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Mass spectrometry (MS)-based quantitation of plasma proteomes is challenging due to the extremely wide dynamic range and molecular heterogeneity of plasma samples. However, recent advances in technology, MS instrumentation, and bioinformatics have enabled in-depth quantitative analyses of very complex proteomes, including plasma. Specifically, recent improvements in both label-based and label-free quantitation strategies have allowed highly accurate quantitative comparisons of expansive proteome datasets. Here we present a method for in-depth label-free analysis of human plasma samples using MaxQuant.

One strategy for improving the throughput of human plasma proteomic discovery analysis while maintaining good depth of analysis is to multiplex using isobaric tags. At present, the greatest multiplexing that is commercially available uses the TMT10plex kit. As an example of this approach, we describe efficient shotgun discovery proteomics of large numbers of human plasma to identify potential biomarkers. In the analysis strategy, a common pooled reference was used to enable comparisons across multiple experiments. Duplicate samples showed excellent overall reproducibility across different TMT experiments. Data filters that improved the quality of individual peptide and protein quantitation included using a filter for purity of the targeted precursor ion in the isolation window and using only unique peptides.

Discovery and validation of plasma biomarkers are quite challenging because of the high complexity and wide dynamic range of the plasma proteome. Current plasma protein profiling strategies usually use major protein immunodepletion and nanoLC-MS/MS as the first and final analytical steps, respectively, but additional fractionation is needed to detect and quantify low-abundance disease biomarkers. In this study, the performances of 1-D SDS-PAGE, peptide isoelectrofocusing, and peptide high pH reverse-phase chromatography for fractionation of immunodepleted human plasma were systematically compared by evaluating protein coverage, peptide resolution, and capacity to detect known low-abundance proteins. Trade-offs between increasing the number of fractions to improve proteome coverage and resulting decreases in throughput also were assessed. High pH reverse-phase HPLC exhibited the highest peptide resolution and yielded the best depth of analysis with detection of the largest number of known low-abundance proteins for a given level of fractionation. Another advantage of using high pH reverse-phase fractionation rather than 1-D SDS gels is that all fractionation steps except for abundant protein depletion occur at the peptide level, making this strategy more compatible with quantitative biomarker validation methods such as stable isotope dilution multiple reaction monitoring.

Proteomics discovery of novel cancer serum biomarkers is hindered by the great complexity of serum, patient-to-patient variability, and triggering by the tumor of an acute-phase inflammatory reaction. This host response alters many serum protein levels in cancer patients, but these changes have low specificity as they can be triggered by diverse causes. We addressed these hurdles by utilizing a xenograft mouse model coupled with an in-depth 4-D protein profiling method to identify human proteins in the mouse serum. This strategy ensures that identified putative biomarkers are shed by the tumor, and detection of low-abundance proteins shed by the tumor is enhanced because the mouse blood volume is more than a thousand times smaller than that of a human. Using TOV-112D ovarian tumors, more than 200 human proteins were identified in the mouse serum, including novel candidate biomarkers and proteins previously reported to be elevated in either ovarian tumors or the blood of ovarian cancer patients. Subsequent quantitation of selected putative biomarkers in human sera using label-free multiple reaction monitoring (MRM) mass spectrometry (MS) showed that chloride intracellular channel 1, the mature form of cathepsin D, and peroxiredoxin 6 were elevated significantly in sera from ovarian carcinoma patients.

Detection of biologically interesting, low-abundance proteins in complex proteomes such as serum typically requires extensive fractionation and high-performance mass spectrometers. Processing of the resulting large data sets involves trade-offs between confidence of identification and depth of protein coverage; that is, higher stringency filters preferentially reduce the number of low-abundance proteins identified. In the current study, an alternative database search and results filtering strategies were evaluated using test samples ranging from purified proteins to ovarian tumor secretomes and human serum to maximize peptide and protein coverage. Full and partial tryptic searches were compared because substantial numbers of partial tryptic peptides were observed in all samples, and the proportion of partial tryptic peptides was particularly high for serum. When data filters that yielded similar false discovery rates (FDR) were used, full tryptic searches detected far fewer peptides than partial tryptic searches. In contrast to the common practice of using full tryptic specificity and a narrow precursor mass tolerance, more proteins and peptides could be confidently identified using a partial tryptic database search with a 100 ppm precursor mass tolerance followed by filtering of results using 10 ppm mass error and full tryptic boundaries.

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS) has emerged as a promising platform for verification of serological candidate biomarkers. However, cost and time needed to synthesize and evaluate stable isotope peptides, optimize spike-in assays, and generate standard curves quickly becomes unattractive when testing many candidate biomarkers. In this study, we demonstrate that label-free multiplexed MRM-MS coupled with major protein depletion and 1D gel separation is a time-efficient, cost-effective initial biomarker verification strategy requiring less than 100 μL of serum. Furthermore, SDS gel fractionation can resolve different molecular weight forms of targeted proteins with potential diagnostic value. Because fractionation is at the protein level, consistency of peptide quantitation profiles across fractions permits rapid detection of quantitation problems for specific peptides from a given protein. Despite the lack of internal standards, the entire workflow can be highly reproducible, and long-term reproducibility of relative protein abundance can be obtained using different mass spectrometers and LC methods with external reference standards. Quantitation down to ~200 pg/mL could be achieved using this workflow. Hence, the label-free GeLC-MRM workflow enables rapid, sensitive, and economical initial screening of large numbers of candidate biomarkers prior to setting up SID-MRM assays or immunoassays for the most promising candidate biomarkers.

Ectopic pregnancy (EP) and normal intrauterine pregnancy (IUP) serum proteomes were quantitatively compared to systematically identify candidate biomarkers. A 3-D biomarker discovery strategy consisting of abundant protein immunodepletion, SDS gels, LC-MS/MS, and label-free quantitation of MS signal intensities identified 70 candidate biomarkers with differences between groups greater than 2.5-fold. Further statistical analyses of peptide quantities were used to select the most promising 12 biomarkers for further study, which included known EP biomarkers, novel EP biomarkers (ADAM12 and ISM2), and five specific isoforms of the pregnancy specific beta-1-glycoprotein family. Technical replicates showed good reproducibility and protein intensities from the label-free discovery analysis compared favorably with reported abundance levels of several known reference serum proteins over at least 3 orders of magnitude. Similarly, relative abundances of candidate biomarkers from the label-free discovery analysis were consistent with relative abundances from pilot validation assays performed for five of the 12 most promising biomarkers using label-free multiple reaction monitoring of both the patient serum pools used for discovery and the individual samples that constituted these pools. These results demonstrate robust, reproducible, in-depth 3-D serum proteome discovery, and subsequent pilot-scale validation studies can be achieved readily using label-free quantitation strategies.

Comprehensive proteomic analysis of human plasma or serum has been a major strategy used to identify biomarkers that serve as indicators of disease. However, such in-depth proteomic analyses are challenging due to the complexity and extremely large dynamic range of protein concentrations in plasma. Therefore, reduction in sample complexity through multidimensional pre-fractionation strategies is critical, particularly for the detection of low-abundance proteins that have the potential to be the most specific disease biomarkers. We describe here a 4D protein profiling method that we developed for comprehensive proteomic analyses of both plasma and serum. Our method consists of abundant protein depletion coupled with separation strategies - microscale solution isoelectrofocusing and 1D SDS-PAGE - followed by reversed-phase separation of tryptic peptides prior to LC-MS/MS. Using this profiling strategy, we routinely identify a large number of proteins over nine orders of magnitude, including a substantial number of proteins at the low ng/mL or lower levels from approximately 300 μL of plasma sample.

Dematin and adducin are actin-binding proteins located at the spectrin-actin junctions, also called the junctional complex, in the erythrocyte membrane. Here we propose a new model whereby dematin and adducin link the junctional complex to human erythrocyte plasma membrane. Using a combination of surface labeling, immunoprecipitation, and vesicle proteomics approaches, we have identified glucose transporter-1 as the receptor for dematin and adducin in the human erythrocyte membrane. This finding is the first description of a transmembrane protein that binds to dematin and adducin, thus providing a rationale for the attachment of the junctional complex to the lipid bilayer. Because homologues of dematin, adducin, and glucose transporter-1 exist in many non-erythroid cells, we propose that a conserved mechanism may exist that couples sugar and other related transporters to the actin cytoskeleton.

The plasma proteome has a wide dynamic range of protein concentrations and is dominated by a few highly abundant proteins. Discovery of novel cancer biomarkers using proteomics is particularly challenging because specific biomarkers are expected to be low abundance proteins with normal blood concentrations of low nanograms per milliliter or less. Conventional, one- and two-dimensional proteomic methods including 2D PAGE, 2D DIGE, LC-MS/MS, and LC/LC-MS/MS do not have the capacity to consistently detect many proteins in this range. In contrast, new higher dimensional (Hi-D) separation strategies, utilizing more than two dimensions of fractionation, can profile the low abundance proteome.

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

This unit focuses on several related methods for profiling and identifying proteins in human serum or plasma. While the methods are specifically described for human serum and plasma, with the possible exception of the major protein depletion steps, they can be readily adapted to serum and plasma from other species as well as to other biological fluids. A very powerful multidimensional protein-profiling method is described that utilizes major protein depletion coupled to two other protein separations, MicroSol IEF fractionation and SDS-PAGE, followed by LC-MS/MS. Other protein-profiling strategies include the depletion of abundant proteins followed by analysis either via 2-D gels or using offline Multidimensional Protein Identification Technology (MudPIT) or LC/LC-MS/MS. Additional protein profiling methods and strategies are also discussed.

A novel strategy, termed protein array pixelation, is described for comprehensive profiling of human plasma and serum proteomes. This strategy consists of three sequential high-resolution protein prefractionation methods (major protein depletion, solution isoelectrofocusing, and 1-DE) followed by nanocapillary RP tryptic peptide separation prior to MS/MS analysis. The analysis generates a 2-D protein array where each pixel in the array contains a group of proteins with known pI and molecular weight range. Analysis of the HUPO samples using this strategy resulted in 575 and 2890 protein identifications from plasma and serum, respectively, based on HUPO-approved criteria for high-confidence protein assignments. Most importantly, a substantial number of low-abundance proteins (low ng/mL - pg/mL range) were identified. Although larger volumes were used in initial prefractionation steps, the protein identifications were derived from fractions equivalent to approximately 0.6 microL (45 microg) of plasma and 2.4 microL (204 microg) of serum. The time required for analyzing the entire protein array for each sample is comparable to some published shotgun analyses of plasma and serum proteomes. Therefore, protein array pixelation is a highly sensitive method capable of detecting proteins differing in abundance by up to nine orders of magnitude. With further refinement, this method has the potential for even higher capacity and higher throughput.

Systematic detection of low-abundance proteins in human blood that may be putative disease biomarkers is complicated by an extremely wide range of protein abundances. Hence, depletion of major proteins is one potential strategy for enhancing detection sensitivity in serum or plasma. This study compared a recently commercialized HPLC column containing antibodies to six of the most abundant blood proteins ("Top-6 depletion") with either older Cibacron blue/Protein A or G depletion methods or no depletion. In addition, a prototype spin column version of the HPLC column and an alternative prototype two antibody spin column were evaluated. The HPLC polyclonal antibody column and its spin column version are very promising methods for substantially simplifying human serum or plasma samples. These columns show the lowest nonspecific binding of the depletion methods tested. In contrast other affinity methods, particularly dye-based resins, yielded many proteins in the bound fractions in addition to the targeted proteins. Depletion of six abundant proteins removed about 85% of the total protein from human serum or plasma, and this enabled 10- to 20-fold higher amounts of depleted serum or plasma samples to be applied to 2-D gels or alternative protein profiling methods such as protein array pixelation. However, the number of new spots detected on 2-D gels was modest, and most newly visualized spots were minor forms of relatively abundant proteins. The inability to detect low-abundance proteins near expected 2-D staining limits was probably due to both the highly heterogeneous nature of most plasma or serum proteins and masking of many low-abundance proteins by the next series of most abundant proteins. Hence, non2-D methods such as protein array pixelation are more promising strategies for detecting lower abundance proteins after depleting the six abundant proteins.

There is a substantial list of pre-analytical variables that can alter the analysis of blood-derived samples. We have undertaken studies on some of these issues including choice of sample type, stability during storage, use of protease inhibitors, and clinical standardization. As there is a wide range of sample variables and a broad spectrum of analytical techniques in the HUPO PPP effort, it is not possible to define a single list of pre-analytical standards for samples or their processing. We present here a compendium of observations, drawing on actual results and sound clinical theories and practices. Based on our data, we find that (1) platelet-depleted plasma is preferable to serum for certain peptidomic studies; (2) samples should be aliquoted and stored preferably in liquid nitrogen; (3) the addition of protease inhibitors is recommended, but should be incorporated early and used judiciously, as some form non specific protein adducts and others interfere with peptide studies. Further, (4) the diligent tracking of pre-analytical variables and (5) the use of reference materials for quality control and quality assurance, are recommended. These findings help provide guidance on sample handling issues, with the overall suggestion being to be conscious of all possible pre-analytical variables as a prerequisite of any proteomic study.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

Background: Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value. Methods: Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP. Results: A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors. Conclusions: Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies. [ABSTRACT FROM AUTHOR]

The most cancer-specific biomarkers in blood are likely to be proteins shed directly by the tumor rather than less specific inflammatory or other host responses. The use of xenograft mouse models together with in-depth proteome analysis for identification of human proteins in the mouse blood is an under-utilized strategy that can clearly identify proteins shed by the tumor. In the current study, 268 human proteins shed into mouse blood from human OVCAR-3 serous tumors were identified based upon human vs. mouse species differences using a four-dimensional plasma proteome fractionation strategy. A multi-step prioritization and verification strategy was subsequently developed to efficiently select some of the most promising biomarkers from this large number of candidates. A key step was parallel analysis of human proteins detected in the tumor supernatant, because substantially greater sequence coverage for many of the human proteins initially detected in the xenograft mouse plasma confirmed assignments as tumor-derived human proteins. Verification of candidate biomarkers in patient sera was facilitated by in-depth, label-free quantitative comparisons of serum pools from patients with ovarian cancer and benign ovarian tumors. The only proteins that advanced to multiple reaction monitoring (MRM) assay development were those that exhibited increases in ovarian cancer patients compared with benign tumor controls. MRM assays were facilely developed for all 11 novel biomarker candidates selected by this process and analysis of larger pools of patient sera suggested that all 11 proteins are promising candidate biomarkers that should be further evaluated on individual patient blood samples. [ABSTRACT FROM AUTHOR]

Spectrin assembles into an anti-parallel heterodimeric flexible rod-like molecule through a multistep process initiated by a high affinity interaction between discrete complementary homologous motifs or ‘repeats’ near the actin binding domain. Attempts to determine crystallographic structures of this critical dimer initiation complex have so far been unsuccessful. Therefore, in this study we determined the subunit-subunit docking interface and a plausible medium resolution structure of the heterodimer initiation site using homology modeling coupled with structural refinement based on experimentally determined distance constraints. Intramolecular and intermolecular cross-links formed by the ‘zero length’ cross-linking reagent, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide were identified after trypsin digestion of cross-linked heterodimer complex using liquid chromatography-tandem mass spectrometry analysis. High confidence assignment of cross-linked peptides was facilitated by determination of cross-linked peptide masses with an uncertainty of a few parts per million using a high sensitivity linear ion trap mass spectrometer equipped with a Fourier- transform ion cyclotron resonance detector. Six interchain cross-links distinguished between alternative docking models, and these distance constraints, as well as three intrachain cross-links, were used to further refine an initial homology-based structure. The final rnodel is consistent with all available physical data, including protease protection experiments, isothermal titration calorimetry analyses, and location of a common polymorphism that destabilizes dimerization. This model supports the hypothesis that initial docking of the correct α and β repeats from among many very similar repeats in both subunits is driven primarily by long range electrostatic interactions. [ABSTRACT FROM AUTHOR]

Red cell spectrin α and β subunits consist primarily of many tandem homologous motifs with very similar three- helix-bundle structures and similar dimer interfaces. Although misassembled homodimers can form under some conditions, correctly aligned heterodimers consistently assemble provided a small ‘dimer initiation’ site near the actin binding domain is present. The dimer initiation site has been characterized to some extent, but little is known about the subsequent, low-affinity lateral interactions of the remaining motifs along the length of this ropelike molecule or the forces involved in these two steps of the dimerization process. In this study, we used isothermal titration calorimetry to deduce the mechanism and energetics of the two heterodimer assembly phases. The high-affinity initiation of dimerization is primarily enthalpically driven, which is consistent with initial alignment and docking of specific complementary α and β motifs in the dimer initiation site driven by long-range electrostatic interactions followed by tight binding stabilized by hydrogen bonds and other hydrophilic interactions. In contrast, the subsequent weak lateral associations of additional motifs are primarily entropically driven, suggesting binding primarily involves weak hydrophobic interactions. Although initial docking is largely electrostatic, the only lateral interaction within the first four pairs of motifs that involves a net change in protons is the interaction of the α18 and β4 repeats. This substoichiometric uptake of protons could be due to a pKa shift of a histidine in the α18 motif located near the dimer interface in a proposed homology-based model. On the basis of this analysis of heterodimer thermodynamics, a detailed model of spectrin dimer assembly is proposed. [ABSTRACT FROM AUTHOR]