iTRAQ experimental design for plasma biomarker discovery

There is considerable interest in using mass spectrometry for biomarker discovery in human blood plasma. We investigated aspects of experimental design for large studies that require analysis of multiple sample sets using iTRAQ reagents for sample multiplexing and quantitation. Immunodepleted plasma samples from healthy volunteers were compared to immunodepleted plasma from patients with osteoarthritis in eight separate iTRAQ experiments. Our analyses utilizing ProteinPilot software for peptide identification and quantitation showed that the methodology afforded excellent reproducibility from run to run for determining protein level ratios (coefficient of variation 11.7%), in spite of considerable quantitative variances observed between different peptides for a given protein. Peptides with high variances were associated with lower intensity iTRAQ reporter ions, while immunodepletion prior to sample analysis had a negligible affect on quantitative variance. We examined the influence of different reference samples, such as pooled samples or individual samples on calculating quantitative ratios. Our findings are discussed in the context of optimizing iTRAQ experimental design for robust plasma-based biomarker discovery.     

Analysis of Human Blood Plasma Proteome from Ten Healthy Volunteers from Indian Population

Analysis of any mammalian plasma proteome is a challenge, particularly by mass spectrometry, due to the presence of albumin and other abundant proteins which can mask the detection of low abundant proteins. As detection of human plasma proteins is valuable in diagnostics, exploring various workflows with minimal fractionation prior to mass spectral analysis, is required in order to study population diversity involving analysis in a large cohort of samples. Here, we used ‘reference plasma sample’, a pool of plasma from 10 healthy individuals from Indian population in the age group of 25–60 yrs including 5 males and 5 females. The 14 abundant proteins were immunodepleted from plasma and then evaluated by three different workflows for proteome analysis using a nanoflow reverse phase liquid chromatography system coupled to a LTQ Orbitrap Velos mass spectrometer. The analysis of reference plasma sample a) without prefractionation, b) after prefractionation at peptide level by strong cation exchange chromatography and c) after prefractionation at protein level by sodium dodecyl sulfate polyacrylamide gel electrophoresis, led to the identification of 194, 251 and 342 proteins respectively. Together, a comprehensive dataset of 517 unique proteins was achieved from all the three workflows, including 271 proteins with high confidence identified by≥2 unique peptides in any of the workflows or identified by single peptide in any of the two workflows. A total of 70 proteins were common in all the three workflows. Some of the proteins were unique to our study and could be specific to Indian population. The high-confidence dataset obtained from our study may be useful for studying the population diversity, in discovery and validation process for biomarker identification.     

Quantitative profiling of serum samples using TMT protein labelling, fractionation and LC-MS/MS

Blood-borne biomarkers are urgently required for the early detection, accurate diagnosis and prognosis of disease. Additionally, improved methods of profiling serum and plasma proteins for biomarker discovery efforts are needed. Herein, we report a quantitative method based on amino-group labelling of serum proteins (rather than peptides) with isobaric tandem mass tags (TMT) and incorporating immune-based depletion, gel-based and strong anion exchange separation of proteins prior to differential endoproteinase treatment and liquid chromatography tandem mass spectrometry. We report a generally higher level of quantitative coverage of the serum proteome compared to other peptide-based isobaric tagging approaches and show the potential of the method by applying it to a set of unique samples that pre-date the diagnosis of pancreatic cancer.     

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.     

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.     

Definitive Characterization of CA 19-9 in Resectable Pancreatic Cancer Using a Reference Set of Serum and Plasma Specimens

The validation of candidate biomarkers often is hampered by the lack of a reliable means of assessing and comparing performance. We present here a reference set of serum and plasma samples to facilitate the validation of biomarkers for resectable pancreatic cancer. The reference set includes a large cohort of stage I-II pancreatic cancer patients, recruited from 5 different institutions, and relevant control groups. We characterized the performance of the current best serological biomarker for pancreatic cancer, CA 19–9, using plasma samples from the reference set to provide a benchmark for future biomarker studies and to further our knowledge of CA 19–9 in early-stage pancreatic cancer and the control groups. CA 19–9 distinguished pancreatic cancers from the healthy and chronic pancreatitis groups with an average sensitivity and specificity of 70–74%, similar to previous studies using all stages of pancreatic cancer. Chronic pancreatitis patients did not show CA 19–9 elevations, but patients with benign biliary obstruction had elevations nearly as high as the cancer patients. We gained additional information about the biomarker by comparing two distinct assays. The two CA 9–9 assays agreed well in overall performance but diverged in measurements of individual samples, potentially due to subtle differences in antibody specificity as revealed by glycan array analysis. Thus, the reference set promises be a valuable resource for biomarker validation and comparison, and the CA 19–9 data presented here will be useful for benchmarking and for exploring relationships to CA 19–9.     

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.     

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 high-quality secretome of A549 cells aided the discovery of C4b-binding protein as a novel serum biomarker for non-small cell lung cancer

Cancer secretomes are a promising source for biomarker discovery. The analysis of cancer secretomes still faces some difficulties mainly related to the intracellular contamination, which hinders the qualification and follow-up validations. This study aimed to establish a high-quality secretome of A549 cells by using the cellular proteome as a reference and to test the merits of this refined secretome for biomarker discovery for non-small cell lung cancer (NSCLC). Using one-dimensional gel electrophoresis followed by liquid-chromatography tandem mass spectrometry, we comprehensively investigated the secretome and the concurrent cellular proteome of A549 cells. A high-quality secretome consisting of 382 proteins was refined from 889 initial secretory proteins. More than 85.3% of proteins were annotated as secreted and 76.8% as extracellular or membrane-bound. The discriminative power of the lung-cancer associated secretome was confirmed by gene expression and serum proteomic data. The elevated level of C4b-binding Protein (C4BP) in NSCLC blood was verified by enzyme-linked immunosorbent assays (ELISA, p = 6.07e-6). Moreover, the serum C4BP level in 89 patients showed a strong association with the clinical staging of NSCLC. Our reference-experiment-driven strategy is simple and widely applicable, and may facilitate the identification of novel promising biomarkers of lung cancer.     

Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ～ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.     

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.     

Differential Plasma Glycoproteome of p19ARF Skin Cancer Mouse Model Using the Corra Label-Free LC-MS Proteomics Platform

Introduction

A proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented in this paper, using a mouse model for skin cancer as an example.

Materials and Methods

Blood plasma was collected from ten control mice and ten mice having a mutation in the p19^ARF gene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists.

Results and Discussions

We assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins are also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localization, transport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application.

Conclusion

These results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.     

Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics

Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel‐based and gel‐free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D‐DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM‐based validation in a separate cohort testified a panel of 21 proteins such as zinc‐alpha2‐glycoprotein, A2GL, retinol‐binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1‐antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.     

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.     

Metabolite profiling of blood plasma of patients with prostate cancer

Prostate cancer is one of the most common types of cancer in men. It is though extremely important to search for specific markers including metabolites, which concentration in blood could be a diagnostic measure. In this regard, the metabolite profiling of blood plasma was performed with two groups of people: healthy volunteers (n = 30) and patients with prostate cancer, second stage (n = 40). The profiling protocol included proteins removal from blood plasma with methanol and direct analysis of metabolite fractions by mass spectrometry. Identification of the most abundant metabolites in samples was performed using an accurate mass tag and an isotope pattern methods. Cancer-specific metabolites were revealed by statistical analysis of metabolite intensities in the mass spectra. Six different metabolites were found to be cancer-specific. Two metabolites, acylcarnitine and arachidonoyl amine, have the AUC 0.97 and 0.86, respectively, which are higher than those from PSA test, 0.59.     

Interindividual Variation in the Proteome of Human Peripheral Blood Mononuclear Cells

Peripheral blood mononuclear cells (PBMCs) are main actors in inflammatory processes and linked to many diseases, including rheumatoid arthritis, atherosclerosis, asthma, HIV and cancer. Moreover, they seem an interesting ‘surrogate tissue’ that can be used in biomarker discovery. In order to get a good experimental design for quantitative expression studies, the knowledge of the interindividual variation is an essential part. Therefore, PBMCs were isolated from 24 healthy volunteers (15 males, 9 females, ages 63–86) with no clinical signs of inflammation. The extracted proteins were separated using the two dimensional difference in gel electrophoresis technology (2D-DIGE), and the gel images were processed with the DeCyder 2D software. Protein spots present in at least 22 out of 24 healthy volunteers were selected for further statistical analysis. Determination of the coefficient of variation (CV) of the normalized spot volume values of these proteins, reveals that the total variation of the PBMC proteome varies between 12,99% to 148,45%, with a mean value of 28%. A supplemental look at the causes of technical variation showed that the isolation of PBMCs from whole blood is the factor which influences the experimental variance the most. This isolation should be handled with extra care and an additional washing step would be beneficial. Knowing the extent of variation, we show that at least 10 independent samples per group are needed to obtain statistical powerful data. This study demonstrates the importance of considering variance of a human population for a good experimental design for future protein profiling or biomarker studies.     

Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Background

The complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery.

Methodology/Principal Findings

We investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease.

Conclusions/Significance

Integrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.     

Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports, and pattern recognition software for profiling potential biomarkers in human plasma.

The advantage of using proteins and peptides as biomarkers is that they can be found readily in blood, urine, and other biological fluids. Such sample types are easily obtained and represent a potentially rich palette of biologically informative molecules. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) represents a key tool for rapidly interrogating such sample types. The goal of clinical proteomics is to harness the power of this tool for identifying novel, condition-specific protein fingerprints that may, in turn, lead to the elucidation and use of diseasespecific biomarkers that may be used to diagnose disease as well as to evaluate disease severity, disease progression, and intervention efficacy. Here we have evaluated a simple, affordable bench-top MALDI-TOF mass spectrometer to generate protein profiles from human plasma samples of asthma patients and healthy individuals. We achieve this profiling by using C8-functionalized magnetic beads that enrich a specific subset of plasma proteins based on their absorption by this resin. This step is followed by elution, transfer onto a prestructured sample support (AnchorChip technology), and analysis in a bench-top MALDI-TOF mass spectrometer (OmniFLEX) with AutoXecute acquisition control which enables automated operation with reproducible results. Resulting spectra are compiled and analyzed through the pattern recognition component of ClinProTools software. This approach in combination with ClinProTools software permits the investigator to rapidly scan for potential biomarker peptides/proteins in human plasma. The reproducibility of plasma profiles within and between days has been evaluated. The results show that the novel and facile approach with manual magnetic-bead sample preparation and a low-cost bench-top MALDI-TOF mass spectrometer is suitable for preliminary biomarker discovery studies.     

High quality catalog of proteotypic peptides from human heart

Proteomics research is beginning to expand beyond the more traditional shotgun analysis of protein mixtures to include targeted analyses of specific proteins using mass spectrometry. Integral to the development of a robust assay based on targeted mass spectrometry is prior knowledge of which peptides provide an accurate and sensitive proxy of the originating gene product (i.e., proteotypic peptides). To develop a catalog of "proteotypic peptides" in human heart, TRIzol extracts of left-ventricular tissue from nonfailing and failing human heart explants were optimized for shotgun proteomic analysis using Multidimensional Protein Identification Technology (MudPIT). Ten replicate MudPIT analyses were performed on each tissue sample and resulted in the identification of 30 605 unique peptides with a q-value < or = 0.01, corresponding to 7138 unique human heart proteins. Experimental observation frequencies were assessed and used to select over 4476 proteotypic peptides for 2558 heart proteins. This human cardiac data set can serve as a public reference to guide the selection of proteotypic peptides for future targeted mass spectrometry experiments monitoring potential protein biomarkers of human heart diseases.     

Plasma proteomics of lung cancer by a linkage of multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis

To investigate aberrant plasma proteins in lung cancer, we compared the proteomic profiles of serum from five lung cancer patients and from four healthy volunteers. Immuno-affinity chromatography was used to deplete highly abundant plasma proteins, and the resulting plasma samples were separated into eight fractions by anion-exchange chromatography. Quantitative protein profiles of the fractionated samples were generated by two-dimensional difference gel electrophoresis, in which the experimental samples and the internal control samples were labeled with different dyes and co-separated by two-dimensional polyacrylamide gel electrophoresis. This approach succeeded in resolving 3890 protein spots. For 364 of the protein spots, the expression level in lung cancer was more than twofold different from that in the healthy volunteers. These differences were statistically significant (Student's t-test, p-value less than 0.05). Mass spectrometric protein identification revealed that the 364 protein spots corresponded to 58 gene products, including the classical plasma proteins and the tissue-leakage proteins catalase, clusterin, ficolin, gelsolin, lumican, tetranectin, triosephosphate isomerase and vitronectin. The combination of multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis provides a valuable tool for serum proteomics in lung cancer.