Research Article

Proteomics Tools – An Update

Sachin Kumar1*, Vikar Gaur, Sartaj Khurana2, Sudeep Bose3, Manjula Kiran4, Manjula Kiran5 and Surender K Sharawat6
1Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, India
2Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, India 3Department of Biotechnology, Amity University, Noida, Uttar Pradesh -201303, India
4Department of Biotechnology, Amity University, Noida, Uttar Pradesh -201303, India
5Deprtment of Biologicals, Amity University, India
6Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, India

*Corresponding author: Sachin Kumar, Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, India

Published: 10 Oct, 2017
Cite this article as: Kumar S, Gaur V, Khurana S, Bose S, Kiran M, Kiran M, et al. Proteomics Tools – An Update. Clin Oncol. 2017; 2: 1358.


Recent advances in high-throughput genome sequencing, analytical instrumentation, bioinformatics and computational biology has permanently changed the way biology is practiced. With the evolution and progress in system biology, scientists can now study the systems comprehensively and in very fine molecular details rather than just focusing on various components of living system individually. Proteomics, commonly described as expressed form of genome, has emerged as one of the vital constituent of system biology and is being studied extensively across the globe. Unlike genome, proteome is a very dynamic and complex entity involving not only the structures, sequences, and functions of proteins, but also their abundance, localization, modifications, and interactions. Hence, proteomics can provide plenty of information for better understanding of the diversity and intricacies of the biological system. However, the analysis of these various properties of the proteome requires an equally diverse range of technologies, like two-dimensional gel electrophoresis, mass spectrometry, and protein microarrays. In this review, we provide an introduction to the field of proteomics with an aim to describe numerous tools and techniques to study proteomics.


The term “proteome” was first used by Marc Wilkins, an Australian postdoctoral fellow, in 1994 [1]. He defined proteome as the entire set of proteins expressed in a specific cell at a specific time and the study of which is now known as “proteomics.” The term proteome is a combination of proteins and genome. Proteomics aims to study the dynamic protein products of the genome, including its structure, function, regulation and interactions, rather than focusing on the simple static DNA blueprint of a cell. In eukaryotes, the proteome is bigger than the genome, mainly due to alternative splicing of genes and various post-translational modifications (PTMs) a protein undergoes, like acetylation, phosphorylation, and glycosylation. As the field of proteomics grew further, it was soon realized that a specific protein we examine can result because of transcripts from identical genes, identical parts of genes or from various PTMs. This led to the concept of protein species which was defined as the smallest unit of the proteome [2]. Soon, it was evident that in proteomics, analysis of protein species can’t be ignored to get a comprehensive picture of the biological complexity. In addition to various post-translational protein modifications, there are different external and internal factors which affect protein expression at a particular time. Keeping all this in mind, proteome of an individual was defined as the sum of all the protein species which are expressed in a time-dependent manner during the life span of an organism [3]. Further, total set of protein species expressed in a specific biological compartment at a given time and under specific environmental situations is known as ‘sub-proteome’ [3]. One of the major challenges of the current proteomics technologies is to measure the entire set of proteins and protein species expressed in an individual from birth to death. Proteomics has been rightly called the ‘science in preparation for the new millennium’, due to rapid advances achieved in its automation, combinatorial chemistry and high throughput screening [4,5]. On account of its enormous potential, proteomics can be further divided into three branches: ‘structural proteomics’, ‘expression proteomics’ and ‘interaction proteomics’. All these branches of proteomics possess enormous potentials. Through structural proteomics, analysis of protein structure and its comparison can help in elucidation of the functions of newly identified genes. Structural proteomics can also be used to investigate drug-protein interactions and protein- protein interactions. In contrast, through expression proteomics, novel, disease-specific or differentially expressed proteins can be identified by comparing the expression levels of proteins in normal healthy tissues and diseased tissues and such proteins can represent a therapeutic target or can serve as diagnostic biomarkers. Determination of protein-protein interactions by interaction proteomics helps to determine protein functions and can also show how proteins assemble in larger complexes.

Figure 1

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Figure 1
Breakthrough achievements along the time line leading to the origin of proteomics.

Figure 2

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Figure 2
General flow scheme for proteomic analysis with highlighted considerations.


Most advances in biology can usually be traced back to the development of new techniques. The field of proteomics is not an exception. In 1945, the pioneering work by Frederick Sanger on separation technology paved the way for the development of protein sequencing [6]. However, protein sequencing gained momentum only when the phenylisothiocyanate sequencing chemistry developed by Edman in 1949 [7] was automated [8]. This was followed by the development of first computer programs for the amino acid sequencing [9,10]. The idea of analyzing the complete complement of proteins being produced by a cell arose nearly four decades ago with the development of two-dimensional gel electrophoresis (2-DE). Margolis and Kenrick [11] used a combination of native isoelectric focusing with pore gradient sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) to separate serum proteins. The 2-DE technique which is mostly used today originated from the work of Patrick O'Farrell [12] and Joachim Klose [13]. Several gel analysis and database programs were developed such as PDQuest [14], GELLAB [15], TYCHO (later KEPLER) [16], and MELANIE [17]. This development in the field of 2-DE led to an idea of building protein database such as human protein index [18]. In the late 1980s, inventions in the field of ionization techniques for peptides and proteins: electrospray ionization (ESI) [19] and matrix-assisted laser desorption/ionization (MALDI) [20,21] revolutionized the field of mass spectroscopy (MS). The success obtained with MALDIand ESI-based ionization techniques enabled the improvement of commercial mass spectrometers by incorporation of these robust ‘ion source’, making them more acceptable methods for protein identification amongst a variety of other proteomics technologies. The field of proteomics grew further with the introduction of peptide mass fingerprinting (PMF) in 1993 by several research groups [22- 26]. Developments in the field of MS led to the first published MS/ MS search algorithms [27,28] which further facilitated MS mediated protein identification. The method of non-spray ESI was developed subsequently [29,30] which was preferred for the chromatography based MS approaches like LC-MS/MS.
With the rapid growth in analytical technologies and development of various tools and databases in computational biology and bioinformatics, it became feasible to extract, interrogate and identify specific protein from complex protein mixtures with precision. With this, in the year 1994, the term ‘proteome’ was coined in the firstever 2-DE meeting held in Siena, Italy [1]. Since then, the proteome is popularly defined as the protein complement of the genome, while its study is known as ‘proteomics’. Today’s proteomic practitioners enjoy the legacy of the past 30 years (Figure 1).

Proteomic Tools

Proteomics is a complement to various genomics-based approaches. One of the major advantages of proteomics is that the protein is the endpoint of biological system. A cell gets it unique identity because of a variety of proteins expressed in that cell type. Study of protein dynamics, like changes in protein expression, modifications, and interactions in a cell type in response to environmental conditions and in pathogenic conditions, such as cancer, and also its intrinsic genetic programs can help in better understanding of molecular mechanisms involved in these processes. Expression and function of proteins is regulated through transcription as well as PTMs. Different splicing can yield more than one RNA from one gene. Moreover, proteins can undergo >200 PTMs which can affect their function, their interactions with other proteins and nucleic acids, trafficking, stability, turn-over rate and so on [31], and this being contributing to production of a large number of protein products from one gene. Elucidating and understanding these changes at molecular levels are the underlying themes in the proteomics.
The essential elements involved in analytical proteomics are illustrated in figure 2. Briefly, a complex protein mixture consisting of proteins of different molecular mass, solubilities and modifications is separated into rather less complex mixtures consisting of fewer components. This is usually achieved by the separation of intact proteins first followed by their digestion into peptides or vice versa. The separation is done by either 2-dimensional gel electrophoresis (2- DE) or various chromatography-based approaches. The peptides are analyzed by MALDI-TOF-MS or ESI-MS. The data generated by MS is then matched with available databases using various bioinformatics software for identifying peptides and their sequences. In the end, identify of a protein is established from the original complex protein mixture. During the last decade, new approaches have been developed not only for the identification of various proteins expressed in cells but also for comparing the expression levels of proteins under different cellular context. In the following section, various classical as well as emerging proteomics technologies are being described.

Two-Dimensional Gel Electrophoresis (2-DE)

2-DE has been the one of the most widely accepted technique in quantitative proteomics [32]. 2-DE involves extraction of total proteins from a given sample and their separation according to isoelectric points (pIs) followed by molecular weights in first and second dimension, respectively to produce protein profiles. The reproducibility and resolution in separating almost entire range of proteins (basic or acidic) has been enhanced significantly by employing immobilized pH gradients in 2-DE [32,33]. By using large format gels (40-30 cm as opposed to the ‘standard’ 20-20 cm format), 10,000 proteins have been resolved from mouse testis extracts [34]. Automated versions of 2-DE have also been established, e.g., 2-D membrane electrophoresis, which provides a quick, high resolution two-dimensional technique for protein separation on polyvinylidene fluoride (PVDF) or nylon membranes without cooling effect. The separation in both dimensions is performed on the same membrane, which just has to be rotated in the electric field after the buffer change. The whole process takes only 20 min as compared with 1-2 days for conventional 2-DE polyacrylamide gel electrophoresis (PAGE).
The detection of different protein spots on the gel is achieved using a variety of dyes and stains available in the market [35]. Coomassie blue dyes, silver stain, and fluorescent dyes are most commonly used dyes in 2-DE. Autoradiography is also being used to visualize and quantitate radiolabelled proteins that are resolved by 2-DE. Proteins are commonly labeled with either 3H, 14C, 35S, 32P or 125I in vivo [36]. Imaging of the stained gels is usually performed by using charge-coupled device (CDD) camera, phosphor imagers and multichannel array detectors. The obtained gel image is processed using Photoshop or Corel programmes before applying an analysis system. The quantification and identification of specific protein resolved on a 2-DE gel can be achieved by scanning of stained gels by laser densitometers set at different resolutions and with the use of bioinformatics software like PDQuest [14]. One of the most widely accepted applications of 2-DE has been to detect differential protein expression in diseased versus healthy tissues. This has led to identification of a number of protein-based biomarkers in various human cancers.

Two-dimensional Differential Gel Electrophoresis (2D-DIGE)

2-DE has some serious limitations such as it is a time consuming and suffers from gel-gel variations. 2D-DIGE can be used to overcome these limitations as it depends on multiple fluorescent dyes, like Cy dyes, for the labeling of different protein samples followed by their separation and visualization on a single 2-DE gel [37]. Up to three protein samples can be labeled, each with a different fluorescent dye, (like Cy2, Cy3, and Cy5 dyes), then combined and separated by 2-D PAGE on a single gel. Densitometric scanning at different wavelengths, characteristic for each dye, permit to acquire three images from only one gel. Even small differences in expression levels can be determined by comparing the ratio obtained from one fluorescent-labeled sample directly with other using different wavelengths for image analysis. Both, the classical 2-DE as well as 2D-DIGE requires image analysis software such as Image Master 2D Platinum (GE Healthcare, Uppsala, Sweden) for classical gels and De Cyder (DeCyder Differential Analysis software, GE Healthcare) with regard to the DIGE gels.

Mass Spectrometry (MS)

Mass spectrometers are usually divided into three fundamental parts, namely the ionization source, the analyzer, and the detector. MS is a powerful tool to obtain peptide mass fingerprints (PMF) for proteins which are resolved by 2-DE. In a typical proteomics experiment, proteins resolved by 2-DE or DIGE are first subjected to ‘in-gel’ digestion followed by extraction of peptides from the gels and their ionization by feeding them into mass spectrometers. The characterization of proteins has been greatly improved by the availability of MALDI and ESI-based ionization techniques in MS [19,20]. In MALDI, peptides are co-precipitated with a lightabsorbing matrix and the mixture is exposed to short pulses of UV light radiation under low pressure. As a result of this, peptides are ionized and accelerated in an electric field followed by their movement back through an energy-corrected machine [38]. Elapsed time is measured from acceleration to-field-free drift by time-of-flight or by a quadrupole detector from which mass of peptides can be derived. A spectrum consisting of molecular mass of individual peptides is generated, which is further used to search available databases to establish the identity of proteins. Unlike MALDI, in ESI, peptides are ionized by passing them through a capillary device at very high voltage [38]. This makes peptides charged which are then directed to a MS under very low pressure to separate them according to their mass to charge ratios. Nano-electrospray ionization tandem MS, commonly known as ESI MS/MS is another variant of MS approach [29,30]. In ESI MS/MS, a microcapillary tube containing 1 ml of peptide solution sprays a fine mist of charged droplets generated from a potential difference between the capillary and the inlet to the mass spectrometer. Desolvated peptides ions are formed as the solvent evaporates in a high vacuum chamber, and are resolved to produce the first MS scan. This is followed by selective targeting of peptide ion in a collision chamber and its fragmentation after its interaction with an inert gas. The fragments of the peptide ion are then resolved based on their m/z ratio to generate the second MS spectrum with a series of small peptides that differ only by a single amino acid. One of the major advantages of ESI MS/MS is their capability to accurately identify PTMs by incorporating the measurement of mass shifts. For the separation of complex peptide mixtures, gel-independent methods like liquid chromatography coupled with MS (LC-MS/MS) systems, has also been developed [39]. One of such approach is known as ‘shotgun proteomics’, in which LC-MS/MS and various protein sequence databases are used together to study complex mixture of peptides generated through proteolytic digestion of proteins isolated from a sample [40,41]. This approach uses reversed-phase LC to separate the tryptic digests of entire proteins followed by online ESI tandem MS for peptide sequencing. The generated MS spectra of whole cellular fraction are carefully analyzed and protein identification is performed through peptide assignment and database searching. MALDI-TOF MS has been widely used to identify differentially expressed proteins in various human malignancies, like renal cell carcinoma [42] and breast carcinoma [43]. Fourier transform ion cyclotron resonance mass spectrometer (FTICR-MS) is another system developed to identify proteins present in low abundance and to separate proteins with closely related mass to charge ratios in complex mixtures [44]. A combination of FTICR-MS, HPLC and ESI were not only able to differentiate single compounds present in big combinatorial chemistry libraries but also to precisely identify peptide mass with high-throughput mode. Imaging MS has been developed to identify and localize biomolecules, like proteins, directly in tissue sections. In imaging MS, a metal plate, which contains individual cells or frozen tissue sections coated with UV-absorbing matrix, is placed in the MS system and images are produced, depending upon selected mass values, through an optical scanner which measures peak intensities in tissue specimen over thousands of spots [45]. Imaging MS has great potential in discovery of biomarkers, identifying aberrant localization of biomarkers in normal and diseased conditions, understanding molecular heterogeneity of cancer cells, and also in intra-operative evaluation of surgical margins of tumours. Imaging MS has been employed to discover biomarkers in glioblastomas [46].
Selected reaction monitoring (SRM)-MS is an emerging targeted MS-based method specifically developed to complement untargeted shotgun methods [47]. The major application of SRM is in the measurements of a set of pre-selected protein-based biomarkers in multiple samples. In such context, SRM has proven to yield consistent and reproducible results with very high precision [48]. It uses sensitive and specific mass spectrometric assays to measure target analytes across multiple samples. Typically, in a single LC-MS test, a large number of peptides are measured [49]. SRM involves selection of a peptide ion and a fragment ion with predefined mass to charge ratio by employing first and third quadrupoles as filters, whereas collision occurs in second quadrupole. The major application of SRM is in cancer research where it has been widely used for the identification and measurements of wild type and mutant forms of various proteins in cancer cells and bio-fluids as well as validation of cancer biomarkers in a fast and cost-effective manner [50]. Due to the increased scan speed and mass window selectivity of the current mass analyzers, SRM can be simultaneously performed on multiple analytes. This capability lead to the multiplexing of SRMs in a method called multiple reaction monitoring (MRM). A number of high resolution MS systems, such as QTOF and Orbitraps have employed triple quadrupole for improved sensitivity, specificity and scan speed. ABI5600 and Xevo G2 XS QTOF are newer MS systems which can perform protein identification was well as their quantification. When performed on QTOFs or Orbitraps, MSM is popularly called as high resolution MRM (HR-MRM) or parallel reaction monitoring (PRM). However, In PRM a particular fragment ion can’t be monitored during acquisition. Generally, quantitation of selected fragment ion is achieved by analyzing chromatogram generated after the acquisition of mass spectrum. Because of fast scanning, it became possible to develop sequential window acquisition of all theoretical mass spectra (SWATH). In this method, information-dependent acquisition i.e., IDA is used for creation of a spectral library and system is set for selecting precursor ions using a mass range (400- 1250 m/z). Finally, the required SWATH-MS data is extracted for the quantification of peptides. MALDI-Imaging (MALDI-MSI) is an emerging tool to investigate the molecular details of tissues in their morphological context and is also being seen as a substitute for immunohistochemistry [51]. It allows direct quantification of various biomolecules, such as proteins, peptides, and metabolites from tissue specimens. An image is captured which is specifically related with the distribution of detected biomolecule. As MALDI-MSI doesn’t involve use of radiolabeled or fluorescently labeled reagents, it is rapidly being accepted for its use in tissue-based biomarker discovery. Accessible body fluids such as blood, urine or saliva can be then used for verification of the presence of these specific markers in the aim of developing non-invasive tests for cancer diagnosis or screening. When combined with 3D reconstruction of tissue composition and other imaging tools like PET, CT scan, and MRI, MALDI-MSI can help in the development of profile signature diagnosis for early detection of cancer and to complement histopathology, assessment of therapeutic efficacy and personalized medicine [52,53].

Liquid Chromatography and Multi-Dimensional Separation Technology

Liquid chromatography (LC) based separation methods are common in proteomics because of their versatility and compatibility with mass spectrometry. LC can be applied either upstream of 2-DE to pre-fractionate the sample (Figure 3), downstream of 2-DE to separate the peptide mixtures from single excised spots, or instead of 2-DE as the major protein separation technology. Alternative LC methods exploiting different separation principles, such as size, charge, hydrophobicity and affinity for particular ligand, are also being used in proteomics. As is the case for electrophoresis, the highest-resolution separations are achieved when two or more separation principles are applied one after the other in orthogonal dimensions.

Figure 3

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Figure 3
Liquid chromatography in combination with 2-DE for standard proteomic analysis.


Abbreviation: SEC, size exclusion chromatography; IEC, ion exchange chromatography; CF, chromatofocusing; 2-DE, 2-dimensional gel electrophoresis; RP-HPLC, reverse-phase HPLC. Apart from LC and 2-DE based technologies, other alternative chromatography techniques are also being used for proteomic studies [54]. Employed a biphasic two-dimensional micro (μ) liquid chromatography (LC) column packed with strong cation exchange and reversed phase materials for step-wise analysis of peptides initially by charge followed by hydrophobicity. The multi-dimensional protein identification technology (MuDPIT; Figure 4) system was built-in-line with a tandem mass spectrometer. With this approach protein of high (>100 kDa)/low (<10kDa) mass and of relatively low abundance (codon bias <0.2) and solubility could be identified; thereby extending the analysis range possible because proteins and peptides are separated by different physicochemical properties than with 2-DE. Multi-dimensional approaches can combine sizeexclusion [55], ion-exchange [56], immobilized metal affinity [57], reversed-phase chromatography or capillary electrophoresis [58] and are either directly integrated with an MS instrument or run off-line prior to MS identification [59].

Peptide Separation

Abbreviation: SEC, size exclusion chromatography; IEC, ion exchange chromatography; CF, chromatofocusing; 2-DE, 2-dimensional gel electrophoresis; RP-HPLC, reverse-phase HPLC; CE, micro-capillary electrophoresis.

Surface-Enhanced Laser Desorption/Ionization (SELDI)

SELDI is based on a patented method for the separation, profiling, and analyzing proteins which are expressed at femtomole levels [60]. In SELDI, specific probe surfaces or chips are used for the affinitybased capture of proteins. The most critical component of SELDI is the chips, which usually have broad binding properties based on immobilized metal affinity capture or biochemically modified surfaces like antibodies and receptors [61]. After their capture on the protein chip array, proteins are detected by MS system where individual proteins are presented as separate peaks based on their mass to charge ratio and a retentate map is generated. Ciphergen’s ProteinChip assay SELDI-MS system is one of the popular commercially available platform based solely on protein biochip [61]. This system has been used to improve the diagnostic efficacy of low-grade bladder cancer to 75% as compared to the 30% when detected with urine cytology [62]. This system has also been used in identifying protein biomarkers expressed by prostate cancer, lung cancer, ovarian cancer and breast cancer.

Figure 4

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Figure 4
A scheme of multi-dimensional protein identification technology (MuDPIT) for peptide separation.

Proteins and Antibody Microarrays

Protein microarray, also known as protein chips, are one of the most promising high throughput techniques developed for the characterization of proteins [63]. A basic protein microarray consists of an array of immobilized protein spots filled with ‘bait’ proteins which are hybridized with small molecular probes labeled with fluorescent tags. There are currently three variants of protein microarrays that are used to study the biochemical activities of the proteins namely, (i) analytical microarrays, (ii) functional microarrays, and (iii) reverse phase microarrays. Analytical microarrays are used widely for monitoring protein expression levels, biomarker identification, clinical diagnosis etc. On the contrary, functional microarrays comprise of whole protein domains and are employed for probing of many protein activities and protein interactions. Reverse phase microarray is used to determine the presence of modified disease causing proteins. For instance, post-translational modifications can be detected using reverse phase microarrays [64]. Of all the analytical protein microarrays, the most widely used is the antibody array. It is an ELISA-based high throughput technique that is used for protein characterization. This technique enunciates the detection of proteins post antibody capture using direct protein labeling. Antibody arrays have two main variants: (i) labeled based assays and (ii) sandwich assays. The labeled based assay involves the labeling of target proteins with a tag allowing detection after the immobilized antibody is captured. This assay can allow incubation of two different samples at the same time- a reference and a test sample each labeled with a different tag to avoid the variation that may arise between the spots after antibody capture. There are drawbacks to this assay too such as limited specificity and sensitivity. The other variant, sandwich assay, involves the capture of unlabeled proteins by immobilized antibodies and these captured proteins are detected by a second antibody. These assays have higher sensitivity and specificity as compared to the labeled based assays [65]. Protein arrays are now being used extensively for characterization of cancer biomarkers from samples ranging from serum to tissue culture lysates to tumor biopsies and have proved themselves to be an essential tool in cancer research. For the identification and tracking of progression of cancer, immobilized proteins derived from purified cancer cells of micro dissected cells have been used as protein arrays [66]. Antibody arrays have also been used for the discovery of differentially expressed proteins in cancer versus non-cancerous tissue for their use as cancer biomarkers. Recently, many antibody arrays for specific detection of different cytokines have been established. For example, TranSignalTM (Panomics, Inc. Frement, California) cytokine antibody array, where multiple cytokines can be measured in a single hybridization experiment. These arrays have detection sensitivity in the range of pg/ ml for cytokines and an array consistency of zero and 10% between the same spots on two similar membranes.

Isotope-Coded Affinity Tags (ICAT)

Isotope-coded affinity tags depends upon the chemical labeling of any pair of protein samples with two identical reagents isotopic ally different in mass, allowing the relative amount of protein to be quantitatively compared in the subsequent mass spectral determination [67]. A typical ICAT reagent is made up of 3 components: i) an affinity tag, usually a biotin-based, for the isolation of ICAT-labeled peptides, ii) a linker for the incorporation of stable isotopes, and iii) a thiol specific reactive group. There are two varieties of ICAT reagent, light (without deuteriums) and heavy (with 8 deuteriums), with a difference in molecular weight of 8Da between them. A pair of protein samples are labeled on their cysteine residues, respectively, with either light or a heavy form of ICAT chemical reagents and then mixed together for proteolytic digestion. The digested mixture is purified through avidin affinity chromatography using biotin tags on ICAT reagents to isolate the ICAT labeled peptides. These peptides are subjected to MS analysis for production of peak ratios of various proteins which is followed by their sequencing through MS/ MS resulting in the identification of proteins expressed in abnormal levels. Thus, in a single automated process, identification and relative abundance of proteins in two related samples can be determined. Thus, the major application of ICAT is in discovery of protein-based diagnostic and prognostic biomarkers and therapeutic drug targets in various human disease conditions, including cancer. Variations of ICAT approach like, alternative labeling chemistries [68]; stable isotope labeling [69]; incorporation of stable isotopes of oxygen (16O or 18O) at the C-terminus of peptides [70,71]; and metabolic labeling of proteins by stable isotopes has also been attempted in a LC-MS/ MS system [72-74].

Isobaric Tag for Relative and Absolute Quantitation (Itraq)

iTRAQ is another MS-based approach for comparative measurements of single peptide or protein in a complex protein mixture [75]. Four iTRAQ reagents e.g., 114, 115, 116, and 117, which are chemically identical and have same molecular mass, can be used to multiplex four unrelated samples for relative quantification of proteins or peptides. A typical iTRAQ label is made up of a 145Da isobaric tag containing carbonyl balancer group and N-methylpiperazine-based reporter group and a NHS ester-based peptide reactive group [76]. The work-flow of iTRAQ experiment involves reduction and denaturation of protein samples, blocking of cysteine residues, and tryptic digestion of proteins followed by their labeling with iTRAQ reagents. This is followed by mixing equal amount of iTRAQ reagent-labeled samples into one sample mixture for LC-MS/MS analysis. Peptide sequence can then be produced from the product ions derived from the cleavage about peptide interresidue bonds. Relative abundance of specific peptides can then be checked in different samples by evaluating the intensities of reporter ion signals from MS/MS scan. As all the above steps are performed in a single tube, errors in protein identification and quantification which may result from sample loss in individual samples is avoided.

Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)

SILAC is an in-vivo process in which non radioactive label is incorporated into proteins for MS analysis resulting in the detection of relative abundance of proteins in different sample types. Amino acids with substituted stable nuclei are incorporated into the protein. This technique involves simultaneous culturing of two populations of cells; one population is fed with growth medium containing a normal amino acid, while the other cell population is fed with a medium with heavy non-radioactive labeled amino acid. The cells with the labeled amino acid in the medium incorporate this heavy isotope of the amino acid into the proteins. All the proteins with the isotope will be heavier than the normal counterparts. Using MS analysis, the proteins can be segregated based on their mass difference [77].
SILAC technology is a powerful tool for identification of cancer biomarkers. Proteins present in the amino acid labeled cells, could prove to be effective chemotherapy targets. Recently, SILAC method was followed to identify protein biomarkers indicative of resistance to paclitaxel in lung cancer patients [78]. Tumor suppressor protein programmed cell death protein 4 (PDCD4) was down regulated by paclitaxel and elevated expression of PDCD4 was positively correlated with longer survival of patients treated with paclitaxel [78]. In another study, two metastatic prostate cancer cell lines, PC3M and PC3M-LN4 were used for SILAC followed by LC-MS/MS-based proteomic approach. With this approach, E-cadherin was found to be highly expressed in PC3M while UCHL1 was predominantly detected in highly metastatic PC3M-LN4. Another protein found exclusively in PC3M was Suppressor of Tumorigenecity-14, a protein expressed in many normal epithelial tissues but absent in tumor lines derived from these same sites [79]. Thus, SILAC can be effective method for the identification of metastasis-related proteins which can be used for drug designing and development and molecular targeted therapy.

Yeast Two-Hybrid System

This is a genetic proteomic assay developed for studying proteinprotein interactions [80]. It involves fusion of a DNA-binding domain with a protein of interest, i.e. bait and fusion of other proteins i.e. preys with a transcription-activating domain. Physical interactions between bait and prey are screened based on the activation of a transcription reporter construct. Yeast two-hybrid system has been widely used for screening of protein interactions in yeast [81], Helicobacter pylori [82], and Caenorhabditis elegans [83].

Affinity Tagging and Mass Spectroscopy

In yeast, affinity tagging and MS approach has been used for the efficient characterization of its protein complexes [84,85]. Briefly, endogenous genes of yeast and coding sequences of a tandem affinity purification tag are fused together. This is followed by purification and separation of tagged proteins along with their associated partners by gel electrophoresis and their identification by MS. Using this approach, Gavin et al purified and resolved 232 protein complexes containing 1440 different proteins in yeast [84]. Similarly, Ho et al identified > 3000 protein interactions consisting of 1578 individual proteins in yeast [85]. Affinity chromatography, fluorescence resonance energy transfer (FRET), and Surface Plasmon Resonance (SPR) are other methods used to identify protein-protein reactions.

Studying Protein Modifications

PTMs to protein structures play an important role in regulation of protein activity. Identification of the location and the type of modification usually give vital information for better understanding of the regulation and function of a specific protein in cellular or biological context. PTMs can be analyzed using various strategies. For example, phosphorylation can be detected by Western blotting using phosphorylated amino acids specific antibodies [86]. Tyrosine phosphorylated proteins have been detected by a combination of Western blotting after 2-DE and MS [87]. In contrast, enrichment of phosphorylated peptides either by immunoprecipitation or by metal-chelate affinity chromatography can be used to identify phosphoproteins [88]. Trypsin digestion and MS analysis is performed on these enriched phosphoproteins for the identification of resulting fragments. The concept of proteome analysis of glycoproteins from 2-D gels and analysis of model proteins has been proposed [89]. After separation of glycoprotein by 2-DE, protein spots are digested and glycopeptides identification and assignment of the glycosylation sites are carried out. A gel-free strategy was developed for the study of lectin-selected glycoproteins from trypsin digested sample of total protein [90]. To identify multiple PTMs in a single test, MacCoss et al employed shotgun MS approach and identified 270 proteins from human lens tissue [91]. Detailed analysis of lens crystallins proteins belonging to 11 families led to identification of a total of 73 PTMs. There have been attempts at a comprehensive analysis of modifications which include: ubiquitination [92], farnesylation [93] and glycosylphophatidylinositol anchoring of proteins [94].


Proteogenomics is an integrated approach employing a combination of proteomics, genomics and transcriptomics data to identify, assign and confirm the functional gene [95]. Such an approach requires handling of vast amount of information generated through different technologies employed in genomics (exome sequencing), transcriptomics (microarray, RNA-sequencing), and proteomics (LC-MS/MS). Since metabolomics i.e., the study of metabolites, also depends on the technologies similar to proteomics (LC-MS/MS), its integration in proteogenomics in inevitable in near future. In the work-flow of proteogenomics, a customized database of protein sequence is created which is entirely based on genomic information and the information obtained is utilized to analyze the model of interest. With the recent evolution in high-throughput sequencing platforms, theoretically, total set of protein-encoding sequences available in particular cells or tissues can be known. Thereafter, ‘shotgun proteomics’ can be used to identify proteins by searching the customized database of protein sequences. In this way, proteogenomics can prove to be very useful in the validation of gene expression at protein level [95]. Proteogenomics has huge potential in medical diagnostics and cancer research. Tumorspecific proteins or peptide signatures can be identifies using Oncoproteogenomics [96]. Such approach has recently been used to identify five protein signatures in The Cancer Genome Atlas (TCGA) cohort of colorectal cancer [97]. Amplification of chromosome 20q was found to be associated with significant global variations at mRNA as well as protein levels indicating it as a potential biomarker and a candidate therapeutic target [97]. In future, proteogenomics can also be used to classify cancers according to their molecular signatures and for the constitution of a personalized gene- or protein-based database.

Conclusion and Future Prospective

Proteomics is a field that promises to bridge the gap between genome sequence and cellular behavior. Initially, proteomics solely focused on studying the identity and measuring the expression levels of proteins. However, the development in the field of instrumentation, automation of large-scale analytical tools and emergence of various bioinformatics tools has shifted the focus to a broader area. This has allowed proteomic researchers to measure all of the biologically relevant properties of the proteins, including PTMs, interactions, folding etc. However, a significant challenge exists in the integration and automation of various proteomics technologies, a factor that underpinned the success of the large-scale DNA-sequencing projects. Important hurdles must be overcome at every stage of analysis, from sample preparation through to database management (Figure 2). Unlike DNA, proteins can’t be amplified and hence proteomics studies are restricted to proteins derived from natural sources making them substrate dependent. Another major challenge in the field of proteomics for the better understanding of any biological system is the extreme complexity associated with the proteome in terms of structure, stability, localization, function and dynamic range of abundance. The fact is that current technologies are simply inadequate for covering a whole proteome, even if statistics and sampling were not an issue. Although progress is being made on lowabundance- protein detection, we are nevertheless still far from even the possibility of producing a complete proteome for any organism for reasons of both complexity and dynamic range. Proteomic researchers will have to be more resourceful, especially as the tools used in today’s high-throughput environment still bear the stamp of an earlier era when one protein at a time was the standard. Clinical proteomics offers the opportunity and the potential to develop new diagnostic and prognostic tests, to identify new therapeutic targets, and eventually to allow the design of individualized patient treatment. With the advancements in proteomic technologies, disease-specific proteins have been identified for monitoring disease progression or therapeutic response. However, in clinical proteomics, sensitivity and specificity are two important determinants of the success of any protein marker. The future research will have to focus on improving techniques and technologies to achieve better sensitivity and specificity from the current proteomic tools. In this context, “technologies” refers to those instruments or instrumental approaches that provide fundamental capabilities, such as MS instruments, ion sources, chromatographic instrumentation, and so on. “Techniques,” on the other hand, are the procedures we use to get the most out of the available instrumentation. It is important to distinguish these two areas, because improvements in both will drive future progress in proteomics. Finally, it should be emphasized that proteomics is a young science, many of the technologies used in proteomics are still prototypical and that better materials, instrument design and methodology are expected to improve sensitivity, resolution and repeatability in the future.


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