Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Relative Quantification of Proteins in Formalin-Fixed Paraffin-Embedded Breast Cancer Tissue Using Multiplexed Mass Spectrometry Assays

The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative, 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched immunohistochemistry or chromogenic in situ hybridization data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in triple-negative breast cancer samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.     

Systematic discovery of ectopic pregnancy serum biomarkers using 3-D protein profiling coupled with label-free quantitation

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

Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted "-omics" approaches are underutilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program, with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. alpha-2-Glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples.     

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.     

In-source decay causes artifacts in SELDI-TOF MS spectra

SELDI-TOF MS is a mass spectrometric technique which has been extensively used for biomarker discovery. In this study, we show that in-source decay is an important source for the generation of additional spectral peaks with this technique, both for pure proteins and proteins in serum samples. Thus, SELDI-TOF MS could be used to gain sequence information from proteins, but the results also question the uncritical use of SELDI-TOF MS as a general method for the detection of biomarkers.     

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 advantage of laser‐capture microdissection over whole tissue analysis in proteomic profiling studies

Laser‐capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label‐free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p‐value < 0.001). 2D analysis on co‐expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 ( http://proteomecentral.proteomexchange.org/dataset/PXD002381 ).     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Comparison of proteomes in various human plasma preparations by two-dimensional gel electrophoresis

Serum or plasma can be utilized in a variety of studies targeted toward the discovery of disease biomarkers. In this study, the proteome profiles of plasma samples prepared using various anticoagulants (EDTA, heparin or citrate), were compared with those of serum using two-dimensional electrophoresis (2-DE). Proteins which evidenced different levels in the plasma and serum were screened and identified using ESI-Q-TOF MS/MS. The proteins which became detectable after the removal of fibrinogen from serum were identified as pigment epithelial differentiating factor (four spots), fetuin-like protein, and the hemopexin precursor. In particular, three proteins, pre-serum amyloid P component, plasma glutathione peroxidase precursor, and tetranectin, evidenced increased volume intensity only in the plasma samples prepared with EDTA.     

细菌性脑膜炎大鼠模型的尿液蛋白质组学变化

与脑脊液和血液不同,尿液不受到稳态机制的调节,更倾向于积累和反应机体生理和病理状态下的变化。生物标志物的本质特征是变化,因此尿液是寻找疾病早期标志物的更好来源。在世界范围内,细菌性脑膜炎依然是引起新生儿和儿童疾病的主要原因。为了降低死亡率和致残率,需要用无创的方法寻找细菌性脑膜炎的相关线索。本研究中,使用大鼠脑室内注射大肠杆菌模型模拟细菌性脑膜炎,在第1天和第3天的大鼠尿液中寻找尿液蛋白谱的差异变化,为进一步寻找大肠杆菌性脑膜炎的早期生物标记物研究进行初步的探索。通过膜上酶切和胶内酶切将尿蛋白切成肽段并通过液相色谱串联质谱技术(LC-MS/MS)分析肽段信息。第1天的尿液通过胶内酶切方法鉴定到17个差异蛋白,通过膜上酶切鉴定到20个差异蛋白;第3天的尿液通过膜上酶切方法鉴定到5个差异蛋白。这些差异蛋白为寻找细菌性脑膜炎早期生物标志物的初步探索奠定了基础。     

Comparison of proteomes in various human plasma preparations by two-dimensional gel electrophoresis

Serum or plasma can be utilized in a variety of studies targeted toward the discovery of disease biomarkers. In this study, the proteome profiles of plasma samples prepared using various anticoagulants (EDTA, heparin or citrate), were compared with those of serum using two-dimensional electrophoresis (2-DE). Proteins which evidenced different levels in the plasma and serum were screened and identified using ESI-Q-TOF MS/MS. The proteins which became detectable after the removal of fibrinogen from serum were identified as pigment epithelial differentiating factor (four spots), fetuin-like protein, and the hemopexin precursor. In particular, three proteins, pre-serum amyloid P component, plasma glutathione peroxidase precursor, and tetranectin, evidenced increased volume intensity only in the plasma samples prepared with EDTA.     

Preliminary study of the urinary proteome in Li and Han ethnic individuals from Hainan

Biomarkers indicate changes associated with disease. Blood is relatively stable due to the homeostatic mechanisms of the body;however, urine accumulates metabolites from changes in the body, making it a better source for early biomarker discovery. The Li ethnic group is a unique minority ethnic group that has only lived on Hainan Island for approximately 5,000 years. Studies have shown that various specific genetic variations are different between the Li and Han ethnic groups. However, whether the urinary proteome between these two ethnic groups is significantly different remains unknown. In this study, differential urinary proteins were identified in the Li and Han ethnic groups using liquid chromatography tandem mass spectrometry(LC-MS/MS).In total, 1,555 urinary proteins were identified. Twenty-five of the urinary proteins were statistically significantly different, 16 of which have been previously reported to be biomarkers of many diseases, and that these significantly different proteins were caused by ethnic differences rather than random differences. Ethnic group differences may be an influencing factor in urine proteome studies and should be considered when human urine samples are used for biomarker discovery.     

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.     

Shotgun proteomics of human bile in hilar cholangiocarcinoma

The need to find biomarkers for hepatobiliary diseases including cholangiocarcinoma (CCA) has led to an interest in using bile as a proximal fluid in biomarker discovery experiments, although there are inherent challenges both in its acquisition and analysis. The study described here greatly extends previous studies that have started to characterise the bile proteome. Bile from four patients with hilar CCA was depleted of albumin and immunoglobulin G and analysed by GeLC-MS/MS. The number of proteins identified per bile sample was between 378 and 741. Overall, the products of 813 unique genes were identified, considerably extending current knowledge of the malignant bile proteome. Of these, 268 were present in at least 3 out of 4 patients. This data set represents the largest catalogue of bile proteins to date and together with other studies in the literature constitutes an important prelude to the potential promise of expression proteomics and subsequent validation studies in CCA biomarker discovery.     

Using proteomic approach to identify tumor-associated proteins as biomarkers in human esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in China. The lower survival rate of ESCC is attributed to late diagnosis and poor therapeutic efficacy; therefore, the identification of tumor-associated proteins as biomarkers for early diagnosis, and the discovery of novel targets for therapeutic intervention, seems very important for increasing the survival rate of ESCC. To identify tumor-associated proteins as biomarkers in ESCC, we have analyzed ESCC tissues and adjacent normal tissues by two-dimensional electrophoresis (2DE) and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The results showed that a total of 104 protein spots with different expression levels were found on 2DE, and 47 proteins were eventually identified by MALDI-TOF MS. Among these identified proteins, 33 proteins including keratin 17 (KRT17), biliverdin reductase B (BLVRB), proteasome activator subunit 1 (PSME1), manganese superoxide dismutase (MnSOD), high-mobility group box-1(HMGB1), heat shock protein 70 (HSP70), peroxiredoxin (PRDX1), keratin 13 (KRT13), and so on were overexpressed, and 14 proteins including cystatin B (CSTB), tropomyosin 2 (TPM2), annexin 1 (ANX1), transgelin (TAGLN), keratin 19 (KRT19), stratifin (SFN), and so on were down-expressed in ESCC. Biological functions of these proteins are associated with cell proliferation, cell motility, protein folding, oxidative stress, and signal transduction. In the subsequent study using immunoassay on ESCC serum samples and tissue-array slides, two representative proteins, HSP70 and HMGB1, were selected as examples for the purpose of validation. The results showed that both HSP70 and HMGB1 can induce autoantibody response in ESCC sera and have higher expression in ESCC tissues. Especially, the frequency of antibodies to HSP70 in ESCC sera was significantly higher than that in normal human sera. The preliminary results suggest that some of these identified proteins might contribute to esophageal cell differentiation and carcinogenesis, certain proteins could be used as tumor-associated antigen (TAA) biomarkers in cancer diagnosis, and further studies on these identified proteins should provide more evidence of how these proteins are involved in carcinogenesis of ESCC.     

Quantification of Cardiovascular Biomarkers in Patient Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution

Verification of candidate biomarkers requires specific assays to selectively detect and quantify target proteins in accessible biofluids. The primary objective of verification is to screen potential biomarkers to ensure that only the highest quality candidates from the discovery phase are taken forward into preclinical validation. Because antibody reagents for a clinical grade immunoassay often exist for a small number of candidates, alternative methodologies are required to credential new and unproven candidates in a statistically viable number of serum or plasma samples. Using multiple reaction monitoring coupled with stable isotope dilution MS, we developed quantitative, multiplexed assays in plasma for six proteins of clinical relevance to cardiac injury. The process described does not require antibodies for immunoaffinity enrichment of either proteins or peptides. Limits of detection and quantitation for each signature peptide used as surrogates for the target proteins were determined by the method of standard addition using synthetic peptides and plasma from a healthy donor. Limits of quantitation ranged from 2 to 15 ng/ml for most of the target proteins. Quantitative measurements were obtained for one to two signature peptides derived from each target protein, including low abundance protein markers of cardiac injury in the nanogram/milliliter range such as the cardiac troponins. Intra- and interassay coefficients of variation were predominantly <10 and 25%, respectively. The configured multiplex assay was then used to measure levels of these proteins across three time points in six patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy. These results are the first demonstration of a multiplexed, MS-based assay for detection and quantification of changes in concentration of proteins associated with cardiac injury in the low nanogram/milliliter range. Our results also demonstrate that these assays retain the necessary precision, reproducibility, and sensitivity to be applied to novel and uncharacterized candidate biomarkers for verification of proteins in blood.Discovery of disease-specific biomarkers with diagnostic and prognostic utility has become an important challenge in clinical proteomics. In general, unbiased discovery experiments often result in the confident identification of thousands of proteins, hundreds of which may vary significantly between case and control samples in small discovery studies. However, because of the stochastic sampling of proteomes in discovery “omics” experiments, a large fraction of the protein biomarkers “discovered” in these experiments are false positives arising from biological or technical variability. Clearly discovery omics experiments do not lead to biomarkers of immediate clinical utility but rather produce candidates that must be qualified and verified in larger sample sets than were used for discovery (1).Traditional, clinical validation of biomarkers has relied primarily on immunoassays because of their specificity and sensitivity for the target analyte and high throughput capability. However, antibody reagents for a clinical grade immunoassay often only exist for a short list of candidates. The development of a reliable sandwich immunoassay for one target protein is expensive, has a long development time, and is dependent upon the generation of high quality protein antibodies. For the large majority of new, unproven candidate biomarkers, an intermediate verification technology is required that has shorter assay development time lines, lower assay cost, and effective multiplexing of dozens of candidates in low sample volumes. Ideally the approach should be capable of analyzing hundreds of samples of serum or plasma with good precision. The desired outcome of verification is a small number of highly credentialed candidates suitable for traditional preclinical and clinical validation studies.Multiple reaction monitoring (MRM)¹ coupled with stable isotope dilution (SID) MS has recently been shown to be well suited for direct quantification of proteins in plasma (2–4) and has emerged as the core technology for candidate biomarker verification. MRM assays can be highly multiplexed such that a moderate number of candidate proteins (in the range of 10–50) can be simultaneously targeted and measured in the statistically viable number of patient samples required for verification (hundreds of serum samples). However, sensitivity for unambiguous detection and quantification of proteins by MS-based assays is often constrained by sample complexity, particularly when the measurements are being made in complex fluids such as plasma.Many biomarkers of current clinical importance, such as prostate-specific antigen and the cardiac troponins, reside in the low nanogram/milliliter range in plasma and, until recently, have been inaccessible by non-antibody approaches. Our laboratory has recently shown for the first time that a combination of abundant protein depletion with limited fractionation at the peptide level prior to SID-MRM-MS provides robust limits of quantitation (LOQs) in the 1–20 ng/ml range with coefficient of variation (CV) of 10–20% at the LOQ for proteins in plasma (3).Here we demonstrate that this work flow can be extended to configure assays for a number of known markers of cardiovascular disease and, more importantly, can be deployed to measure their concentrations in clinical samples. We modeled a verification study comprising six patients undergoing alcohol septal ablation treatment for hypertrophic obstructive cardiomyopathy, a human model of “planned” myocardial infarction (PMI), and obtained targeted, quantitative measurements for moderate to low concentrations of cardiac biomarkers in plasma. This work provides additional evidence that MS-based assays can be configured and applied to verification of new protein targets for which high quality antibody reagents are not available.     

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.