Nondepleted Plasma Research Articles

Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology

BackgroundColorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization.MethodsUsing a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients.ResultsWe achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods.ConclusionsOur study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Depletion of abundant plasma proteins for extracellular vesicle proteome characterization: benefits and pitfalls

Blood extracellular vesicles (EVs) play essential roles in cell–cell communication and their molecular cargo is a promising source of disease biomarkers. However, proteomic characterization of plasma-derived EVs is challenged by the presence of highly abundant plasma proteins, which limits the detection of less abundant proteins, and by the low number of EVs in biological fluids. The aim of this study was to investigate if the removal of abundant plasma proteins prior to EV isolation could improve plasma-derived EV characterization by LC–MS/MS and expand the proteome coverage. Plasma depletion was performed using a single-use spin column and EVs were isolated from only 100 µL of non-depleted and depleted plasma by size exclusion chromatography. Afterwards, EVs were characterized by nanoparticle tracking analysis and mass spectrometry–based proteomics using a data-independent acquisition approach. Depleted plasma-derived EVs had higher particle concentrations and particle-to-protein ratios. Depletion did increase the protein coverage with a higher number of identifications in EVs from depleted plasma (474 proteins) than from non-depleted (386 proteins). However, EVs derived from non-depleted plasma carried a slightly higher number of common EV markers. Overall, our findings suggest that plasma depletion prior to EV isolation by size exclusion chromatography provides higher yield and protein coverage, but slightly lower identification of EV markers. This study also showed the possibility to characterize the proteome of EVs derived from small plasma volumes, encouraging the clinical feasibility of the discovery of EV biomarkers.

Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins.

Preclinical and clinical trials require rapid, precise, and multiplexed analytical methods to characterize the complex samples and to allow high-throughput biomarker monitoring with low consumption of sample material. Targeted proteomics has been used to address these challenges when quantifying protein abundances in complex biological matrices. In many of these studies, blood plasma is collected either as the main research or diagnostic sample or in combination with other specimens. Mass spectrometry (MS)-based targeted proteomics using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) with stable isotope-labeled internal standard (SIS) peptides allows robust characterization of blood plasma protein via absolute quantification. Compared to other commonly used technologies like enzyme-linked immunosorbent assay (ELISA), targeted proteomics is faster, more sensitive, and more cost-effective. Here we describe a protocol for the quantification of proteins in blood plasma using targeted MRM proteomics with heavy-labeled internal standards. The 270-protein panel allows rapid and robust absolute quantitative proteomic characterization of blood plasma in a 1h gradient. The method we describe here works for non-depleted plasma, which makes it simple and easy to implement. Moreover, the protocol works with the two most commonly used blood plasma collection methods used in practice, namely, either K2EDTA or sodium citrate as anticoagulants.

Identification of plasma proteins associated with oesophageal cancer chemotherapeutic treatment outcomes using SWATH-MS

Oesophageal adenocarcinoma (OAC) is an aggressive cancer with a five-year survival of <15%. Current chemotherapeutic strategies only benefit a minority (20‐30%) of patients and there are no methods available to differentiate between responders and non-responders. We performed quantitative proteomics using Sequential Window Acquisition of all THeoretical fragment-ion spectra-Mass Spectrometry (SWATH-MS) on albumin/IgG-depleted and non-depleted plasma samples from 23 patients with locally advanced OAC prior to treatment. Individuals were grouped based on tumour regression (TRG) score (TRG1/2/3 vs TRG4/5) after chemotherapy, and differentially abundant proteins were compared. Protein depletion of highly abundant proteins led to the identification of around twice as many proteins. SWATH-MS revealed significant quantitative differences in the abundance of several proteins between the two groups. These included complement c1q subunit proteins, C1QA, C1QB and C1QC, which were of higher abundance in the low TRG group. Of those that were found to be of higher abundance in the high TRG group, glutathione S-transferase pi (GSTP1) exhibited the lowest p-value and highest classification accuracy and Cohen's kappa value. Concentrations of these proteins were further examined using ELISA-based assays. This study provides quantitative information relating to differences in the plasma proteome that underpin response to chemotherapeutic treatment in oesophageal cancers. SignificanceOesophageal cancers, including oesophageal adenocarcinoma (OAC) and oesophageal gastric junction cancer (OGJ), are one of the leading causes of cancer mortality worldwide. Curative therapy consists of surgery, either alone or in combination with adjuvant or neoadjuvant chemotherapy or radiation, or combination chemoradiotherapy regimens. There are currently no clinico-pathological means of predicting which patients will benefit from chemotherapeutic treatments. There is therefore an urgent need to improve oesophageal cancer disease management and treatment strategies. This work compared proteomic differences in OAC patients who responded well to chemotherapy as compared to those who did not, using quantitative proteomics prior to treatment commencement. SWATH-MS analysis of plasma (with and without albumin/IgG-depletion) from OAC patients prior to chemotherapy was performed. This approach was adopted to determine whether depletion offered a significant improvement in peptide coverage. Resultant datasets demonstrated that depletion increased peptide coverage significantly. Additionally, there was good quantitative agreement between commonly observed peptides. Data analysis was performed by adopting both univariate as well as multivariate analysis strategies. Differentially abundant proteins were identified between treatment response groups based on tumour regression grade. Such proteins included complement C1q sub-components and GSTP1. This study provides a platform for further work, utilising larger sample sets across different treatment regimens for oesophageal cancer, that will aid the development of ‘treatment response prediction assays’ for stratification of OAC patients prior to chemotherapy.

Optimized Glycopeptide Enrichment Method-It Is All About the Sauce.

Protein glycosylation is a family of posttranslational modifications that play a crucial role in many biological pathways and diseases. The enrichment and analysis of such a diverse family of modifications are very challenging because of the number of possible glycan–peptide combinations. Among the methods used for the enrichment of glycopeptides, boronic acid never lived up to its promise. While most studies focused on improving the affinity of the boronic acids to the sugars, we discovered that the buffer choice is just as important for successful enrichment if not more so. We show that an amine-less buffer allows for the best glycoproteomic coverage, in human plasma and brain specimens, improving total quantified glycopeptides by over 10-fold, and reaching 1598 N-linked glycopeptides in the brain and 737 in nondepleted plasma. We speculate that amines compete with the glycans for boronic acid binding, and therefore the elimination of them improved the method significantly.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma.

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Leveraging homologies for cross-species plasma proteomics in ungulates using data-independent acquisition

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SignificanceAbility to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis.

BACKGROUNDTuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma.METHODSWe applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203).RESULTSWe generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low–molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81).CONCLUSIONWe report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test.FUNDINGColciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research.

Toxicoproteomics Disclose Pesticides as Downregulators of TNF-α, IL-1β and Estrogen Receptor Pathways in Breast Cancer Women Chronically Exposed.

Deleterious effects have been widely associated with chronic pesticide exposure, including cancer development. In spite of several known consequences that pesticides can trigger in the human body, few is known regarding its impact on breast cancer women that are chronically exposed to such substances during agricultural work lifelong. In this context, the present study performed a high-throughput toxicoproteomic study in association with a bioinformatics-based design to explore new putative processes and pathways deregulated by chronic pesticide exposure in breast cancer patients. To reach this goal, we analyzed comparatively non-depleted plasma samples from exposed (n = 130) and non-occupationally exposed (n = 112) women diagnosed with breast cancer by using a label-free proteomic tool. The list of proteins differentially expressed was explored by bioinformatics and the main pathways and processes further investigated. The toxicoproteomic study revealed that women exposed to pesticides exhibited mainly downregulated events, linked to immune response, coagulation and estrogen-mediated events in relation to the unexposed ones. Further investigation shown that the identified deregulated processes and pathways correlated with significant distinct levels tumor necrosis factor alpha and interleukin 1 beta in the blood, and specific clinicopathological characteristics pointed out by bioinformatics analysis as adipose-trophic levels, menopause and intratumoral clots formation. Altogether, these findings reinforce pesticides as downregulators of several biological process and highlight that these compounds can be linked to poor prognosis in breast cancer.

Abundant plasma protein depletion using ammonium sulfate precipitation and Protein A affinity chromatography

Plasma is a highly valuable resource for biomarker research since it is easy obtainable and contains a high amount of information on patient health status. Although advancements in the field of proteomics enabled analysis of the plasma proteome, identification of low abundant proteins remains challenging due to high complexity and large dynamic range. In order to reduce the dynamic range of protein concentrations, a tandem depletion technique consisting of ammonium sulfate precipitation and Protein A affinity chromatography was developed. Using this method, 50% of albumin, together with other high abundant proteins such as alpha-1-antitrypsin, was depleted from the plasma sample at 20% to 40% ammonium sulfate saturation levels. In combination with immunoglobulin removal using a Protein A column, this technique delivered up to 40 new low- to medium abundance protein identifications when performing a shotgun mass spectrometry analysis. Compared to non-depleted plasma, 270 additional protein spots were observed during 2D-PAGE analysis. These results illustrate that this tandem depletion method is equivalent to commercial kits which are based on immune-affinity chromatography. Moreover, this method using Protein A immunoglobulin depletion was shown to be highly reproducible and a minimal amount of non-target proteins was depleted. The combination of ammonium sulfate precipitation and Protein A affinity chromatography offers a low cost, efficient, straightforward and reproducible alternative to commercial kits, with proteins remaining in native conformation, allowing protein activity and protein interaction studies.

SWATH Mass Spectrometry for Proteomics of Non-Depleted Plasma.

The limitations commonly observed in data-dependent acquisition (DDA) mass spectrometric investigation of non-depleted human plasma are mainly due to the large dynamic concentration range of protein expression. Less abundant proteins are usually masked by highly abundant proteins and are therefore difficult to reliably detect. Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) mass spectrometry (MS), as a representative of data-independent acquisition (DIA) approaches, provides an opportunity to improve plasma-based biomarker discovery studies because this approach does not rely on precursor intensity for fragmentation selection but rather analyzes all precursors in specified mass ranges. Here, we describe a workflow for SWATH-MS-based analysis of non-depleted plasma including sample preparation, data acquisition, and statistical analysis.

Proteomic biomarkers for psoriasis and psoriasis arthritis

Although several new biomarkers have been recently proposed for psoriasis (Ps) and psoriasis arthritis (PsA), nothing is known about their diagnostic sensitivity and specificity, and their routine use. We therefore searched in-depth for new biomarker candidates using a biobank with EDTA-plasma from 158 individuals, patients and healthy controls. Samples from 6 selected pairs (patients against healthy controls) were searched proteomically using a workflow of extensive and precise design that is highly comprehensive. Subsequent verification was performed using ELISA and the entire biobank. By proteomic methods, 208 altered proteins were identified. Of these, 15 biomarker candidates were selected for verification. Of these 15, 4 individual parameters and 11 combinations significantly discriminated between patient and control groups. These individual parameters were Zn-α2-glycoprotein, complement C3, polymeric immunoglobulin receptor, and plasma kallikrein. Significant discrimination was obtained by combinations of 2 or 3 parameters. One combination seemed suitable for diagnosing PsA. Moreover, several candidates desmoplakin, complement C3, polymeric immunoglobulin receptor, and cytokeratin 17, correlated with PASI in all patients. This first comprehensive proteomic study on non-depleted plasma identified several biomarker candidates that have not been described before as well as some known from previous studies. Biological significanceOur non-gel proteomic analysis is based on the highly comprehensive and significantly optimized chromatographic protein pre-fractionation. The method allows a biomarker search in non-depleted plasma. The subsequent verification by ELISA identifies several biomarker-candidates for the unbiased diagnosis of psoriasis and psoriasis arthritis. Four of the identified candidate markers might be used individually. Combinations of several parameters improve the diagnostic sensitivity and specificity. The still not validated candidates form a reserve for further evaluation. Moreover, mass spectrometric data uncover several biomarker-candidates which show diverse protein species of the same protein with opposing changes in the same sample.

Reverse-Polynomial Dilution Calibration Methodology Extends Lower Limit of Quantification and Reduces Relative Residual Error in Targeted Peptide Measurements in Blood Plasma

Matrix effect is the alteration of an analyte's concentration-signal response caused by co-existing ion components. With electrospray ionization (ESI), matrix effects are believed to be a function of the relative concentrations, ionization efficiency, and solvation energies of the analytes within the electrospray ionization droplet. For biological matrices such as plasma, the interactions between droplet components is immensely complex and the effect on analyte signal response not well elucidated. This study comprised of three sequential quantitative analyses: we investigated whether there is a generalizable correlation between the range of unique ions in a sample matrix (complexity); the amount of matrix components (concentration); and matrix effect, by comparing an E. coli digest matrix (∼2600 protein proteome) with phospholipid depleted human blood plasma, and unfractionated, nondepleted human plasma matrices (∼10(7) proteome) for six human plasma peptide multiple reaction monitoring assays. Our data set demonstrated analyte-specific interactions with matrix complexity and concentration properties resulting in significant ion suppression for all peptides (p < 0.01), with nonuniform effects on the ion signals of the analytes and their stable-isotope analogs. These matrix effects were then assessed for translation into relative residual error and precision effects in a low concentration (∼0-250 ng/ml) range across no-matrix, complex matrix, and highly complex matrix, when a standard addition stable isotope dilution calibration method was used. Relative residual error (%) and precision (CV%) by stable isotope dilution were within <20%; however, error in phospholipid-depleted and nondepleted plasma matrices were significantly higher compared with no-matrix (p = 0.006). Finally a novel reverse-polynomial dilution calibration method with and without phospholipid-depletion was compared with stable isotope dilution for relative residual error and precision. Reverse-polynomial dilution techniques extend the Lower Limit of Quantification and reduce error (p = 0.005) in low-concentration plasma peptide assays and is broadly applicable for verification phase Tier 2 multiplexed multiple reaction monitoring assay development within the FDA-National Cancer Institute (NCI) biomarker development pipeline.

Ultradepletion of Human Plasma using Chicken Antibodies: A Proof of Concept Study

Human plasma arguably represents the most comprehensive version of the human proteome. Despite its immense theoretical discovery potential, plasma has many high and medium abundance proteins that mask low abundance protein disease biomarkers of relevance, making the discovery of novel diagnostic markers particularly difficult. Some form of protein depletion and/or fractionation is essential in order to detect markers of low abundance. Here, we describe a "proof of concept" two-pronged approach to immunodeplete abundant proteins from human plasma. The method, called API (Abundant Protein Immunodepletion), involves the fractionation of plasma using dual ion exchange columns (protein repetitive orthogonal offline fractionation (PROOF)) to simplify the proteome, the production of polyclonal IgY against each fraction and finally using the purified antibodies in a immunodepletion column. We explored the use of this product for immunodepletion of human plasma and identified a total of 165 nonredundant proteins after depletion. Of these, 38 proteins that were not previously identified in nondepleted plasma were now detected. It is envisaged that further optimization of the method as well as its cyclic implementation (by reinjecting depleted plasma into chickens for second round of antibody production) can make this technology highly robust, extremely cost-effective, and ideal for high throughput biomarker discovery.

Apoe, Mbl2, and Psp Plasma Protein Levels Correlate with Diabetic Phenotype in NZO Mice—An Optimized Rapid Workflow for SRM-Based Quantification

Male New Zealand Obese (NZO) mice progress through pathophysiological stages similar to humans developing obesity-associated type 2 diabetes (T2D). The current challenge is to establish quantitative proteomics from small plasma sample amounts. We established an analytical workflow that facilitates a reproducible depletion of high-abundance proteins, has high throughput applicability, and allows absolute quantification of proteins from mouse plasma samples by LC-SRM-MS. The ProteoMiner equalizing technology was adjusted to the small sample amount, and reproducibility of the identifications was monitored by spike proteins. Based on the label-free relative quantification of proteins in depleted plasma of a test set of NZO mice, assays for potential candidates were designed for the setup of a targeted selected reaction monitoring (SRM) approach and absolute quantification. We could demonstrate that apolipoprotein E (Apoe), mannose-binding lectin 2 (Mbl2), and parotid secretory protein (Psp) are present at significantly different quantities in depleted plasma of diabetic NZO mice compared to non-diabetic controls using AQUA peptides. Quantification was validated for Mbl2 using the ELISA technology on non-depleted plasma. We conclude that the depletion technique is applicable to restricted sample amounts and suitable for the identification of T2D signatures in plasma.

The N-Glycome of Human Plasma

N-linked glycans isolated from human plasma proteins have been profiled and sequenced by mass spectrometry using an ion trap instrument (ITMSn). The released glycans were prepared as reduced, methylated analogues and directly infused into a chip-based nanoelectrospray ionization system and analyzed by ITMSn. The resulting mass profiles (MS1) of IgG-depleted and nondepleted plasma samples were contrasted and these results were again compared with recent literature reports. Before depletion, approximately 50 independent glycan ions were detected; this more than doubled to 106 after depletion. The mass range profiled was 1-5 kDa which included many doubly and triply charged ions that were resolved by higher MS resolution. Selected ions in the depleted sample were disassembled to define their detailed structure providing a high-performance sequencing result. The simplicity of this nonchromatographic, direct infusion and gas-phase structural characterization compares most favorably with the latest reports using alternative instrumentation and adjunct techniques.

Optimizing Performance of Glycopeptide Capture for Plasma Proteomics

Selective capture of glycopolypeptides followed by release and analysis of the former glycosylation-site peptides has been shown to have promise for reducing the complexity of body fluids such as blood for biomarker discovery. In this work, a protocol based on capture of polypeptides containing a N-linked carbohydrate from human plasma using commercially available magnetic beads coupled with hydrazide chemistry was optimized and partially automated through the use of a KingFisher magnetic particle processor. Comparison of bead-based glycocapture at the protein-level vs the peptide-level revealed differences in the specificity, reproducibility, and absolute number of former glycosylation-site peptides detected. Evaluation of a range of capture and elution conditions led to an optimized protocol with a 24% intraday and 30% interday CV and a glycopeptide capture specificity of 99%. Depleting the plasma of 14 high abundance proteins improved detection sensitivity by approximately 1 order of magnitude compared to nondepleted plasma and resulted in an increase of 24% in the number of identified glycoproteins. The sensitivity of SPEG for detection of glycoproteins in depleted, non-fractionated plasma was found to be in the 10-100 pmol/mL range corresponding to glycoprotein levels ranging from 100's of nanograms/mL to 10's of micrograms/mL. Despite high capture specificity, the total number of glycoproteins detected and the sensitivity of SPEG in plasma is surprisingly limited.

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 α1-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, 2 D 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.

Two-stage Off-Gel? isoelectric focusing: Protein followed by peptide fractionation and application to proteome analysis of human plasma

This paper presents the recently introduced Off-Gel electrophoresis (OGE) technology as a versatile tool to reproducibly fractionate intact proteins and peptides into discrete liquid fractions. The coupling of two stages of OGE, i.e., the separation of intact proteins in a first-stage followed by fractionation of peptides derived from each protein fraction after proteolysis in a second stage, results in an array of 15 x 15 fractions that are directly amenable to additional peptide fractionation like reverse-phase liquid chromatography (RPC). The analysis of all second-stage peptide fractions from only the first-stage protein fraction representing pH 5.0 -5.15 by on-line reverse-phase LC-tandem mass spectrometry resulted in the identification of 53 proteins (337 peptides), of which 10 were on different immunoglobulin (Ig) chains, with an input of only 1.5 mg human blood plasma proteins. Increasing the protein load to approximately 12 mg increased the number of identified proteins in the same protein fraction to 73 proteins (449 peptides), of which 15 were Ig-related. Immunodepletion of six of the most abundant proteins (albumin, transferrin, haptoglobin, IgG, IgA, and alpha-1-antitrypsin) prior to first-stage OGE with an input of 1.5 mg of protein (equivalent to approximately 10 mg nondepleted plasma) resulted in the identification of 81 proteins (660 peptides), of which three were still Ig fragments. The pI-based separation of peptides appears to be nonuniform based on the theoretically determined pI values of identified peptides. This observation specifically accounts for the neutral zone (pI 5-8) and can be accounted for by the physicochemical properties of the peptides given by their amino acid composition. The power of OGE separation of proteins and peptides is discussed with a focus on the use of the knowledge about the pI of proteins and peptides that assist the validation of correct identifications together with the retention time of peptides on RPC.