Fractionation of serum components using nanoporous substrates

Numerous previously uncharacterized molecules resident within the low molecular weight circulatory proteome may provide a picture of the ongoing pathophysiology of an organism. Recently, proteomic signatures composed of low molecular weight molecules have been identified using mass spectrometry combined with bioinformatic algorithms. Attempts to sequence and identify the molecules that underpin the fingerprints are currently underway. The finding that many of these low molecular weight molecules may exist bound to circulating carrier proteins affords a new opportunity for fractionation and separation techniques prior to mass spectrometry-based analysis. In this study we demonstrate a method whereby nanoporous substrates may be used for the facile and reproducible fractionation and selective binding of the serum-based biomarker material, including subcellular proteins found within the serum. Aminopropyl-coated nanoporous silicon, when exposed to serum, can deplete serum of proteins and yield a serum with a distinct, altered MS profile. Additionally, aminopropyl-coated, nanoporous controlled-pore glass beads are able to bind a subset of serum proteins and release them with stringent elution. The eluted proteins have distinct MS profiles, gel electrophoresis profiles, and differential peptide sequence identities, which vary based on the size of the nanopores. These material surfaces could be employed in strategies for the harvesting and preservation of labile and carrier-protein-bound molecules in the blood.     

Data mining in proteomic mass spectrometry

Data mining application to proteomic data from mass spectrometry has gained much interest in recent years. Advances made in proteomics and mass spectrometry have resulted in considerable amount of data that cannot be easily visualized or interpreted. Mass spectral proteomic datasets are typically high dimensional but with small sample size. Consequently, advanced artificial intelligence and machine learning algorithms are increasingly being used for knowledge discovery from such datasets. Their overall goal is to extract useful information that leads to the identification of protein biomarker candidates. Such biomarkers could potentially have diagnostic value as tools for early detection, diagnosis, and prognosis of many diseases. The purpose of this review is to focus on the current trends in mining mass spectral proteomic data. Special emphasis is placed on the critical steps involved in the analysis of surface-enhanced laser desorption/ionization mass spectrometry proteomic data. Examples are drawn from previously published studies and relevant data mining terminology and techniques are exlained.     

Mining the malignant ascites proteome for pancreatic cancer biomarkers

Pancreatic cancer (PC) is one of the most lethal malignancies and disease‐specific biomarkers are desperately needed for better diagnosis, prognosis, monitoring treatment efficacy and for accelerating the development of novel targeted therapeutics. Being an advanced stage manifestation and a proximal fluid in contact with cancer tissues, the ascitic fluid presents itself as a promising rich source of biomarkers. Herein, we present a comprehensive proteomic analysis of pancreatic ascitic fluid. To fractionate the complex ascites proteome, we adopted a multi‐dimensional chromatographic approach that included size‐exclusion, ion‐exchange and lectin‐affinity chromatographic techniques. Our detailed proteomic analysis with high‐resolution Orbitrap^® mass spectrometer resulted in the identification of 816 proteins. We followed rigorous filtering criteria that consisted of PC‐specific information obtained from three publicly available databases (Oncomine, Protein Atlas and Unigene) to segregate 20 putative biomarker candidates for future validation. Since these proteins are of membranous and extra‐cellular origin, most are glycosylated, and many of them are over‐expressed in cancer tissues, the probability of these proteins entering the peripheral blood circulation is high. Their validation as serological PC biomarkers in the future is highly warranted.     

Critical dependence of blood-borne biomarker concentrations on the half-lives of their carrier proteins

Until recently, the low-abundance (LA) range of the serum proteome was an unexplored reservoir of diagnostic information. Today it is increasingly appreciated that a diagnostic goldmine of LA biomarkers resides in the blood stream in complexed association with more abundant higher molecular weight carrier proteins such as albumin and immunoglobulins. As we now look to the possibility of harvesting these LA biomarkers more efficiently through engineered nano-scale particles, mathematical approaches are needed in order to reveal the mechanisms by which blood carrier proteins act as molecular ‘mops’ for LA diagnostic cargo, and the functional relationships between bound LA biomarker concentrations and other variables of interest such as biomarker intravasation and clearance rates and protein half-lives in the bloodstream. Here we show, by simple mathematical modeling, how the relative abundance of large carrier proteins and their longer half-lives in the bloodstream work together to amplify the total blood concentration of these tiny biomarkers. The analysis further suggests that alterations in the production of biomarkers lead to gradual rather than immediate changes in biomarker levels in the blood circulation. The model analysis also points to the characteristics of artificial nano-particles that would render them more efficient harvesters of tumor biomarkers in the circulation, opening up possibilities for the early detection of curable disease, rather than simply better detection of advanced disease.     

Targeted and Untargeted Proteomics Approaches in Biomarker Development

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The “fit‐for‐purpose” approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS‐based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.     

Impact of fixation time on GeLC-MS/MS proteomic profiling of formalin-fixed, paraffin-embedded tissues

Formalin-fixed, paraffin-embedded (FFPE) tissue banks represent an invaluable resource for biomarker discovery. Recently, the combination of full-length protein extraction, GeLC-MS/MS analysis, and spectral counting quantification has been successfully applied to mine proteomic information from these tissues. However, several sources of variability affect these samples; among these, the duration of the fixation process is one of the most important and most easily controllable ones. To assess its influence on quality of GeLC-MS/MS data, the impact of fixation time on efficiency of full-length protein extraction efficiency and on quality of label-free quantitative data was evaluated. As a result, although proteins were successfully extracted from FFPE liver samples fixed for up to eight days, fixation time appeared to negatively influence both protein extraction yield and GeLC-MS/MS quantitative proteomic data. Particularly, MS identification efficiency decreased with increasing fixation times. Moreover, amino acid modifications putatively induced by formaldehyde were detected and characterized. These results demonstrate that proteomic information can be achieved also from tissue samples fixed for relatively long times, but suggest that variations in fixation time need to be carefully taken into account when performing proteomic biomarker discovery studies on fixed tissue archives.     

Practical points in urinary proteomics

During the proteomic era, one of the most rapidly growing areas in biomedical research is biomarker discovery, particularly using proteomic technologies. Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics, as the urine is an ideal source for the discovery of noninvasive biomarkers for human diseases. However, there are several barriers to the success of the field and urinary proteome analysis is not a simple task because the urine has low protein concentration, high levels of salts or other interfering compounds, and more importantly, high degree of variations (both intra-individual and inter-individual variabilities). This article provides step-by-step practical points to perform urinary proteome analysis, covering detailed information for study design, sample collection, sample storage, sample preparation, proteomic analysis, and data interpretation. The discussion herein should stimulate further discussion and refinement to develop guidelines and standardizations for urinary proteome study.     

Advances in proteomic prostate cancer biomarker discovery

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For reasons largely unknown, the incidence of prostate cancer has increased in the last two decades, in spite or perhaps because of a concomitant increase in serum prostate-specific antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and “secretome” for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

Modern Tumor Marker Discovery in Urology: Surface Enhanced Laser Desorption and Ionization (SELDI)

During the last two decades, biomarker research has benefited from the introduction of new proteomic analytical techniques. In this article, we review the application of surface enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy in urologic cancer research. After reviewing the literature from MEDLINE on proteomics and urologic oncology, we found that SELDI-TOF is an emerging proteomic technology in biomarker discovery that allows for rapid and sensitive analysis of complex protein mixtures. SELDI-TOF is a novel proteomic technology that has the potential to contribute further to the understanding and clinical exploitation of new, clinically relevant biomarkers.     

Approaches to dimensionality reduction in proteomic biomarker studies

Mass-spectra based proteomic profiles have received widespreadattention as potential tools for biomarker discovery and earlydisease diagnosis. A major data-analytical problem involvedis the extremely high dimensionality (i.e. number of featuresor variables) of proteomic data, in particular when the samplesize is small. This article reviews dimensionality reductionmethods that have been used in proteomic biomarker studies.It then focuses on the problem of selecting the most appropriatemethod for a specific task or dataset, and proposes method combinationas a potential alternative to single-method selection. Finally,it points out the potential of novel dimension reduction techniques,in particular those that incorporate domain knowledge throughthe use of informative priors or causal inference.      

Binding of nanoparticle receptors to peptide alpha-helices using amino acid-functionalized nanoparticles.

Nanoparticles provide large surface areas and controlled surface functionality and structure, making them excellent scaffolds for peptide recognition. A family of nanoparticles has been fabricated by amino acid functionalization to afford tailored surfaces. These particles are complementary to a tetraaspartate peptide (TAP) featuring cofacial anionic functionality when in the alpha-helical conformation. The functional groups present on these nanoparticle surfaces provide a tool to investigate the contribution of various noncovalent interactions at the nanoparticle-peptide interface. The ability of these particles to enforce the folding of the peptide into an alpha-helix was explored, demonstrating high helicity induction with particles featuring dicationic amino acids such as lysine or histidine, and little or no helix stabilization with hydrophobic amino acid termini.     

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.     

Mining the archival formalin-fixed paraffin-embedded tissue proteome: opportunities and challenges

The significant potential of tissue-based proteomic biomarker studies can be restricted by difficulties in accessing samples in optimal fresh-frozen form. While archival formalin-fixed tissue collections with attached clinical and outcome data represent a valuable alternate resource, the use of formalin as a fixative which induces protein cross-linking, has generally been assumed to render them unsuitable for proteomic studies. However, this view has been challenged recently with the publication of several papers accomplishing variable degrees of heat-induced reversal of cross-links. Although still in its infancy and requiring the quantitative optimisation of several critical parameters, formalin-fixed tissue proteomics holds promise as a powerful tool for biomarker-driven translational research. Here, we critically review the current status of research in the field, highlighting challenges which need to be addressed for robust quantitative application of protocols to ensure confident high impact inferences can be made.     

Computational prediction of human proteins that can be secreted into the bloodstream

We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: 'what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them?' We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved approximately 90% prediction sensitivity and approximately 98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi.     

Current state of the art for enhancing urine biomarker discovery

Introduction: Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual’s metabolic and pathophysiologic state.

Areas covered: High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins.

Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.     

Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.