Human fallopian tube proteome shows high coverage of mesenchymal stem cells associated proteins

The object of this research was to report a draft proteome of human fallopian tube (hFT) comprises 5416 identified proteins, which could be considered as a physiological reference to complement Human Proteome Draft. The proteomic raw data and metadata were stored in an integrated proteome resources centre iProX (IPX00034300). This hFT proteome contains many hFT markers newly identified by mass spectrum. This hFT proteome comprises 660 high-, 3605 medium- and 1181 low-abundant proteins. Ribosome, cytoskeleton, vesicle and protein folding associated proteins showed obvious tendency to be higher abundance in hFT. The extraordinary high coverage of mesenchymal stem cells (MSCs)-associated proteins were identified in this hFT proteome, which highly supported that hFT should contain a plenty of MSCs.     

Serum/Plasma depletion with chicken immunoglobulin Y antibodies for proteomic analysis from multiple Mammalian species.

Plasma from different species is the most accessible and valuable source for biomarker discovery in clinical and animal samples. However, due to the high abundance of some proteins such as albumin and immunoglobulins, low-abundant proteins are often undetectable in proteomic analysis of plasma. We have established a plasma depletion scheme using chicken antibodies against various abundant proteins. This immunoaffinity purification procedure is able to deplete albumin across multiple species. The high binding capacity and specificity of the chicken antibody enables the efficient capture of its ligand from microliter volumes of plasma sample. The resulting two-dimensional gel analyses of the depleted and captured samples show significant enhancement of the low-abundant proteins and specific capture of the abundant ligand. By utilizing this sample preparation scheme, it is now possible to analyze the plasma proteome from multiple species in a potentially rapid and large-scale capacity for biomarker discovery, drug target discovery, and toxicology studies.     

Depletion and fractionation technologies in plasma proteomic analysis

This review intends to survey the traditional and current technologies in the depletion and subfractionation of plasma proteins for further analyses. The value of depletion aims to enrich low-abundant proteins by removing highly abundant proteins, such as albumin or immunoglobulin G, from plasma. With this approach, one can examine both the resulting high- and low-abundant protein fractions. The depleted protein population can be further subfractionated based on their isoelectric point ranges, creating a more discrete pool of proteins for detailed post-translational modification studies by methods such as 2D gel electrophoresis and mass spectrometry. The concept of divide to conquer will greatly enhance our ability to identify and characterize low-abundant proteins and cleaved peptides from plasma as important diagnostic markers or potential drug targets. This can potentially reverse the decline in the development of new plasma diagnostic tests.     

Depletion and fractionation technologies in plasma proteomic analysis

This review intends to survey the traditional and current technologies in the depletion and subfractionation of plasma proteins for further analyses. The value of depletion aims to enrich low-abundant proteins by removing highly abundant proteins, such as albumin or immunoglobulin G, from plasma. With this approach, one can examine both the resulting high- and low-abundant protein fractions. The depleted protein population can be further subfractionated based on their isoelectric point ranges, creating a more discrete pool of proteins for detailed post-translational modification studies by methods such as 2D gel electrophoresis and mass spectrometry. The concept of divide to conquer will greatly enhance our ability to identify and characterize low-abundant proteins and cleaved peptides from plasma as important diagnostic markers or potential drug targets. This can potentially reverse the decline in the development of new plasma diagnostic tests.     

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.     

The human plasma proteome: a nonredundant list developed by combination of four separate sources

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.     

Two-dimensional separation of human plasma proteins using iterative free-flow electrophoresis

Blood plasma is the most complex human-derived proteome, containing other tissue proteomes as subsets. This proteome has only been partially characterized due to the extremely wide dynamic range of the plasma proteins of more than ten orders of magnitude. Thus, the reduction in sample complexity prior to mass spectrometric analysis is particularly important and alternative separation methodologies are required to more effectively mine the lower abundant plasma proteins. Here, we demonstrated a novel separation approach using 2-D free-flow electrophoresis (FFE) separating proteins and peptides in solution according to their pI prior to LC-MS/MS. We used the combination of sequential protein and peptide separation by first separating the plasma proteins into specific FFE fractions. Tryptic digests of the separated proteins were generated and subsequently separated using FFE. The protein separation medium was optimized to segregate albumin into specific fractions containing only few other proteins. An optimization of throughput for the protein separation reduced the separation time of 1 mL of plasma to approximately 3 h providing sufficient material for digestion and the subsequent peptide separation. Our approach revealed low-abundant proteins (e.g., L-selectin at 17 ng/mL and vascular endothelial-cadherin precursor at 30 ng/mL) and several tissue leakage products, thus providing a powerful orthogonal separation step in the proteomics workflow.     

Enhanced detection of CNS cell secretome in plasma protein-depleted cerebrospinal fluid

Human cerebrospinal fluid (CSF) proteome is actively investigated to identify relevant biomarkers and therapeutic targets for neurological disorders. Approximately 80% of CSF proteome originate from plasma, yielding a high dynamic range in CSF protein concentration and precluding identification of potential biomarkers originating from CNS cells. Here, we have adapted the most complete multiaffinity depletion method available to remove 20 abundant plasma proteins from a CSF pool originating from patients with various cognitive disorders. We identified 622 unique CSF proteins in immunodepleted plus retained fractions versus 299 in native CSF, including 22 proteins hitherto not identified in CSF. Parallel analysis of neuronal secretome identified 34 major proteins secreted by cultured cortical neurons (cell adhesion molecules, proteins involved in neurite outgrowth and axonal guidance, modulators of synaptic transmission, proteases and protease inhibitors) of which 76% were detected with a high confidence in immunodepleted CSF versus 50% in native CSF. Moreover, a majority of proteins previously identified as secretory products of choroid plexus cells or astrocytes were detected in immunodepleted CSF. Hence, removal of 20 major plasma proteins from CSF improves detection of brain cell-derived proteins in CSF and should facilitate identification of relevant biomarkers in CSF proteome profiling analyses.     

The serum proteome of Equus caballus

We constructed a reference two-dimensional protein map for horse (Equus caballus) serum. The serum proteins were separated by two-dimensional electrophoresis (2-DE); 29 different gene products were identified. Proteins represented by 25 spots/spot groups were identified by tandem nanoelectrospray mass spectrometry (MS), four by matrix-assisted laser desorption ionization time-of-flight (TOF) MS and one was sequenced by TOF-TOF technology. The identities of four proteins were deduced by similarity to the human plasma protein database. In selected cases, i.e. the immunoglobulins, immunoblotting with specific antibodies provided additional information about the respective proteins. Albumin was detected as the full-length protein and as fragments of various sizes. Spots representing products of different mass and charge were also detected for alpha1-antitrypsin, haptoglobin and transthyretin. Thus, despite the fact that the Equus caballus genome is incompletely characterized, we were able to identify almost all moderate to high abundance proteins stained in the serum 2-DE pattern.     

Rat plasma proteomics: effects of abundant protein depletion on proteomic analysis

The proteomic analysis of plasma and serum samples represents a formidable challenge due to the presence of a few highly abundant proteins such as albumin and immunoglobulins. Detection of low abundance protein biomarkers requires therefore either the specific depletion of high abundance proteins with immunoaffinity columns and/or optimized protein fractionation methods based on charge, size or hydrophobicity. Here we describe the depletion of seven abundant rat plasma proteins with an immunoaffinity column with coupled antibodies directed against albumin, IgG, transferrin, IgM, haptoglobin, fibrinogen and alpha1-anti-trypsin. The IgY-R7-LC2 (Beckman Coulter) column showed high specificity for the targeted proteins and was able to efficiently remove most of the albumin, IgG and transferrin from rat plasma samples as judged by Western blot analysis. Depleted rat plasma protein samples were analyzed by SELDI-TOF MS, 2D SDS-PAGE and 2D-LC and compared to non-depleted plasma samples as well as to the abundant protein fraction that was eluted from the immunoaffinity column. Analysis of the depleted plasma protein fraction revealed improved signal to noise ratios, regardless of which proteomic method was applied. However, only a small number of new proteins were observed in the depleted protein fraction. Immunoaffinity depletion of abundant plasma proteins results in the significant dilution of the original sample which complicates subsequent analysis. Most proteomic approaches require specialized sample preparation procedures during which significant losses of less abundant proteins and potential biomarkers can occur. Even though abundant protein depletion reduces the dynamic range of the plasma proteome by about 2-3 orders of magnitude, the difference between medium-abundant and low abundant plasma proteins is still in the range of 7-8 orders of magnitude and beyond the dynamic range of current proteomic technologies. Thus, exploring the plasma proteome in greater detail remains a daunting task.     

Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples

Serum proteins may often serve as indicators of disease and is a rich source for biomarker discovery. However, the large dynamic range of proteins in serum makes the analysis very challenging because high-abundant proteins tend to mask those of lower abundance. A prefractionation step, such as depletion of a few high-abundant proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may prove to be informative biomarkers. In the present study, five different depletion columns were investigated considering efficiency, specificity, and reproducibility. Our research included quantitative determination of total protein, albumin, and immunoglobulin G (IgG) concentrations, one- and two-dimensional gels and mass spectrometric analysis of the serum samples before and after the depletion step. Our results showed that all five depletion columns tested removed albumin and IgG with high efficiency. We found that based on reproducibility and binding specificity, the Multiple Affinity Removal Column that removed a total of six high-abundant proteins (albumin, IgG, antitrypsin, IgA, transferring, and haptoglobin) offered the most promising depletion approach. Among the disposable (single-use) products, the ProteoExtract Albumin/IgG Removal kit displayed the best results. Depleted serum from the Multiple Affinity Removal column was further evaluated by 2-D gel electrophoresis (2-DE) analysis, and the results indicated increased resolution and improved intensity of low-abundant proteins in a reproducible fashion. Our study provides a comprehensive investigation of commercially available depletion columns and will be of high importance for future proteomic studies on serum samples.     

Sequential Extraction Results in Improved Proteome Profiling of Medicinal Plant Pinellia ternata Tubers,Which Contain Large Amounts of High-Abundance Proteins

Pinellia ternata tuber is one of the well-known Chinese traditional medicines. In order to understand the pharmacological properties of tuber proteins, it is necessary to perform proteome analysis of P. ternata tubers. However, a few high-abundance proteins (HAPs), mainly mannose-binding lectin (agglutinin), exist in aggregates of various sizes in the tubers and seriously interfere with proteome profiling by two-dimensional electrophoresis (2-DE). Therefore, selective depletion of these HAPs is a prerequisite for enhanced proteome analysis of P. ternata tubers. Based on differential protein solubility, we developed a novel protocol involving two sequential extractions for depletion of some HAPs and prefractionation of tuber proteins prior to 2-DE. The first extraction using 10% acetic acid selectively extracted acid-soluble HAPs and the second extraction using the SDS-containing buffer extracted remaining acid-insoluble proteins. After application of the protocol, 2-DE profiles of P. ternata tuber proteins were greatly improved and more protein spots were detected, especially low-abundance proteins. Moreover, the subunit composition of P. ternata lectin was analyzed by electrophoresis. Native lectin consists of two hydrogen-bonded subunits (11 kDa and 25 kDa) and the 11 kDa subunit was a glycoprotein. Subsequently, major HAPs in the tubers were analyzed by mass spectrometry, with nine protein spots being identified as lectin isoforms. The methodology was easy to perform and required no specialized apparatus. It would be useful for proteome analysis of other tuber plants of Araceae.     

Multiplexed LC‐MS/MS analysis of horse plasma proteins to study doping in sport

The development of protein biomarkers for the indirect detection of doping in horse is a potential solution to doping threats such as gene and protein doping. A method for biomarker candidate discovery in horse plasma is presented using targeted analysis of proteotypic peptides from horse proteins. These peptides were first identified in a novel list of the abundant proteins in horse plasma. To monitor these peptides, an LC‐MS/MS method using multiple reaction monitoring was developed to study the quantity of 49 proteins in horse plasma in a single run. The method was optimised and validated, and then applied to a population of race‐horses to study protein variance within a population. The method was finally applied to longitudinal time courses of horse plasma collected after administration of an anabolic steroid to demonstrate utility for hypothesis‐driven discovery of doping biomarker candidates.     

Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery

The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.     

Proteomics of Protein Secretion by Bacillus subtilis: Separating the “Secrets” of the Secretome

Secretory proteins perform a variety of important “remote-control” functions for bacterial survival in the environment. The availability of complete genome sequences has allowed us to make predictions about the composition of bacterial machinery for protein secretion as well as the extracellular complement of bacterial proteomes. Recently, the power of proteomics was successfully employed to evaluate genome-based models of these so-called secretomes. Progress in this field is well illustrated by the proteomic analysis of protein secretion by the gram-positive bacterium Bacillus subtilis, for which ~90 extracellular proteins were identified. Analysis of these proteins disclosed various “secrets of the secretome,” such as the residence of cytoplasmic and predicted cell envelope proteins in the extracellular proteome. This showed that genome-based predictions reflect only ~50% of the actual composition of the extracellular proteome of B. subtilis. Importantly, proteomics allowed the first verification of the impact of individual secretion machinery components on the total flow of proteins from the cytoplasm to the extracellular environment. In conclusion, proteomics has yielded a variety of novel leads for the analysis of protein traffic in B. subtilis and other gram-positive bacteria. Ultimately, such leads will serve to increase our understanding of virulence factor biogenesis in gram-positive pathogens, which is likely to be of high medical relevance.     

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.     

Mining the plasma proteome for disease applications across seven logs of protein abundance

The current state of proteomics technologies has sufficiently advanced to allow in-depth quantitative analysis of the plasma proteome and development of a related knowledge base. Here we review approaches that have been applied to increase depth of analysis by mass spectrometry given the substantial complexity of plasma and the vast dynamic range of protein abundance. Fractionation strategies resulting in reduced complexity of individual fractions followed by mass spectrometry analysis of digests from individual fractions has allowed well in excess of 1000 proteins to be identified and quantified with high confidence that span more than seven logs of protein abundance. Such depth of analysis has contributed to elucidation of plasma proteome variation in health and of protein changes associated with disease states.