Sandipan Ray

Label-free detection techniques for protein microarrays: Prospects, merits and challenges

Protein microarrays, on which thousands of discrete proteins are printed, provide a valuable platform for functional analysis of the proteome. They have been widely used for biomarker discovery and to study protein–protein interactions. The accomplishments of DNA microarray technology, which had enabled massive parallel studies of gene expression, sparked great interest for the development of protein microarrays to achieve similar success at the protein level. Protein microarray detection techniques are often classified as being label‐based and label‐free. Most of the microarray applications have employed labelled detection such as fluorescent, chemiluminescent and radioactive labelling. These labelling strategies have synthetic challenges, multiple label issues and may exhibit interference with the binding site. Therefore, development of sensitive, reliable, high‐throughput, label‐free detection techniques are now attracting significant attention. Label‐free detection techniques monitor biomolecular interactions and simplify the bioassays by eliminating the need for secondary reactants. Moreover, they provide quantitative information for the binding kinetics. In this article, we will review several label‐free techniques, which offer promising applications for the protein microarrays, and discuss their prospects, merits and challenges.

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.

Nanotechniques in proteomics: current status, promises and challenges.

Proteomics has witnessed rapid growth over the last decade, with increasing emphasis on development of robust and high-throughput technologies to understand the diverse proteome. Researchers have gone beyond traditional techniques and approached promising disciplines like nanotechnology to satisfy the growing demands of studying numerous complex and functional proteins. Applications of nanotechniques in proteomics have steadily been growing over the years and it has established itself as a technical platform for sensitive detection of low abundance proteins in shorter time. The main focus of this inter-disciplinary approach is to increase the sensitivity and improve biocompatibility. The most promising candidates for nanoproteomics, namely carbon nanotubes and nanowires, quantum dots and nanoscopic gold particles, offer several advantages such as high sensitivity, real-time measurements and improved reproducibility. However, toxicity and biocompatibility issues for in vivo applicability require further exploration to provide a suitable risk-to-benefit assessment of these emerging nanotechniques. This article highlights the current status and key technological advances of nanotechniques that offer promising applications in proteomics and addresses their prospects, merits and challenges.

Serum proteome analysis of vivax malaria: An insight into the disease pathogenesis and host immune response

Vivax malaria is the most widely distributed human malaria resulting in 80-300 million clinical cases every year. It causes severe infection and mortality but is generally regarded as a benign disease and has not been investigated in detail. The present study aimed to perform human serum proteome analysis in a malaria endemic area in India to identify potential serum biomarkers for vivax malaria and understand host response. The proteomic analysis was performed on 16 age and gender matched subjects (vivax patients and control) in duplicate. Protein extraction protocols were optimized for large coverage of the serum proteome and to obtain high-resolution data. Identification of 67 differentially expressed and statistically significant (Students t-test; p<0.05) protein spots was established by MALDI-TOF/TOF mass spectrometry. Many of the identified proteins such as apolipoprotein A and E, serum amyloid A and P, haptoglobin, ceruloplasmin, and hemopexin are interesting from a diagnostic point of view and could further be studied as potential serum biomarkers. The differentially expressed serum proteins in vivax malaria identified in this study were subjected to functional pathway analysis using multiple software, including Ingenuity Pathway Analysis (IPA), Protein ANalysis THrough Evolutionary Relationships (PANTHER) and Database for Annotation, Visualization and Integrated Discovery (DAVID) functional annotation tool for better understanding of the biological context of the identified proteins, their involvement in various physiological pathways and association with disease pathogenesis. Functional pathway analysis of the differentially expressed proteins suggested the modulation of multiple vital physiological pathways, including acute phase response signaling, complement and coagulation cascades, hemostasis and vitamin D metabolism pathway due to this parasitic infection. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Proteomic Investigation of Falciparum and Vivax Malaria for Identification of Surrogate Protein Markers

This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n = 20), vivax malaria (VM) (n = 17) and healthy controls (HC) (n = 20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.

Challenges and prospects for biomarker research: A current perspective from the developing world

Majority of deaths due to communicable and non-communicable diseases occur in the low and middle-income nations (LMNs), mainly due to the lack of early diagnoses and timely treatments. In such a scenario, biomarkers serve as an indispensible resource that can be used as indicators of biological processes, specific disease conditions or response to therapeutic interventions. Evaluation, diagnosis and management of diseases in developing world by following/extrapolating the findings obtained on the basis of the research work involving only the populations from the developed countries, could often be highly misleading due to existence of diverse patterns of diseases in developing countries compared to the developed world. Biomarker candidates identified from high-throughput integrated omics technologies have promising potential; however, their actual clinical applications are found to be limited, primarily due to the challenges of disease heterogeneity and pre-analytical variability associated with the biomarker discovery pipeline. Additionally, in the developing world, economic crunches, lack of awareness and education, paucity of biorepositories, enormous diversities in socio-epidemiological background, ethnicity, lifestyle, diet, exposure to various environmental risk factors and infectious agents, and ethical and social issues also cumulatively hinder biomarker discovery ventures. Establishment of standard operating procedures, comprehensive data repositories and exchange of scientific findings are crucial for reducing the variability and fragmentation of data. This review highlights the challenges associated with the discovery, validation and translational phases of biomarker research in LMNs with some of their amenable solutions and future prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Investigation of serum proteome alterations in human glioblastoma multiforme

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D‐DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A‐1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade‐specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low‐density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS‐DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.

Serum profiling of leptospirosis patients to investigate proteomic alterations.

Abstract Leptospirosis is a zoonotic infectious disease of tropical, subtropical and temperate zones, which is caused by the pathogenic spirochetes of genus Leptospira. Although this zoonosis is generally not considered as fatal, the pathogen can eventually cause severe infection with septic shock, multi-organ failure and lethal pulmonary hemorrhages leading to mortality. In this study, we have performed a proteomic analysis of serum samples from leptospirosis patients (n=6), febrile controls (falciparum malaria) (n=8) and healthy subjects (n=18) to obtain an insight about disease pathogenesis and host immune responses in leptospiral infections. 2DE and 2D-DIGE analysis in combination with MALDI-TOF/TOF MS revealed differential expression of 22 serum proteins in leptospirosis patients compared to the healthy controls. Among the identified differentially expressed proteins, 8 candidates exhibited different trends compared to the febrile controls. Functional analysis suggested the involvement of differentially expressed proteins in vital physiological pathways, including acute phase response, complement and coagulation cascades and hemostasis. This is the first report of analysis of human serum proteome alterations in leptospirosis patients, which revealed several differentially expressed proteins, including α-1-antitrypsin, vitronectin, ceruloplasmin, G-protein signaling regulator, apolipoprotein A-IV, which have not been reported in context of leptospirosis previously. This study will enhance our understanding about leptospirosis pathogenesis and provide a glimpse of host immunological responses. Additionally, a few differentially expressed proteins identified in this study may further be investigated as diagnostic or prognostic serum biomarkers for leptospirosis. This article is part of a Special Issue entitled: Integrated omics.

Virtual Labs in proteomics: new E-learning tools.

Web-based educational resources have gained enormous popularity recently and are increasingly becoming a part of modern educational systems. Virtual Labs are E-learning platforms where learners can gain the experience of practical experimentation without any direct physical involvement on real bench work. They use computerized simulations, models, videos, animations and other instructional technologies to create interactive content. Proteomics being one of the most rapidly growing fields of the biological sciences is now an important part of college and university curriculums. Consequently, many E-learning programs have started incorporating the theoretical and practical aspects of different proteomic techniques as an element of their course work in the form of Video Lectures and Virtual Labs. To this end, recently we have developed a Virtual Proteomics Lab at the Indian Institute of Technology Bombay, which demonstrates different proteomics techniques, including basic and advanced gel and MS-based protein separation and identification techniques, bioinformatics tools and molecular docking methods, and their applications in different biological samples. This Tutorial will discuss the prominent Virtual Labs featuring proteomics content, including the Virtual Proteomics Lab of IIT-Bombay, and E-resources available for proteomics study that are striving to make proteomic techniques and concepts available and accessible to the student and research community. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 14). Details can be found at: http://www.proteomicstutorials.org/.

Cancer Biomarker Detection by Surface Plasmon Resonance Biosensors

Diagnosis of cancer at an early stage of development is essential for effective treatment to control its progression and reduce the mortality rate. Biomarkers are useful candidates for disease diagnosis, monitoring disease progression, and following prognosis in response to the therapeutic interventions. Over the last decade here has been a growing interest in analysis of various biological fluids to identify anels of protein markers for cancer, leading to the discovery of several potential argets. Despite the sincere efforts from various research groups from all over the world, only a handful of the identified candidates has been approved by the US Food and Drug Administration, which indicates serious “bottle neck” between the “benchside” findings and their successful “bed-side” implications. Multiple issues associated with candidate markers, such as very low abundance, ambiguity, lack of specificity, enormous variation among individuals, and paucity of reproducibility, are hindering their successful translation in clinics. Several proteomic approaches, such as mass spectrometry-based assays, gelased profiling, and, more recently, protein and antibody arrays, are emerging rapidly as advanced platforms for cancer biomarker discovery and have enhanced our understanding of biological networks at the functional level to provide some mechanistic insight into this fatal disease. However, limited dynamic range and low sensitivity are the major limitations for most of the existing gel and MS-based proteomic approaches. Moreover, accurate diagnosis of complex diseases like ancer requires the simultaneous detection of multiple biomarkers, stipulating the eed for high-throughput (HT) detection platforms. To this end, array-based aproaches, such as antibody, reverse phase, and functional microarrays, are promising