identification (5, 13).In parallel to the early days of bottom-up proteomics a decade ago (17–21), in this work we brought the latest methods for top-down proteomics into combination with subcellular fractionation and cellular treatments to expand coverage of the human proteome. We utilized multiple dimensions of separation and an Orbitrap Elite mass spectrometer to achieve large-scale interrogation of intact proteins derived from H1299 cells. For this focus issue on post-translational modifications, we report this summary of findings from the largest implementation of top-down proteomics to date, which resulted in the identification of 1,220 proteins and thousands more proteoforms. We also applied the platform to H1299 cells induced into senescence by treatment with the DNA-damaging agent camptothecin.     

A New in Vivo Cross-linking Mass Spectrometry Platform to Define Protein–Protein Interactions in Living Cells

Protein–protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.Protein–protein interactions (PPIs)¹ play a key role in defining protein functions in biological systems. Aberrant PPIs can have drastic effects on biochemical activities essential to cell homeostasis, growth, and proliferation, and thereby lead to various human diseases (1). Consequently, PPI interfaces have been recognized as a new paradigm for drug development. Therefore, mapping PPIs and their interaction interfaces in living cells is critical not only for a comprehensive understanding of protein function and regulation, but also for describing the molecular mechanisms underlying human pathologies and identifying potential targets for better therapeutics.Several strategies exist for identifying and mapping PPIs, including yeast two-hybrid, protein microarray, and affinity purification mass spectrometry (AP-MS) (2–5). Thanks to new developments in sample preparation strategies, mass spectrometry technologies, and bioinformatics tools, AP-MS has become a powerful and preferred method for studying PPIs at the systems level (6–9). Unlike other approaches, AP-MS experiments allow the capture of protein interactions directly from their natural cellular environment, thus better retaining native protein structures and biologically relevant interactions. In addition, a broader scope of PPI networks can be obtained with greater sensitivity, accuracy, versatility, and speed. Despite the success of this very promising technique, AP-MS experiments can lead to the loss of weak/transient interactions and/or the reorganization of protein interactions during biochemical manipulation under native purification conditions. To circumvent these problems, in vivo chemical cross-linking has been successfully employed to stabilize protein interactions in native cells or tissues prior to cell lysis (10–16). The resulting covalent bonds formed between interacting partners allow affinity purification under stringent and fully denaturing conditions, consequently reducing nonspecific background while preserving stable and weak/transient interactions (12–16). Subsequent mass spectrometric analysis can reveal not only the identities of interacting proteins, but also cross-linked amino acid residues. The latter provides direct molecular evidence describing the physical contacts between and within proteins (17). This information can be used for computational modeling to establish structural topologies of proteins and protein complexes (17–22), as well as for generating experimentally derived protein interaction network topology maps (23, 24). Thus, cross-linking mass spectrometry (XL-MS) strategies represent a powerful and emergent technology that possesses unparalleled capabilities for studying PPIs.Despite their great potential, current XL-MS studies that have aimed to identify cross-linked peptides have been mostly limited to in vitro cross-linking experiments, with few successfully identifying protein interaction interfaces in living cells (24, 25). This is largely because XL-MS studies remain challenging due to the inherent difficulty in the effective MS detection and accurate identification of cross-linked peptides, as well as in unambiguous assignment of cross-linked residues. In general, cross-linked products are heterogeneous and low in abundance relative to non-cross-linked products. In addition, their MS fragmentation is too complex to be interpreted using conventional database searching tools (17, 26). It is noted that almost all of the current in vivo PPI studies utilize formaldehyde cross-linking because of its membrane permeability and fast kinetics (10–16). However, in comparison to the most commonly used amine reactive NHS ester cross-linkers, identification of formaldehyde cross-linked peptides is even more challenging because of its promiscuous nonspecific reactivity and extremely short spacer length (27). Therefore, further developments in reagents and methods are urgently needed to enable simple MS detection and effective identification of in vivo cross-linked products, and thus allow the mapping of authentic protein contact sites as established in cells, especially for protein complexes.Various efforts have been made to address the limitations of XL-MS studies, resulting in new developments in bioinformatics tools for improved data interpretation (28–32) and new designs of cross-linking reagents for enhanced MS analysis of cross-linked peptides (24, 33–39). Among these approaches, the development of new cross-linking reagents holds great promise for mapping PPIs on the systems level. One class of cross-linking reagents containing an enrichment handle have been shown to allow selective isolation of cross-linked products from complex mixtures, boosting their detectability by MS (33–35, 40–42). A second class of cross-linkers containing MS-cleavable bonds have proven to be effective in facilitating the unambiguous identification of cross-linked peptides (36–39, 43, 44), as the resulting cross-linked products can be identified based on their characteristic and simplified fragmentation behavior during MS analysis. Therefore, an ideal cross-linking reagent would possess the combined features of both classes of cross-linkers. To advance the study of in vivo PPIs, we have developed a new XL-MS platform based on a novel membrane-permeable, enrichable, and MS-cleavable cross-linker, Azide-A-DSBSO (azide-tagged, acid-cleavable disuccinimidyl bis-sulfoxide), and multistage tandem mass spectrometry (MSⁿ). This new XL-MS strategy has been successfully employed to map in vivo PPIs from mammalian cells at both the proteome scale and the targeted protein complex level.     

Phosphoproteome Analysis of Functional Mitochondria Isolated from Resting Human Muscle Reveals Extensive Phosphorylation of Inner Membrane Protein Complexes and Enzymes

Mitochondria play a central role in energy metabolism and cellular survival, and consequently mitochondrial dysfunction is associated with a number of human pathologies. Reversible protein phosphorylation emerges as a central mechanism in the regulation of several mitochondrial processes. In skeletal muscle, mitochondrial dysfunction is linked to insulin resistance in humans with obesity and type 2 diabetes. We performed a phosphoproteomics study of functional mitochondria isolated from human muscle biopsies with the aim to obtain a comprehensive overview of mitochondrial phosphoproteins. Combining an efficient mitochondrial isolation protocol with several different phosphopeptide enrichment techniques and LC-MS/MS, we identified 155 distinct phosphorylation sites in 77 mitochondrial phosphoproteins, including 116 phosphoserine, 23 phosphothreonine, and 16 phosphotyrosine residues. The relatively high number of phosphotyrosine residues suggests an important role for tyrosine phosphorylation in mitochondrial signaling. Many of the mitochondrial phosphoproteins are involved in oxidative phosphorylation, tricarboxylic acid cycle, and lipid metabolism, i.e. processes proposed to be involved in insulin resistance. We also assigned phosphorylation sites in mitochondrial proteins involved in amino acid degradation, importers and transporters, calcium homeostasis, and apoptosis. Bioinformatics analysis of kinase motifs revealed that many of these mitochondrial phosphoproteins are substrates for protein kinase A, protein kinase C, casein kinase II, and DNA-dependent protein kinase. Our results demonstrate the feasibility of performing phosphoproteome analysis of organelles isolated from human tissue and provide novel targets for functional studies of reversible phosphorylation in mitochondria. Future comparative phosphoproteome analysis of mitochondria from healthy and diseased individuals will provide insights into the role of abnormal phosphorylation in pathologies, such as type 2 diabetes.Mitochondria are the primary energy-generating systems in eukaryotes. They play a crucial role in oxidative metabolism, including carbohydrate metabolism, fatty acid oxidation, and urea cycle, as well as in calcium signaling and apoptosis (1, 2). Mitochondrial dysfunction is centrally involved in a number of human pathologies, such as type 2 diabetes, Parkinson disease, and cancer (3). The most prevalent form of cellular protein post-translational modifications (PTMs),1 reversible phosphorylation (4–6), is emerging as a central mechanism in the regulation of mitochondrial functions (7, 8). The steadily increasing numbers of reported mitochondrial kinases, phosphatases, and phosphoproteins imply an important role of protein phosphorylation in different mitochondrial processes (9–11).Mass spectrometry (MS)-based proteome analysis is a powerful tool for global profiling of proteins and their PTMs, including protein phosphorylation (12, 13). A variety of proteomics techniques have been developed for specific enrichment of phosphorylated proteins and peptides and for phosphopeptide-specific data acquisition techniques at the MS level (14). Enrichment methods based on affinity chromatography, such as titanium dioxide (TiO₂) (15–17), zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) (18), immobilized metal affinity chromatography (IMAC) (19, 20), and ion exchange chromatography (strong anion exchange and strong cation exchange) (21, 22), have shown high efficiencies for enrichment of phosphopeptides (14). Recently, we demonstrated that calcium phosphate precipitation (CPP) is highly effective for enriching phosphopeptides (23). It is now generally accepted that no single method is comprehensive, but combinations of different enrichment methods produce distinct overlapping phosphopeptide data sets to enhance the overall results in phosphoproteome analysis (24, 25). Phosphopeptide sequencing by mass spectrometry has seen tremendous advances during the last decade (26). For example, MS/MS product ion scanning, multistage activation, and precursor ion scanning are effective methods for identifying serine (Ser), threonine (Thr), and tyrosine (Tyr) phosphorylated peptides (14, 26).A “complete” mammalian mitochondrial proteome was reported by Mootha and co-workers (27) and included 1098 proteins. The mitochondrial phosphoproteome has been characterized in a series of studies, including yeast, mouse and rat liver, porcine heart, and plants (19, 28–31). To date, the largest data set by Deng et al. (30) identified 228 different phosphoproteins and 447 phosphorylation sites in rat liver mitochondria. However, the in vivo phosphoproteome of human mitochondria has not been determined. A comprehensive mitochondrial phosphoproteome is warranted for further elucidation of the largely unknown mechanisms by which protein phosphorylation modulates diverse mitochondrial functions.The percutaneous muscle biopsy technique is an important tool in the diagnosis and management of human muscle disorders and has been widely used to investigate metabolism and various cellular and molecular processes in normal and abnormal human muscle, in particular the molecular mechanism underlying insulin resistance in obesity and type 2 diabetes (32). Skeletal muscle is rich in mitochondria and hence a good source for a comprehensive proteomics and functional analysis of mitochondria (32, 33).The major aim of the present study was to obtain a comprehensive overview of site-specific phosphorylation of mitochondrial proteins in functionally intact mitochondria isolated from human skeletal muscle. Combining an efficient protocol for isolation of skeletal muscle mitochondria with several different state-of-the-art phosphopeptide enrichment methods and high performance LC-MS/MS, we identified 155 distinct phosphorylation sites in 77 mitochondrial phosphoproteins, many of which have not been reported before. We characterized this mitochondrial phosphoproteome by using bioinformatics tools to classify functional groups and functions, including kinase substrate motifs.     

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

Development of a Novel Cross-linking Strategy for Fast and Accurate Identification of Cross-linked Peptides of Protein Complexes

Knowledge of elaborate structures of protein complexes is fundamental for understanding their functions and regulations. Although cross-linking coupled with mass spectrometry (MS) has been presented as a feasible strategy for structural elucidation of large multisubunit protein complexes, this method has proven challenging because of technical difficulties in unambiguous identification of cross-linked peptides and determination of cross-linked sites by MS analysis. In this work, we developed a novel cross-linking strategy using a newly designed MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). DSSO contains two symmetric collision-induced dissociation (CID)-cleavable sites that allow effective identification of DSSO-cross-linked peptides based on their distinct fragmentation patterns unique to cross-linking types (i.e. interlink, intralink, and dead end). The CID-induced separation of interlinked peptides in MS/MS permits MS³ analysis of single peptide chain fragment ions with defined modifications (due to DSSO remnants) for easy interpretation and unambiguous identification using existing database searching tools. Integration of data analyses from three generated data sets (MS, MS/MS, and MS³) allows high confidence identification of DSSO cross-linked peptides. The efficacy of the newly developed DSSO-based cross-linking strategy was demonstrated using model peptides and proteins. In addition, this method was successfully used for structural characterization of the yeast 20 S proteasome complex. In total, 13 non-redundant interlinked peptides of the 20 S proteasome were identified, representing the first application of an MS-cleavable cross-linker for the characterization of a multisubunit protein complex. Given its effectiveness and simplicity, this cross-linking strategy can find a broad range of applications in elucidating the structural topology of proteins and protein complexes.Proteins form stable and dynamic multisubunit complexes under different physiological conditions to maintain cell viability and normal cell homeostasis. Detailed knowledge of protein interactions and protein complex structures is fundamental to understanding how individual proteins function within a complex and how the complex functions as a whole. However, structural elucidation of large multisubunit protein complexes has been difficult because of a lack of technologies that can effectively handle their dynamic and heterogeneous nature. Traditional methods such as nuclear magnetic resonance (NMR) analysis and x-ray crystallography can yield detailed information on protein structures; however, NMR spectroscopy requires large quantities of pure protein in a specific solvent, whereas x-ray crystallography is often limited by the crystallization process.In recent years, chemical cross-linking coupled with mass spectrometry (MS) has become a powerful method for studying protein interactions (1–3). Chemical cross-linking stabilizes protein interactions through the formation of covalent bonds and allows the detection of stable, weak, and/or transient protein-protein interactions in native cells or tissues (4–9). In addition to capturing protein interacting partners, many studies have shown that chemical cross-linking can yield low resolution structural information about the constraints within a molecule (2, 3, 10) or protein complex (11–13). The application of chemical cross-linking, enzymatic digestion, and subsequent mass spectrometric and computational analyses for the elucidation of three-dimensional protein structures offers distinct advantages over traditional methods because of its speed, sensitivity, and versatility. Identification of cross-linked peptides provides distance constraints that aid in constructing the structural topology of proteins and/or protein complexes. Although this approach has been successful, effective detection and accurate identification of cross-linked peptides as well as unambiguous assignment of cross-linked sites remain extremely challenging due to their low abundance and complicated fragmentation behavior in MS analysis (2, 3, 10, 14). Therefore, new reagents and methods are urgently needed to allow unambiguous identification of cross-linked products and to improve the speed and accuracy of data analysis to facilitate its application in structural elucidation of large protein complexes.A number of approaches have been developed to facilitate MS detection of low abundance cross-linked peptides from complex mixtures. These include selective enrichment using affinity purification with biotinylated cross-linkers (15–17) and click chemistry with alkyne-tagged (18) or azide-tagged (19, 20) cross-linkers. In addition, Staudinger ligation has recently been shown to be effective for selective enrichment of azide-tagged cross-linked peptides (21). Apart from enrichment, detection of cross-linked peptides can be achieved by isotope-labeled (22–24), fluorescently labeled (25), and mass tag-labeled cross-linking reagents (16, 26). These methods can identify cross-linked peptides with MS analysis, but interpretation of the data generated from interlinked peptides (two peptides connected with the cross-link) by automated database searching remains difficult. Several bioinformatics tools have thus been developed to interpret MS/MS data and determine interlinked peptide sequences from complex mixtures (12, 14, 27–32). Although promising, further developments are still needed to make such data analyses as robust and reliable as analyzing MS/MS data of single peptide sequences using existing database searching tools (e.g. Protein Prospector, Mascot, or SEQUEST).Various types of cleavable cross-linkers with distinct chemical properties have been developed to facilitate MS identification and characterization of cross-linked peptides. These include UV photocleavable (33), chemical cleavable (19), isotopically coded cleavable (24), and MS-cleavable reagents (16, 26, 34–38). MS-cleavable cross-linkers have received considerable attention because the resulting cross-linked products can be identified based on their characteristic fragmentation behavior observed during MS analysis. Gas-phase cleavage sites result in the detection of a “reporter” ion (26), single peptide chain fragment ions (35–38), or both reporter and fragment ions (16, 34). In each case, further structural characterization of the peptide product ions generated during the cleavage reaction can be accomplished by subsequent MSⁿ1 analysis. Among these linkers, the “fixed charge” sulfonium ion-containing cross-linker developed by Lu et al. (37) appears to be the most attractive as it allows specific and selective fragmentation of cross-linked peptides regardless of their charge and amino acid composition based on their studies with model peptides.Despite the availability of multiple types of cleavable cross-linkers, most of the applications have been limited to the study of model peptides and single proteins. Additionally, complicated synthesis and fragmentation patterns have impeded most of the known MS-cleavable cross-linkers from wide adaptation by the community. Here we describe the design and characterization of a novel and simple MS-cleavable cross-linker, DSSO, and its application to model peptides and proteins and the yeast 20 S proteasome complex. In combination with new software developed for data integration, we were able to identify DSSO-cross-linked peptides from complex peptide mixtures with speed and accuracy. Given its effectiveness and simplicity, we anticipate a broader application of this MS-cleavable cross-linker in the study of structural topology of other protein complexes using cross-linking and mass spectrometry.     

Sampling From the Proteome to the Human Leukocyte Antigen-DR (HLA-DR) Ligandome Proceeds Via High Specificity

Comprehensive analysis of the complex nature of the Human Leukocyte Antigen (HLA) class II ligandome is of utmost importance to understand the basis for CD4⁺ T cell mediated immunity and tolerance. Here, we implemented important improvements in the analysis of the repertoire of HLA-DR-presented peptides, using hybrid mass spectrometry-based peptide fragmentation techniques on a ligandome sample isolated from matured human monocyte-derived dendritic cells (DC). The reported data set constitutes nearly 14 thousand unique high-confident peptides, i.e. the largest single inventory of human DC derived HLA-DR ligands to date. From a technical viewpoint the most prominent finding is that no single peptide fragmentation technique could elucidate the majority of HLA-DR ligands, because of the wide range of physical chemical properties displayed by the HLA-DR ligandome. Our in-depth profiling allowed us to reveal a strikingly poor correlation between the source proteins identified in the HLA class II ligandome and the DC cellular proteome. Important selective sieving from the sampled proteome to the ligandome was evidenced by specificity in the sequences of the core regions both at their N- and C- termini, hence not only reflecting binding motifs but also dominant protease activity associated to the endolysosomal compartments. Moreover, we demonstrate that the HLA-DR ligandome reflects a surface representation of cell-compartments specific for biological events linked to the maturation of monocytes into antigen presenting cells. Our results present new perspectives into the complex nature of the HLA class II system and will aid future immunological studies in characterizing the full breadth of potential CD4⁺ T cell epitopes relevant in health and disease.Human Leukocyte Antigen (HLA)¹ class II molecules on professional antigen presenting cells such as dendritic cells (DC) expose peptide fragments derived from exogenous and endogenous proteins to be screened by CD4⁺ T cells (1, 2). The activation and recruitment of CD4⁺ T cells recognizing disease-related peptide antigens is critical for the development of efficient antipathogen or antitumor immunity. Furthermore, the presentation of self-peptides and their interaction with CD4⁺ T cells is essential to maintain immunological tolerance and homeostasis (3). Knowledge of the nature of HLA class II-presented peptides on DC is of great importance to understand the rules of antigen processing and peptide binding motifs (4), whereas the identity of disease-related antigens may provide new knowledge on immunogenicity and leads for the development of vaccines and immunotherapy (5, 6).Mass spectrometry (MS) has proven effective for the analysis HLA class II-presented peptides (4, 7, 8). MS-based ligandome studies have demonstrated that HLA class II molecules predominantly present peptides derived from exogenous proteins that entered the cells by endocytosis and endogenous proteins that are associated with the endo-lysosomal compartments (4). Yet proteins residing in the cytosol, nucleus or mitochondria can also be presented by HLA class II molecules, primarily through autophagy (9–11). Multiple studies have mapped the HLA class II ligandome of antigen presenting cells in the context of infectious pathogens (12), autoimmune diseases (13–17) or cancer (14, 18, 19), or those that are essential for self-tolerance in the human thymus (3, 20). Notwithstanding these efforts, and certainly not in line with the extensive knowledge on the HLA class I ligandome (21), the nature of the HLA class II-presented peptide repertoire and particular its relationship to the cellular source proteome remains poorly understood.To advance our knowledge on the HLA-DR ligandome on activated DC without having to deal with limitations in cell yield from peripheral human blood (12, 21, 22) or tissue isolates (3), we explored the use of MUTZ-3 cells. This cell line has been used as a model of human monocyte-derived DCs. MUTZ-3 cells can be matured to act as antigen presenting cells and express then high levels of HLA class II molecules, and can be propagated in vitro to large cell densities (23–25). We also evaluated the performance of complementary and hybrid MS fragmentation techniques electron-transfer dissociation (ETD), electron-transfer/higher-energy collision dissociation (EThcD) (26), and higher-energy collision dissociation (HCD) to sequence and identify the HLA class II ligandome. Together this workflow allowed for the identification of an unprecedented large set of about 14 thousand unique peptide sequences presented by DC derived HLA-DR molecules, providing an in-depth view of the complexity of the HLA class II ligandome, revealing underlying features of antigen processing and surface-presentation to CD4⁺ T cells.