Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery

The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.     

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.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Discovery of biomarker candidates within disease by protein profiling: principles and concepts

Proteins and peptides present within clinical samples represent a valuable library of information regarding the ongoing processes within cells and tissues in health and disease. We have developed and validated novel technology applications that can be used to characterize the patterns of global protein expression in tissue and biofluids in either gel-based systems or by automated multidimensional nanocapillary liquid chromatography. Mass spectrophotometry platforms using MALDI MS and MS/MS or LTQ ion trap MS were capable of delivering sensitive and accurate identifications of hundreds of proteins contained in individual samples including individual forms of processing intermediates such as phospho peptides. The Systems Biology approach of integrating protein expression data with clinical data such as histopathology, clinical functional measurements, medical imaging scores, patient demographics, and clinical outcome provides a powerful tool for linking biomarker expression with biological processes that can be segmented and linked to disease presentation.     

Protein expression profiling of nuclear membrane protein reveals potential biomarker of human hepatocellular carcinoma

Background

Complex molecular events lead to development and progression of liver cirrhosis to HCC. Differentially expressed nuclear membrane associated proteins are responsible for the functional and structural alteration during the progression from cirrhosis to carcinoma. Although alterations/ post translational modifications in protein expression have been extensively quantified, complementary analysis of nuclear membrane proteome changes have been limited. Deciphering the molecular mechanism that differentiate between normal and disease state may lead to identification of biomarkers for carcinoma.

Results

Many proteins displayed differential expression when nuclear membrane proteome of hepatocellular carcinoma (HCC), fibrotic liver, and HepG2 cell line were assessed using 2-DE and ESI-Q-TOF MS/MS. From the down regulated set in HCC, we have identified for the first time a 15 KDa cytochrome b5A (CYB5A), ATP synthase subunit delta (ATPD) and Hemoglobin subunit beta (HBB) with 11, 5 and 22 peptide matches respectively. Furthermore, nitrosylation studies with S-nitrosocysteine followed by immunoblotting with anti SNO-cysteine demonstrated a novel and biologically relevant post translational modification of thiols of CYB5A in HCC specimens only. Immunofluorescence images demonstrated increased protein S-nitrosylation signals in the tumor cells and fibrotic region of HCC tissues. The two other nuclear membrane proteins which were only found to be nitrosylated in case of HCC were up regulated ATP synthase subunit beta (ATPB) and down regulated HBB. The decrease in expression of CYB5A in HCC suggests their possible role in disease progression. Further insight of the functional association of the identified proteins was obtained through KEGG/ REACTOME pathway analysis databases. String 8.3 interaction network shows strong interactions with proteins at high confidence score, which is helpful in characterization of functional abnormalities that may be a causative factor of liver pathology.

Conclusion

These findings may have broader implications for understanding the mechanism of development of carcinoma. However, large scale studies will be required for further verification of their critical role in development and progression of HCC.     

Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling

Although colorectal cancer is one of the best-characterized tumors with regard to the multistep progression, it remains one of the most frequent and deadly neoplasms. For a better understanding of the molecular mechanisms behind the process of tumorigenesis and tumor progression, changes in protein expression between microdissected normal and tumorous colonic epithelium were analyzed. Cryostat sections from colorectal tumors, adenoma tissue, and adjacent normal mucosa were laser-microdissected and analyzed using ProteinChip Arrays. The derived MS profiles exhibited numerous statistical differences. One peak showing significantly high expression in the tumor was purified by reverse-phase chromatography and SDS-PAGE. The protein band of interest was passively eluted from the gel and identified as heat shock protein 10 (HSP 10) by tryptic digestion, peptide mapping, and MS/MS analysis. This tumor marker was further characterized by immunohistochemistry. Analysis of HSP 10-positive tissue by ProteinChip technology confirmed the identity of this protein. This work demonstrates that biomarker in colorectal cancer can be detected, identified, and assessed by a proteomic approach comprising tissue microdissection, protein profiling, and immunological techniques. In our experience, histological defined microdissected tissue areas should be used to identify proteins that might be responsible for tumorigenesis.     

Serum protein profiling in mice: identification of Factor XIIIa as a potential biomarker for muscular dystrophy

Protein profiling in blood serum by fractionation and MS analysis has been applied in mice to assess its applicability as a fast, economical alternative to current DNA and RNA analyses for diagnosis of neuromuscular disorders. Mass spectra of peptides and proteins were generated using serum from dystrophin-deficient mdx and control mice by WCX ClinProt bead fractionation, followed by MALDI-MS. Double cross-validatory linear discriminant and logistic regression data analysis methods were compared with a new Bayesian logistic regression method. These were evaluated on their ability to discriminate between healthy and dystrophic samples, and to identify the discriminatory peaks in the mass spectra. All three approaches classified the spectra with comparable misclassification rates (between 18.4 and 20.6%), with much overlap between the differential peaks identified between the methods. The differential peak pattern from the Bayesian method was sparser and easier to interpret than from the other two methods, without compromising classifying strength. One of the two main differentiating peaks at m/z 3908 was identified as an N-terminal peptide of coagulation Factor XIIIa, previously identified in human serum. This work underlines the translational aspect of serum protein profiling in mice and supports a further study with serum from patients with neuromuscular disorders.     

The first decade of MALDI protein profiling: a lesson in translational biomarker research

MALDI protein profiling has identified several important challenges in omics-based biomarker research. First, research into the analytical performance of a novel omics-platform of potential diagnostic impact must be carried out in a critical manner and according to common guidelines. Evaluation studies should be performed at an early time and preferably before massive advancement into explorative biomarker research. In particular, MALDI profiling underscores the need for an adequate understanding of the causal relationship between molecular abundance and the quantitative measure in multivariate biomarker research. Secondly, MALDI profiling has raised awareness of the significant risk of false-discovery in biomarker research due to several confounding factors, including sample processing and unspecific host-response to disease. Here, the experience from MALDI profiling supports that a central challenge in unbiased molecular profiling is to pinpoint the aberrations of clinical interest among potentially massive unspecific changes that can accompany disease. The lessons from the first decade of MALDI protein profiling are relevant for future efforts in advancing omics-based biomarker research beyond the laboratory setting and into clinical verification.     

Quantum dot-based protein micro- and nanoarrays for detection of prostate cancer biomarkers

In this article, we demonstrate the fabrication and detection of cancer protein biochips consisting of micro- and nanoarrays whereby pegylated quantum dots (QDs) conjugated to antibodies (Abs) of prostate specific antigens (PSA) were used for the detection of clinical biomarkers such as PSA. BSA which acts as an efficient blocking layer in microarrays, tends to show an interaction with QDs. In view of this fact, we investigated two series of samples which were fabricated in the presence and absence of BSA blocking layer. Variation in the incubation time required for the antigen-antibody interaction to take place, different proteins as controls and the effect of bare QDs on these microarrays, were the three main parameters which were studied in these two series. Samples fabricated in the absence of BSA blocking layer exhibited an extremely high specificity in the detection of cancer proteins and were also marked by negligible nonspecific binding effects of QDs, in stark contrast to the samples fabricated using BSA as a blocking layer. Fabrication of nanoarrays of QD-conjugated PSA Abs having a spot size of nearly 900 nm has also been demonstrated. Thus, we show the potential offered by QDs in in vitro analysis of cancer biomarker imaging.     

Tumor profiling using protein biomarker panels in malignant melanoma: application of tissue microarrays and beyond

In the past decade, analysis of the urinary proteome (urinary proteomics) has intensified in response to the need for novel biomarkers that support early diagnosis of kidney diseases. In particular, this also applies to acute kidney injury, which is a heterogeneous complex syndrome with a still-increasing incidence at the intensive care unit. Unfortunately, this major need remains largely unmet to date. The current report aims to explain why attempts to implement urinary proteomic-discovered acute kidney injury diagnostic candidates in the intensive care unit setting have not yet led to success. Subsequently, some key notes are provided that should enhance the chance of translating selected urinary proteomic candidates to valuable tools for the nephrologist and intensivist in the near future.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems’ blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.     

Metabolic profiling of an Echinostoma caproni infection in the mouse for biomarker discovery

Background

Metabolic profiling holds promise with regard to deepening our understanding of infection biology and disease states. The objectives of our study were to assess the global metabolic responses to an Echinostoma caproni infection in the mouse, and to compare the biomarkers extracted from different biofluids (plasma, stool, and urine) in terms of characterizing acute and chronic stages of this intestinal fluke infection.

Methodology/Principal Findings

Twelve female NMRI mice were infected with 30 E. caproni metacercariae each. Plasma, stool, and urine samples were collected at 7 time points up to day 33 post-infection. Samples were also obtained from non-infected control mice at the same time points and measured using ¹H nuclear magnetic resonance (NMR) spectroscopy. Spectral data were subjected to multivariate statistical analyses. In plasma and urine, an altered metabolic profile was already evident 1 day post-infection, characterized by reduced levels of plasma choline, acetate, formate, and lactate, coupled with increased levels of plasma glucose, and relatively lower concentrations of urinary creatine. The main changes in the urine metabolic profile started at day 8 post-infection, characterized by increased relative concentrations of trimethylamine and phenylacetylglycine and lower levels of 2-ketoisocaproate and showed differentiation over the course of the infection.

Conclusion/Significance

The current investigation is part of a broader NMR-based metabonomics profiling strategy and confirms the utility of this approach for biomarker discovery. In the case of E. caproni, a diagnosis based on all three biofluids would deliver the most comprehensive fingerprint of an infection. For practical purposes, however, future diagnosis might aim at a single biofluid, in which case urine would be chosen for further investigation, based on quantity of biomarkers, ease of sampling, and the degree of differentiation from the non-infected control group.     

LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery

Bladder cancer (BC) and kidney cancer (KC) are the first two commonly occurring genitourinary cancers in China. In this study, a comprehensive LC-MS-based method, which utilizes both reversed phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of BC, KC, and noncancer controls. An independent test set consisting of different patients has been used to objectively evaluate the predictive ability of the analysis platform. Excellent sensitivity and specificity have been achieved in detection of KC and BC. The results suggest that serum metabolic profiling could be used for different types of genitourinary cancer diagnosis. Furthermore, cancer type-specific biomarkers were found through a critical selection criterion. As a result, eicosatrienol, azaprostanoic acid, docosatrienol, retinol, and 14'-apo-beta-carotenal were found as specific biomarkers for BC; and PE(P-16:0e/0:0), glycerophosphorylcholine, ganglioside GM3 (d18:1/22:1), C17 sphinganine, and SM(d18:0/16:1(9Z)) were found as specific biomarkers for KC. Receiver operating characteristic (ROC) analysis was used for the preliminary evaluation of the biomarkers. These biomarkers have great potential to be used in the clinical diagnosis after further rigorous assessment.     

Nuclear staining and relative distance for quantifying epidermal differentiation in biomarker expression profiling

Background  

The epidermal physiology results from a complex regulated homeostasis of keratinocyte proliferation, differentiation and death and is tightly regulated by a specific protein expression during cellular maturation. Cellular in silico models are considered a promising and inevitable tool for the understanding of this complex system. Hence, we need to incorporate the information of the differentiation dependent protein expression in cell based systems biological models of tissue homeostasis. Such methods require measuring tissue differentiation quantitatively while correlating it with biomarker expression intensities.     

Targeted mass spectrometry approaches for protein biomarker verification

The search for protein biomarkers has been a highly pursued topic in the proteomics community in the last decade. This relentless search is due to the constant need for validated biomarkers that could facilitate disease risk stratification, disease diagnosis, prognosis, monitoring as well as drug development, which ultimately would improve our quality of life. The recent development of proteomic technologies including the advancement of mass spectrometers with high sensitivity and speed has greatly advanced the discovery of potential biomarkers. One of the bottlenecks lies in the development of well-established verification assays to screen the biomarker candidates identified in the discovery stage. Recently, absolute quantitation using multiple-reaction monitoring mass spectrometry (MRM-MS) in combination with isotope-labeled internal standards has been extensively investigated as a tool for high-throughput protein biomarker verification. In this review, we describe and discuss recent developments and applications of MRM-MS methods for biomarker verification.     

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.     

Focused differential glycan analysis with the platform antibody-assisted lectin profiling for glycan-related biomarker verification

Protein glycosylation is a critical subject attracting increasing attention in the field of proteomics as it is expected to play a key role in the investigation of histological and diagnostic biomarkers. In this context, an enormous number of glycoproteins have now been nominated as disease-related biomarkers. However, there is no appropriate strategy in the current proteome platform to qualify such marker candidate molecules, which relates their specific expression to particular diseases. Here, we present a new practical system for focused differential glycan analysis in terms of antibody-assisted lectin profiling (ALP). In the developed procedure, (i) a target protein is enriched from clinic samples (e.g. tissue extracts, cell supernatants, or sera) by immunoprecipitation with a specific antibody recognizing a core protein moiety; (ii) the target glycoprotein is quantified by immunoblotting using the same antibody used in (i); and (iii) glycosylation difference is analyzed by means of antibody-overlay lectin microarray, an application technique of an emerging glycan profiling microarray. As model glycoproteins having either N-linked or O-linked glycans, prostate-specific antigen or podoplanin, respectively, were subjected to systematic ALP analysis. As a result, specific signals corresponding to the target glycoprotein glycans were obtained at a sub-picomole level with the aid of specific antibodies, whereby disease-specific or tissue-specific glycosylation changes could be observed in a rapid, reproducible, and high-throughput manner. Thus, the established system should provide a powerful pipeline in support of on-going efforts in glyco-biomarker discovery.