Affinity enrichment for mass spectrometry: improving the yield of low abundance biomarkers

Introduction: Mass spectrometry (MS) is the premier tool for discovering novel disease-associated protein biomarkers. Unfortunately, when applied to complex body fluid samples, MS has poor sensitivity for the detection of low abundance biomarkers (?10 ng/mL), derived directly from the diseased tissue cells or pathogens.

Areas covered: Herein we discuss the strengths and drawbacks of technologies used to concentrate low abundance analytes in body fluids, with the aim to improve the effective sensitivity for MS discovery. Solvent removal by dry-down or dialysis, and immune-depletion of high abundance serum or plasma proteins, is shown to have disadvantages compared to positive selection of the candidate biomarkers by affinity enrichment. A theoretical analysis of affinity enrichment reveals that the yield for low abundance biomarkers is a direct function of the binding affinity (Association/Dissociation rates) used for biomarker capture. In addition, a high affinity capture pre processing step can effectively dissociate the candidate biomarker from partitioning with high abundance proteins such as albumin.

Expert commentary: Properly designed high affinity capture materials can enrich the yield of low abundance (0.1–10 picograms/mL) candidate biomarkers for MS detection. Affinity capture and concentration, as an upfront step in sample preparation for MS, combined with MS advances in software and hardware that improve the resolution of the chromatographic separation can yield a transformative new class of low abundance biomarkers predicting disease risk or disease latency.     

Evaluation of multiprotein immunoaffinity subtraction for plasma proteomics and candidate biomarker discovery using mass spectrometry

Strategies for removal of high abundance proteins have been increasingly utilized in proteomic studies of serum/plasma and other body fluids to enhance the detection of low abundance proteins and achieve broader proteome coverage; however, both the reproducibility and specificity of the high abundance protein depletion process still represent common concerns. Here we report a detailed evaluation of immunoaffinity subtraction performed applying the ProteomeLab IgY-12 system that is commonly used in human serum/plasma proteome characterization in combination with high resolution LC-MS/MS. Plasma samples were repeatedly processed using this approach, and the resulting flow-through fractions and bound fractions were individually analyzed for comparison. The removal of target proteins by the immunoaffinity subtraction system and the overall process was highly reproducible. Non-target proteins, including one spiked protein standard (rabbit glyceraldehyde-3-phosphate dehydrogenase), were also observed to bind to the column at different levels but also in a reproducible manner. The results suggest that multiprotein immunoaffinity subtraction systems can be readily integrated into quantitative strategies to enhance detection of low abundance proteins in biomarker discovery studies.     

A new versatile peptide-based size exclusion chromatography platform for global profiling and quantitation of candidate biomarkers in hepatocellular carcinoma specimens

Disease biomarkers are predicted to be in low abundance; thus, the most crucial step of biomarker discovery is the efficient fractionation of clinical samples into protein sets that define disease stages and/or predict disease development. For this purpose, we developed a new platform that uses peptide-based size exclusion chromatography (pep-SEC) to quantify disease biomarker candidates. This new platform has many advantages over previously described biomarker profiling platforms, including short run time, high resolution, and good reproducibility, which make it suitable for large-scale analysis. We combined this platform with isotope labeling and label-free methods to identify and quantitate differentially expressed proteins in hepatocellular carcinoma (HCC) tissues. When we combined pep-SEC with a gas phase fractionation method, which broadens precursor ion selection, the protein coverage was significantly increased, which is critical for the global profiling of HCC specimens. Furthermore, pep-SEC-LC-MS/MS analysis enhanced the detection of low-abundance proteins (e.g. insulin receptor substrate 2 and carboxylesterase 1) and glycopeptides in HCC plasma. Thus, our pep-SEC platform is an efficient and versatile pre-fractionation system for the large-scale profiling and quantitation of candidate biomarkers in complex disease proteomes.     

Utilizing human blood plasma for proteomic biomarker discovery

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.     

High dynamic range characterization of the trauma patient plasma proteome

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this "divide-and-conquer" strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3,654 different proteins with 1,494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2,910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1,553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.     

Reduced plasma APOA1 level is associated with Gastric Tumor Growth in MKN45 mouse xenograft model

There is no suitable diagnostic and prognostic biomarker for gastric cancer. The biggest hurdles in biomarker discovery are (i) the low abundance of cancer cell-specific proteins that limits their detection and (ii) complex inter-patient variations that complicate the discovery process. To circumvent these issues, we conducted proteomics on the plasma of gastric cancer mouse xenograft and attempted to identify proteins released by cancer cells. MKN45 gastric cancer cells were subcutaneously implanted into immune-incompetent nude mice. Plasma samples collected from mice with different tumor sizes (low, mid and high tumor loads) were subjected to iTRAQ and mass spectrometric analyses. Detection of human APOA1 in mouse plasma was verified and its expression level was shown to be lower in mice with large tumors compared to those with small tumors. Studies on a panel of about 14 gastric cancer cell lines supported the notion that APOA1 in mouse plasma was of human gastric cancer cell origin. While the clinical utility of APOA1 remains to be ascertained with a larger scale study, the current work supported the feasibility of using mouse xenograft model for gastric cancer biomarker discovery.     

A systematic analysis of eluted fraction of plasma post immunoaffinity depletion: implications in biomarker discovery

Plasma is the most easily accessible source for biomarker discovery in clinical proteomics. However, identifying potential biomarkers from plasma is a challenge given the large dynamic range of proteins. The potential biomarkers in plasma are generally present at very low abundance levels and hence identification of these low abundance proteins necessitates the depletion of highly abundant proteins. Sample pre-fractionation using immuno-depletion of high abundance proteins using multi-affinity removal system (MARS) has been a popular method to deplete multiple high abundance proteins. However, depletion of these abundant proteins can result in concomitant removal of low abundant proteins. Although there are some reports suggesting the removal of non-targeted proteins, the predominant view is that number of such proteins is small. In this study, we identified proteins that are removed along with the targeted high abundant proteins. Three plasma samples were depleted using each of the three MARS (Hu-6, Hu-14 and Proteoprep 20) cartridges. The affinity bound fractions were subjected to gelC-MS using an LTQ-Orbitrap instrument. Using four database search algorithms including MassWiz (developed in house), we selected the peptides identified at <1% FDR. Peptides identified by at least two algorithms were selected for protein identification. After this rigorous bioinformatics analysis, we identified 101 proteins with high confidence. Thus, we believe that for biomarker discovery and proper quantitation of proteins, it might be better to study both bound and depleted fractions from any MARS depleted plasma sample.     

Evaluation of the combination of bead technology with SELDI-TOF-MS and 2-D DIGE for detection of plasma proteins

Today biomarker discovery is one of the most active aspects of proteomic investigations. However, the wide dynamic range of plasma proteins makes the analysis very challenging because high abundance proteins tend to mask those of lower abundance. Using a large bead-based library of combinatorial peptide ligands (Equalizer beads or ProteoMiner), the dynamic range of the protein concentration is compressed, the high abundance proteins present in the sample are reduced and the low abundance proteins are enriched, while retaining representatives of all proteins within the sample. In the present study, the combination of beads with surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and two-dimensional differential gel electrophoresis (2-D DIGE) technology were evaluated considering efficiency, reproducibility, sensitivity, and compatibility. The bead technology is easily compatible with both SELDI-TOF-MS and 2-D DIGE and the samples can be analyzed directly without any processing of the sample. The use of the beads prior SELDI-TOF-MS and 2-D DIGE enabled detection of many new protein spots/peaks and increased resolution and improved intensity of low abundance proteins in a reproducible fashion compared with the depletion technique. Several proteins have been identified by the combination of beads, 2-D DIGE and MS for example different kinds of complement factors and cytoskeletal proteins. Our data suggest that integration of the bead technology with our current proteomic technologies will enhance the possibility to deliver new peptide/protein biomarker candidates in our projects.     

Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples

The field of extracellular vesicle (EV) research has rapidly expanded in recent years, with particular interest in their potential as circulating biomarkers. Proteomic analysis of EVs from clinical samples is complicated by the low abundance of EV proteins relative to highly abundant circulating proteins such as albumin and apolipoproteins. To overcome this, size exclusion chromatography (SEC) has been proposed as a method to enrich EVs whilst depleting protein contaminants; however, the optimal SEC parameters for EV proteomics have not been thoroughly investigated. Here, quantitative evaluation and optimization of SEC are reported for separating EVs from contaminating proteins. Using a synthetic model system followed by cell line‐derived EVs, it is found that a 10 mL Sepharose 4B column in PBS produces optimal resolution of EVs from background protein. By spiking‐in cancer cell‐derived EVs to healthy plasma, it is shown that some cancer EV‐associated proteins are detectable by nano‐LC‐MS/MS when as little as 1% of the total plasma EV number are derived from a cancer cell line. These results suggest that an optimized SEC and nanoLC‐MS/MS workflow may be sufficiently sensitive for disease EV protein biomarker discovery from patient‐derived clinical samples.     

Development of quantitative plasma N-glycoproteomics using label-free 2-D LC-MALDI MS and its applicability for biomarker discovery in hepatocellular carcinoma

There has been rapid progress in the development of clinical proteomic methodologies with improvements in mass spectrometric technologies and bioinformatics, leading to many new methodologies for biomarker discovery from human plasma. However, it is not easy to find new biomarkers because of the wide dynamic range of plasma proteins and the need for their quantification. Here, we report a new methodology for relative quantitative proteomic analysis combining large-scale glycoproteomics with label-free 2-D LC-MALDI MS. In this method, enrichment of glycopeptides using hydrazide resin enables focusing on plasma proteins with lower abundance corresponding to the tissue leakage region. On quantitative analysis, signal intensities by 2-D LC-MALDI MS were normalized using a peptide internal control, and the values linked to LC data were treated with DeView? software. Our proteomic method revealed that the quantitative dynamic ranged from 102 to 10? pg/mL of plasma proteins with good reproducibility, and the limit of detection was of the order of a few ng/mL of proteins in biological samples. To evaluate the applicability of our method for biomarker discovery, we performed a feasibility study using plasma samples from patients with hepatocellular carcinoma, and identified biomarker candidates, including ceruloplasmin, alpha-1 antichymotrypsin, and multimerin-1.     

Enhanced detection of low abundance human plasma proteins using a tandem IgY12-SuperMix immunoaffinity separation strategy

The enormous dynamic range of human bodily fluid proteomes poses a significant challenge for current MS-based proteomics technologies as it makes it especially difficult to detect low abundance proteins in human biofluids such as blood plasma, which is an essential aspect for successful biomarker discovery efforts. Here we present a novel tandem IgY12-SuperMix immunoaffinity separation system for enhanced detection of low abundance proteins in human plasma. The tandem IgY12-SuperMix system separates approximately 60 abundant proteins from the low abundance proteins in plasma, allowing for significant enrichment of low abundance plasma proteins in the SuperMix flow-through fraction. High reproducibility of the tandem separations was observed in terms of both sample processing recovery and LC-MS/MS identification results based on spectral count data. The ability to quantitatively measure differential protein abundances following application of the tandem separations was demonstrated by spiking six non-human standard proteins at three different levels into plasma. A side-by-side comparison between the SuperMix flow-through and IgY12 flow-through samples analyzed by both one- and two-dimensional LC-MS/MS revealed a 60-80% increase in proteome coverage as a result of the SuperMix separations, suggesting significantly enhanced detection of low abundance proteins. A total of 695 plasma proteins were confidently identified in a single analysis (with a minimum of two peptides per protein) by coupling the tandem separation strategy with two-dimensional LC-MS/MS, including 42 proteins with reported normal concentrations of approximately 100 pg/ml to 100 ng/ml. The concentrations of two selected proteins, macrophage colony-stimulating factor 1 and matrix metalloproteinase-8, were independently validated by ELISA as 202 pg/ml and 12.4 ng/ml, respectively. Evaluation of binding efficiency revealed that 45 medium abundance proteins were efficiently captured by the SuperMix column with >90% retention. Taken together, these results illustrate the potential broad utilities of this tandem IgY12-SuperMix strategy for proteomics applications involving human biofluids where effectively addressing the dynamic range challenge of the specimen is imperative.     

Different immunoaffinity fractionation strategies to characterize the human plasma proteome

Plasma proteins may often serve as indicators of disease and are a rich source for biomarker discovery. However, the intrinsic large dynamic range of plasma proteins makes the analysis very challenging because a large number of low abundance proteins are often masked by a few high abundance proteins. The use of prefractionation methods, such as depletion of higher abundance proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may ultimately prove to be informative biomarkers. But there are few studies on comprehensive investigation of the proteins both in the fractions depleted and remainder. In the present study, two different immunoaffinity fractionation columns for the top-6 or the top-12 proteins in plasma were investigated and both the proteins in column-bound and flow-through fractions were subsequently analyzed. A two-dimensional peptide separation strategy, utilizing chromatographic separation techniques, combined with tandem mass spectrometry (MS/MS) was employed for proteomic analysis of the four fractions. Using the established HUPO PPP criteria, a total of 2401 unique plasma proteins were identified. The Multiple Affinity Removal System yielded 921 and 725 unique proteins from the flow-through and bound fractions, respectively, whereas the Seppro MIXED 12 column yielded identification of 897 and 730 unique proteins from the flow-through and bound fractions, respectively. When more stringent criteria, based on searching against the reversed database, were implemented, 529 unique proteins were identified from the four fractions with the confidence in peptide identification increased from 73.6% to 99%. To determine whether the presence of nontarget proteins in the immunoaffinity-bound fraction could be attributed to their interaction with high abundance proteins, co-immunoprecipitation analysis with an antibody to human plasma albumin was performed, which resulted in an identification of 40 unique proteins from the coimmunoprecipitate with the more stringent criteria. This study illustrated that combining the column-bound and flow-through fractions from immunoaffinity separation affords more extensive profiling of the protein content of human plasma. The presence of nontarget proteins in the column-bound fractions may be induced by their binding to the higher abundance proteins targeted by the immunoaffinity column.     

A quantitative proteomic approach to prion disease biomarker research: delving into the glycoproteome

Mass spectrometry (MS) -- based proteomic approaches have evolved as powerful tools for the discovery of biomarkers. However, the identification of potential protein biomarkers from biofluid samples is challenging because of the limited dynamic range of detection. Currently there is a lack of sensitive and reliable premortem diagnostic test for prion diseases. Here, we describe the use of a combined MS-based approach for biomarker discovery in prion diseases from mouse plasma samples. To overcome the limited dynamic range of detection and sample complexity of plasma samples, we used lectin affinity chromatography and multidimensional separations to enrich and isolate glycoproteins at low abundance. Relative quantitation of a panel of proteins was obtained by a combination of isotopic labeling and validated by spectral counting. Overall 708 proteins were identified, 53 of which showed more than 2-fold increase in concentration whereas 58 exhibited more than 2-fold decrease. A few of the potential candidate markers were previously associated with prion or other neurodegenerative diseases.     

The effect of pre-analytical variability on the measurement of MRM-MS-based mid- to high-abundance plasma protein biomarkers and a panel of cytokines

Blood sample processing and handling can have a significant impact on the stability and levels of proteins measured in biomarker studies. Such pre-analytical variability needs to be well understood in the context of the different proteomics platforms available for biomarker discovery and validation. In the present study we evaluated different types of blood collection tubes including the BD P100 tube containing protease inhibitors as well as CTAD tubes, which prevent platelet activation. We studied the effect of different processing protocols as well as delays in tube processing on the levels of 55 mid and high abundance plasma proteins using novel multiple-reaction monitoring-mass spectrometry (MRM-MS) assays as well as 27 low abundance cytokines using a commercially available multiplexed bead-based immunoassay. The use of P100 tubes containing protease inhibitors only conferred proteolytic protection for 4 cytokines and only one MRM-MS-measured peptide. Mid and high abundance proteins measured by MRM are highly stable in plasma left unprocessed for up to six hours although platelet activation can also impact the levels of these proteins. The levels of cytokines were elevated when tubes were centrifuged at cold temperature, while low levels were detected when samples were collected in CTAD tubes. Delays in centrifugation also had an impact on the levels of cytokines measured depending on the type of collection tube used. Our findings can help in the development of guidelines for blood collection and processing for proteomic biomarker studies.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification

Verification of candidate biomarker proteins in blood is typically done using multiple reaction monitoring (MRM) of peptides by LC-MS/MS on triple quadrupole MS systems. MRM assay development for each protein requires significant time and cost, much of which is likely to be of little value if the candidate biomarker is below the detection limit in blood or a false positive in the original discovery data. Here we present a new technology, accurate inclusion mass screening (AIMS), designed to provide a bridge from unbiased discovery to MS-based targeted assay development. Masses on the software inclusion list are monitored in each scan on the Orbitrap MS system, and MS/MS spectra for sequence confirmation are acquired only when a peptide from the list is detected with both the correct accurate mass and charge state. The AIMS experiment confirms that a given peptide (and thus the protein from which it is derived) is present in the plasma. Throughput of the method is sufficient to qualify up to a hundred proteins/week. The sensitivity of AIMS is similar to MRM on a triple quadrupole MS system using optimized sample preparation methods (low tens of ng/ml in plasma), and MS/MS data from the AIMS experiments on the Orbitrap can be directly used to configure MRM assays. The method was shown to be at least 4-fold more efficient at detecting peptides of interest than undirected LC-MS/MS experiments using the same instrumentation, and relative quantitation information can be obtained by AIMS in case versus control experiments. Detection by AIMS ensures that a quantitative MRM-based assay can be configured for that protein. The method has the potential to qualify large number of biomarker candidates based on their detection in plasma prior to committing to the time- and resource-intensive steps of establishing a quantitative assay.     

Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

The amplified peptidome: the new treasure chest of candidate biomarkers

Mass spectrometric analysis of the low-molecular weight (LMW) range of the serum/plasma proteome is revealing the existence of large numbers of previously unknown peptides and protein fragments predicted to be derived from low-abundance proteins. This raises the question of why such low abundance molecules would be retained at detectable levels in the circulation, instead of being rapidly cleared and excreted. Theoretical models of biomarker production and association with serum carrier proteins have been developed to elucidate the mechanisms governing biomarker half-life in the bloodstream. These models predict that the vast majority of LMW biomarkers exist in association with circulating high molecular mass carrier proteins. Moreover, the total serum/plasma concentration of the biomarker is largely determined by the clearance rate of the carrier protein, not the free-phase biomarker clearance itself. These predictions have been verified experimentally using molecular mass fractionation of human serum before mass spectrometry sequence analysis. These principles have profound implications for biomarker discovery and measurement.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Combination of abundant protein depletion and multi-lectin affinity chromatography (M-LAC) for plasma protein biomarker discovery

We report on the development of a robust and relatively high-throughput method for in-depth proteomic analysis of human plasma suitable for biomarker discovery. The method consists of depletion of albumin and IgG and multi-lectin affinity chromatography (M-LAC), followed by nanoLC-MS/MS analysis of digested proteins and label-free comparative quantitation of proteins. The performance of the method is monitored by multiple quality control points to ensure reproducibility of the analysis. The method identifies proteins that are reported to be present in normal plasma at concentrations of 10-100 ng/mL and that may be of particular interest when studying a variety of disease conditions. Numerous tissue leakage proteins of potentially even lower concentrations are also identified. When the method was used in a study to identify potential biomarkers of psoriasis, the differential abundance of proteins present at low mug/mL level was quantitated and later verified by ELISA measurements.