Progressive degradation of serum samples limits proteomic biomarker discovery

Preanalytical variables play a key role in discovery of biomarkers. Although the effect of several preanalytical variables on the mass spectral profiles has been studied extensively, little is known about long-term storage of serum samples. This is important because samples used in case-control or epidemiological studies are usually stored for a long time before analysis. Here we evaluated long-term storage effects on mass spectral peak patterns of serum peptides extracted using weak cation exchange magnetic beads. For this, 20 serum samples stored at −80 °C were divided equally into two groups based on their storage time. We found that intensities of 26 mass spectral peaks significantly varied between these two groups. Intensities of these peaks significantly correlated with storage time. Genetic algorithm-based models generated using these 26 peaks could classify 63 additional samples into these two groups with 100% and 96% accuracy, respectively. We also show that storing samples for 10 months at −80 and −20 °C results in the appearance/disappearance or intensity variation of peaks, some of which were previously reported as disease biomarkers.     

Heat-shock protein 27: a potential biomarker for hepatocellular carcinoma identified by serum proteome analysis

Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide and ranks second in China. The prognosis of HCC remains dismal mainly because of its late diagnosis, especially in patients with coexisting chronic liver diseases. To identify serum biomarkers for HCC, sera from 20 healthy volunteers, 20 hepatitis B virus (HBV) infected patients and 20 HCC patients were selected for screening study and same number of sera into the same three groups were used for validation study. A strategy including sonication, albumin and immunoglobulin G (IgG) depletion and desalting was optimized for screening differentially expressed proteins of low abundance in serum. By 2-DE image analysis and MALDI-TOF-MS/MS identification, eight proteins including heat-shock protein 27 (HSP27), alpha-fetoprotein (AFP), alpha-1 antitrypsin, clusterin, caeruloplasmin, haptoglobin alpha2 chain, tranferrin and transthyretin were found significantly changed among the healthy, HBV and HCC groups. Further validation study by Western blot showed the detection of HSP27 in 90% HCC sera and two HBV sera, but in none of normal sera. Thus, 2-DE based serum proteome analysis can be useful in the screening of serum biomarkers for HCC and HSP27 could aid in the diagnosis of HCC though further validation is needed.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Evaluation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry proteomic profiling: identification of alpha 2-HS glycoprotein B-chain as a biomarker of diet

Biomarkers have the potential to impact a wide range of public health concerns, including early detection of diseases, drug discovery, and improved accuracy of monitoring effects of interventions. Given new technological developments, broad-based screening approaches will likely advance biomarker discovery at an accelerated pace. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) allows for the elucidation of individual protein masses from a complex mixture with high throughput. We have developed a method for identifying serum biomarkers using MALDI-TOF and statistical analysis. However, before applying this approach to screening of complex diseases, we evaluated the approach in a controlled dietary intervention study. In this study, MALDI-TOF spectra were generated using samples from a randomized controlled trial. During separate feeding periods, 38 participants ate a basal diet devoid of fruits and vegetables and a basal diet supplemented with cruciferous (broccoli) family vegetables. Serum samples were obtained at the end of each 7-day feeding period and treated to remove large, abundant proteins. MALDI-TOF spectra were analyzed using peak picking algorithms and logistic regression models. Our bioinformatics methods identified two significant peaks at m/z values of 2740 and 1847 that could classify participants based on diet (basal vs. cruciferous) with 76% accuracy. The 2740 m/z peak was identified as the B-chain of alpha 2-HS glycoprotein, a serum protein previously found to vary with diet and be involved in insulin resistance and immune function.     

Immunoprecipitation on magnetic beads and liquid chromatography-tandem mass spectrometry for carbonic anhydrase II quantification in human serum

In this study, a magnetic bead-based platform amenable to high-throughput protein carbonic anhydrase II (CA II) capture is presented. The key steps in this approach involved immunoaffinity purification of the target protein from serum followed by on-bead digestion with trypsin to release a surrogate peptide. This tryptic peptide was quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) operating in multiple reaction monitoring acquisition mode. Using a synthetic peptide standard and a structural analogue free-labeled internal standard, the resulting concentration was stoichiometrically converted to CA II serum concentration. The analytical steps, such as preparation of immunobeads, protein capture, proteolysis, and calibration, were optimized. The method was validated in terms of recovery (77%), reproducibility (relative standard deviation [RSD] < 12%), and method detection limit (0.5 pmol ml⁻¹). The developed method was applied to determining the CA II in eight healthy subjects, and the concentration measured was 27.3 pmol ml⁻¹ (RSD = 65%).     

GPDE: A biological proteomic database for biomarker discovery and evaluation

Clinical proteomics faces extremely complex and variable data. Here, we present an updated version of the Griss Proteomics Database Engine (GPDE): A free biological proteomic database specifically designed for clinical proteomics and biomarker discovery (http://gpde.sourceforge.net). It combines experiments based on investigated cell types thereby supporting customizable biological meta-analyses. Through the new features described here, the GPDE now became a powerful yet easy-to-use tool to support the fast identification and reliable evaluation of biomarker candidates.     

Depletion of abundant proteins from non-human primate serum for biomarker studies

Non-human primates are an important biomedical research model organism and offer great promise for serum biomarker proteomic studies. However, potential obstacles to these studies include affinity serum depletion methods based on human antigens, depletion methods altering quantitation, and incomplete non-human primate genome sequences for protein identification. In the present study, high-abundance protein removal from monkey serum using a human multiple affinity removal system (MARS) was shown to be specific and did not alter quantitation. Depleted serum also demonstrated greater sensitivity for previously masked, lower-abundance proteins.     

Plasma proteomic analysis of patients infected with H1N1 influenza virus

This study profiled the plasma proteins of patients infected by the 2011 H1N1 influenza virus. Differential protein expression was identified in plasma obtained from noninfected control subjects (n = 15) and H1N1‐infected subjects (n = 15). Plasma proteins were separated by a 2DE large gel system and identified by nano‐ultra performance LC‐MS. Western blot assays were performed to validate proteins. Eight plasma proteins were upregulated and six proteins were downregulated among 3316 plasma proteins in the H1N1‐infected group as compared with the control group. Of 14 up‐ and downregulated proteins, nine plasma proteins were validated by Western blot analysis. Putative protein FAM 157A, leucine‐rich alpha 2 glycoprotein, serum amyloid A protein, and dual oxidase 1 showed significant differential expression. The identified plasma proteins could be potential candidates for biomarkers of H1N1 influenza viral infection. Further studies are needed to develop these proteins as diagnostic biomarkers.     

Preliminary characterizations of a serum biomarker for sarcoidosis by comparative proteomic approach with tandem-mass spectrometry in ethnic Han Chinese patients

Background

The diagnosis of sarcoidosis is still a significant challenge in China because of the need to exclude other diseases including granulomatous infections and malignancies that may be clinically and radiographically similar. The specific aim of the study is to search for serum protein biomarkers of sarcoidosis and to validate their clinical usefulness in differential diagnosis.

Methods

Serum samples were collected from patients with sarcoidosis (n = 37), and compared to those from patients with tuberculosis (n = 20), other pulmonary diseases (n = 20), and healthy volunteers (n = 20) for determination of sarcoidosis-specific or -associated protein expression profiles. The first part of this study focused on proteomic analysis of serum from patients with sarcoidosis to identify a pattern of peptides capable of differentiating the studied populations using the ClinProt profiling technology based on mass spectrometry. Enzyme Linked Immunosorbent Assay (ELISA) was then used to verify corresponding elevation of the serum protein concentration of the potential biomarkers in the same patients sets. Receiver operating characteristic curve (ROC) analyses was performed to determine the optimal cutoff value for diagnosis. Immunohistochemistry was carried out to further confirm the protein expression patterns of the biomarkers in lung tissue.

Results

An unique protein peak of M/Z 3,210 Daltons (Da) was found to be differentially expressed between the sarcoidosis and control groups and was identified as the N-terminal peptide of 29 amino acids (94-122) of serum amyloid A (SAA). ELISA confirmed that the serum SAA level was significantly higher in the sarcoidosis group than that of the other 3 control groups (p < 0.05). The cutoff for serum SAA concentration determined by ROC analysis was 101.98 ng/ml, with the sensitivity and specificity of 96.3% and 52.5%, respectively. Immunohistochemical staining showed that the SAA depositions in lung tissue of the sarcoidosis patients were also significantly more intense than in non-sarcoid lung tissue (p < 0.05).

Conclusion

This is the first study to investigate serum protein markers in Chinese subjects with sarcoidosis. This study shows that the serum SAA expression profiles were different between the sarcoidosis and non-sarcoidosis groups. SAA may be a potential serum biomarker for ruling-out the diagnosis of sarcoidosis in Chinese subjects.     

Identification of proteins secreted by head and neck cancer cell lines using LC-MS/MS: Strategy for discovery of candidate serological biomarkers

In search of blood-based biomarkers that would enhance the ability to diagnose head and neck/oral squamous cell carcinoma (HNOSCC) in early stages or predict its prognosis, we analyzed the HNOSCC secretome (ensemble of proteins secreted and/or shed from the tumor cells) for potential biomarkers using proteomic technologies. LC-MS/MS was used to identify proteins in the conditioned media of four HNOSCC cell lines (SCC4, HSC2, SCC38, and AMOSIII); 140 unique proteins were identified on the basis of 5% global false discovery rate, 122 of which were secretory proteins, with 29 being previously reported to be overexpressed in HNOSCC in comparison to normal head and neck tissues. Of these, five proteins including α-enolase, peptidyl prolyl isomerase A/cyclophilin A, 14-3-3 ζ, heterogeneous ribonucleoprotein K, and 14-3-3 σ were detected in the sera of HNOSCC patients by Western blot analysis. Our study provides the evidence that analysis of head and neck cancer cells' secretome is a viable strategy for identifying candidate serological biomarkers for HNOSCC. In future, these biomarkers may be useful in predicting the likelihood of transformation of oral pre-malignant lesions, prognosis of HNOSCC patients and evaluate response to therapy using minimally invasive tests.     

Comparative proteome analysis of serum from acute pulmonary embolism rat model for biomarker discovery

Pulmonary embolism (PE) is a common, potentially fatal disease and its diagnosis is challenging because clinical signs and symptoms are nonspecific. In this study, to investigate protein alterations of a rat PE model, total serum proteins collected at different time points were separated by two-dimensional electrophoresis (2-DE) and identified using matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Bioinformatics analysis of 24 differentially expressed proteins showed that 20 had corresponding protein candidates in the database. According to their properties and obvious alterations after PE, changes of serum concentrations of Hp, Fn, DBP, RBP, and TTR were selected to be reidentified by western blot analysis. Semiquantitative RT-PCR showed DBP, RBP, and TTR to be down-regulated at mRNA levels in livers but not in lung tissues. The low serum concentrations of DBP, RBP, and TTR resulted in the up-regulation of 25(OH)D3, vitamin A, and FT4 (ligands of DBP, RBP, and TTR) after acute PE in rat models. The serum levels of Hp and Fn were detected in patients with DVT/PE and controls to explore their diagnostic prospects in acute PE because the mRNA levels of Hp and Fn were found to be up-regulated both in lung tissues and in livers after acute PE. Our data suggested that the concentration of serum Fn in controls was 79.42 +/- 31.57 microg/L, whereas that of PE/DVT patients was 554.43 +/- 136.18 microg/L (P < 0.001), and that the concentration of serum Hp in controls was 824.37 +/- 235.24 mg/L, whereas that of PE/DVT patients was 2063.48 +/- 425.38 mg/L (P < 0.001). The experimental PE rat model selected in this study was more similar to the clinical process than the other existing PE animal models, and the findings indicated instant changes of serum proteins within 48 h after acute PE. The exploration of these differentially expressed proteins or their combination with existent markers such as D-dimer may greatly improve the accuracy of the diagnosis of acute PE, but diagnostic tests are still needed to evaluate the sensitivity and specificity of these markers and also the number of false positives and false negatives.     

Differential serum proteomic analysis in a model of metabolic disease

Protein profiling would aid in better understanding the pathophysiology of metabolic disease. Here, we report on differential proteomic analysis using an animal model of diabetes mellitus and associated metabolic disorders (Otsuka Long-Evans Tokushima Fatty rat). Serum was analyzed by a new two-dimensional liquid chromatography system which separated proteins by chromatofocusing and subsequent reversed-phase chromatography. This is the first application of this approach to differential serum proteomics. Differentially expressed proteins, identified with MALDI-TOF mass spectrometry, included apolipoproteins and alpha2-HS-glycoprotein. These findings add to our understanding of the underlying pathophysiology. This new proteomic analysis is a promising tool to elucidate disease mechanisms.     

Experimental reconsideration of the utility of serum starvation as a method for synchronizing mammalian cells

Accurate cell-size determinations support the prediction that serum starvation and related whole-culture methods cannot synchronize cells. Theoretical considerations predict that whole-culture methods of synchronization cannot synchronize cells. Upon serum starvation, the fraction of cells with a G1-phase amount of DNA increased, but the cell-size distribution is not narrowed. In true synchronization, the cell-size distribution should be narrower than the cell-size distribution of the original culture. In contrast, cells produced by a selective (i.e. non-whole-culture) method have a specific DNA content, a narrow size distribution, and divide synchronously. The general theory leading to the conclusion that whole-culture methods for synchronization do not work implies that one can generalize these serum-starvation results to other cell lines and other whole-culture methods, to conclude that these methods do not synchronize cells.     

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

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

SELDI-TOF MS of quadruplicate urine and serum samples to evaluate changes related to storage conditions

Proteomic profiling with SELDI-TOF MS has facilitated the discovery of disease-specific protein profiles. However, multicenter studies are often hindered by the logistics required for prompt deep-freezing of samples in liquid nitrogen or dry ice within the clinic setting prior to shipping. We report high concordance between MS profiles within sets of quadruplicate split urine and serum samples deep-frozen at 0, 2, 6, and 24 h after sample collection. Gage R&R results confirm that deep-freezing times are not a statistically significant source of SELDI-TOF MS variability for either blood or urine.     

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.     

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.     

A simple affinity spin tube filter method for removing high-abundant common proteins or enriching low-abundant biomarkers for serum proteomic analysis

Although it is possible to identify new proteins from crude cell extracts using proteomics technology, it is often difficult to elucidate low-abundant biomarkers in the presence of a large amount of high-abundant proteins in serum. We have developed a simple and rapid method using an affinity spin tube filter to remove high-abundant common proteins and enrich the low-abundant biomarkers. The affinity spin tube filter contains protein G, coupled with antibodies against either high-abundant proteins or specific proteins of interest. After incubating with serum, the flow-through or the elute was collected and analyzed by two-dimensional gel electrophoresis. By using this affinity spin tube filter, the possibilities of identifying new biomarkers are shown. This technique could be used for large-scale sample preparation for high-throughput proteomic analysis.