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DOI 10.1002/pmic.201500429
REVIEW
An overview of innovations and industrial solutions in Protein Microarray Technology Shabarni Gupta1 , K. P. Manubhai1 , Vishwesh Kulkarni2 and Sanjeeva Srivastava1 1 2
Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, India School of Engineering, University of Warwick, Coventry, UK
The complexity involving protein array technology reflects in the fact that instrumentation and data analysis are subject to change depending on the biological question, technical compatibility of instruments and software used in each experiment. Industry has played a pivotal role in establishing standards for future deliberations in sustenance of these technologies in the form of protein array chips, arrayers, scanning devices, and data analysis software. This has enhanced the outreach of protein microarray technology to researchers across the globe. These have encouraged a surge in the adaptation of “nonclassical” approaches such as DNA-based protein arrays, micro-contact printing, label-free protein detection, and algorithms for data analysis. This review provides a unique overview of these industrial solutions available for protein microarray based studies. It aims at assessing the developments in various commercial platforms, thus providing a holistic overview of various modalities, options, and compatibility; summarizing the journey of this powerful high-throughput technology.
Received: November 8, 2015 Revised: March 2, 2016 Accepted: March 3, 2016
Keywords: Arrayers / Data analysis software / Industry / Protein microarrays / Scanners
1
Additional supporting information may be found in the online version of this article at the publisher’s web-site
Introduction
The rise of omics-driven approaches in quantitative biological sciences has led to a paradigm shift in the molecular understanding of living systems, ushering in an era of biomedical innovations and technologies [1–3]. Microarrays were developed in the 1980s as a lab-on-chip platform for performing expression analysis, where DNA- or mRNA-derived cDNAs were extensively used [4, 5]. Having undergone an evolution of over three decades, these arrays were further miniaturized to the stable DNA microarrays available commercially. DNA microarrays laid a foundation for functional genomics-based studies [6, 7]. However, the need to understand organisms at
Correspondence: Dr. Sanjeeva Srivastava, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India E-mail:
[email protected] Fax: +91-22-2572-3480 Abbreviations: CFES, cell-free expression systems; NAPPA, Nucleic Acid Protein Programmable Array; RPPA, Reverse-phase Protein Arrays
C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
the systemic level in the post-genomic era led to the rise of proteomics and subsequent development of protein microarray platforms. Aberrant activity and interactions at the protein level have often been the basis of various diseases such as cancer. Protein arrays provided a powerful platform to screen any query molecule for protein–protein, protein–small molecular interactions [5, 8, 9]. However, the labile nature of proteins, as compared with the stable DNA molecules, led many to undermine the vast potential of protein arrays [10]. Therefore, establishing a platform and workflow for the diverse protein arrays has been a huge challenge as compared to DNA arrays. In addition to that, the short shelf-life, lack of universal binding partners, and amplification modalities for proteins add to the complexity of developing protein arrays [10]. However, timely innovations in this domain have helped in overcoming many of these obstacles and proved to be useful in numerous interactions-based studies [11–13]. A powerful and high-throughput platform such as protein array relies extensively on technological innovations at every stage right Colour Online: See the article online to view Fig. 1 in colour.
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Figure 1. Schematic representation of core components of protein microarray platform. (A) Commonly used proteins array platforms. Analytical arrays have capture molecules printed in multiplex formats to check for binding partners. Functional arrays have multiple proteins or peptides arrayed on a single chip to screen biospecimens for diagnostics, expression or profiling. RPPA have multiple cell lysates spotted on a single chip to detect the presence of specific proteins/biomarkers in a sample. (B–D) Commonly used surface chemistry, protein arrayers, and detection and data analysis platforms, respectively.
from chip printing, assembly, detection, and data acquisition [8, 14]. Active collaboration and exchange of technology between academia and industry has resulted in advancements of protein array technology, making it available to scientists around the globe [5, 15]. In this review, we summarize the wide range of platforms available for protein array technology and the role of industry in catering to these technological innovations in this field (Fig. 1). A protein array essentially comprises of immobilized proteins, peptides, nucleic acid, or cell lysate probed with query molecules, which can be small molecules, ligands, recombinant proteins/peptides, and proteins from biological specimens or cell lysates [8]. The origin of protein microarrays was inspired by the idea of miniaturizing immunoassays [4]. Serological antigens have been assayed using immunoassays since the late 1920s [5, 15, 16]. The first attempt of developing high-throughput immunoassays led to the development of RIA, followed by ELISA, which continues to be instrumental in diagnostics even today [17, 18]. In 1975, Edwin Southern demonstrated the binding of DNA to a solid substrate to capture complementary nucleotides through a technique now popularly known as Southern blotting [19]. Almost a decade later, in 1989, Roger Ekins took the next step in revolutionizing immunoassays by introducing the concept of multianalyte immunoassays. He proposed that simultaneous measurements of multiple analytes or molecular variants in a single sample were possible using spatially separated antibody spots on a solid support, which would lead to increased throughput and sensitivity [4]. These principles received recognition in C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
1991 when Stephen Fodor fabricated the first DNA microarrays and commercialized them as the first high-throughput miniaturized lab-on-chip Affymetrix platform [15]. With the completion of the Human Genome Project, the DNA microarray platform achieved great heights and continued to be one of the most sought-after platforms for global genomic or transcriptomic signature scouting. In due course of time, the complexity of the proteome was appreciated with the transition from genomics to proteomics [20]. This landmark work by Gygi et al., corroborated by several other new age technologies on a number of other organisms and cell lines [21], dismissed the myth about the correlation of mRNA levels to protein expression profiles [22–25]. The pertinent questions thus revolved around high-throughput protein detection in biospecimens and their functional roles in the cell. It was realized that answers to these questions could not be obtained by studying a single molecule in isolation. This led to the rebirth of Ekins concept in the form of protein microarrays.
2
Protein array platforms
2.1 Evolution of protein array chips The first examples of protein microarray based experiments were inspired from sandwich immunoassay based platforms. In 1998, Silzel et al. demonstrated the subclass-specific reaction of human myeloma proteins to monoclonal antibodies using sandwich immunoassay based microarray [26]. www.proteomics-journal.com
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Antibody arrays are based on antigen capture in which the antigens are detected either by direct labels or by using labelfree approaches. In a study by Haab et al. [27], a two-color method was used wherein 115 antibodies spotted in arrays were probed with a predefined mixture labeled with Cy5 along with Cy3 labeled reference mixture [5]. This was one of the first extensive studies, which was performed on an antigen capture immunoassay based platform. Antibody arrays have evolved further to become one of the popular types of analytical protein arrays, a broader class of solid arrays printed with multiple antibodies, aptamers, or affibodies probed with query proteins for multiplex profiling in a biospecimen [28] (Table 1a). Another category of protein array that, in principal, is similar to direct immunoassays is the one where samples are profiled against purified recombinant antigens spotted on the chips [29]. This platform is particularly useful to capture information on protein–protein interactions, and has had a significant impact on biomarker discovery [30]. These arrays fall in the category of functional microarrays; wherein printed proteins can be assayed not only against antibodies but also against protein, peptide, nucleic acid, synthetic ligand, or small molecule, thus expanding the repertoire of traditional immunoassays. Andrei Mirzabekov and co-workers fabricated the first functional array in 1997, where they immobilized proteins by manually spotting them on a polyacrylamide gel surface using a pin device [31]. The year 2000 was a milestone in the protein microarrays field that marked the beginning of commercialization of modern-day high-throughput protein arrays. Gavin MacBeath and Stuart L. Schreiber reported a proof of concept study where proteins were printed in a high-throughput manner to build an array of 10 800 proteins in a single chip using DNA arrayers [32]. In the same year, Michael Snyder and co-workers devised a method to build yeast protein chips by molding microwells in silicone elastomer sheets on glass slides and performed studies on yeast kinases. This was the first instance where protein chips were used to deduce novel biochemical activities of proteins. One year later, Snyder and co-workers built a functional yeast proteome chip with as many as 5800 GSTtagged unique yeast proteins spotted in duplicates and from there on, these chips were termed as “proteome-on-a-chip” [33]. Snyder, in an attempt to make these platforms accessible, commercialized them by cofounding Protometrix, which was later acquired by Invitrogen, leading to commercialization of the Human Protein Array platform as ProtoArray Human Protein Microarrays. The ProtoArray line of products is now available under Thermo Fisher Scientific. Interestingly, Heng Zhu, who was one of the lead co-workers alongside Michael Snyder, had independently developed these functional arrays to device yeast, herpes virus, Escherichia coli, rice blast fungus, and human “proteome-on-a-chip.” One of the popular chips developed by this group is the Human Protein Array chips, referred to as HuProt, containing as many as 17 000 full-length GST-tagged human recombinant proteins. These have been popularly used by various groups C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
for biomarker and protein interaction screening [13]. These arrays now manufactured by CDI Laboratories feature more than 19 000 recombinant human proteins, and are few of the highly dense protein arrays available (Table 1a). Functional arrays are also available as customized modalities where services specialize in fabricating peptides arrays as per the user’s specifications (Supporting Information Table 1b). The above-discussed microarrays fall in the broader subset of Forward-phase Microarrays wherein one sample is tested against multiple analytes. The other subset of protein microarrays, namely Reverse-phase Protein Arrays (RPPA), is the alternative type of direct capture immunoassay. This platform has gained popularity due to its power to assay a single analyte against many biospecimens spotted on a single chip at once. Like the way forward-phase microarrays were developed based on inspiration from immunoassays, RPPA were a result of attempts to create sensitive and miniaturized alternatives for immunohistochemistry [4]. Lance Liotta and Emanuel Petricoin were the first to report such an approach [34]. Since its development in 2001, this technology has been used extensively to decipher cell signaling and molecular networks in infectious diseases as well as many cancers such as ovarian and breast cancer, and lymphomas to identify underlying perturbations [35]. Since RPPA involve lysate printing, which is a variable among researchers, there are limited commercial services that enable customization of these arrays (Supporting Information Table 1b). These services involve submission of sample lysates to the service providers who fabricate the RPPA chips for researchers (Supporting Information Table 1b).
2.2 Surface chemistry The surface chemistry that leads to the immobilization of proteins is one of the most crucial aspects of protein array architecture [36]. The orientation of immobilized proteins and its binding strength against the substrate determine the fate of the downstream interactions, which can drastically affect the findings of the study. Proteins are highly complex biomolecules possessing various reactive functional groups, which act as effective anchor domains on a chip. The carboxylic functional groups on proteins can bind to an amino surface through electrostatic interactions, thiol groups to maleimide surface through covalent thio-ether binding, and hydroxide groups to epoxy surfaces through covalent ether binding [37]. Proteins immobilize on the slide surface through covalent or noncovalent bonds based on the functional groups present in them. The nucleophilic residues of proteins are immobilized by reactive surfaces coated with aldehyde, NHS-ester, epoxide. Chemo ligation is another example of an azide-based site-specific immobilization. Noncovalent interactions are driven through different affinity tags such as histidine, GST, and streptavidin to their respective ligands or antibodies. Apart from these, several enzyme-dependent methods are available to immobilize www.proteomics-journal.com
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Protein array
Peptide array
NA Detects tissue, cells, and different body fluid proteome Biomarker detection, protein expression profiling Protein–protein interaction, protein phosphorylation Protein expression profiling from serum, tissue, and cell Epitope mapping and antigen discovery NA Human sera based autoantibody screening, specificity of antibody, cross-reaction detection using proteome-wide antibody screening Autoantibody screening, antibody binding specificity profiling, protein–protein interactions, protein–small molecular interactions, protein-DNA/RNA interactions Protein–protein interactions, small molecule–protein binding, serum profiling (patient/population) NA
Spring Bioscience BD Biosciences Eurogentec Hypromatrix Sigma Aldrich B-bridge international PamGene CDI Laboratories
CDI Laboratories
CDI Laboratories
HuProt v2 human proteome microarrays
TB array Mycobacterium tuberculosis proteome microarray VirD array (functional membrane protein virion display microarray) ProtoArray human protein microarray
Sengenics
Screen environmental, food, and herbal allergens
Arrayit
Deciding suitable pet food, identifying the environmental allergens, and selecting proper allergic medication To detect antibodies against M. tuberculosis, P. falciparum, B. pseudomallei, S. typhi, S. aureus Biomarker discovery for cancer and autoimmune disease; global immune response to viral, fungal, and microbial; global-level immune expression drug responses in clinical trials
Autoantibody screening (immune response), protein–protein interaction, small molecule profiling, enzyme substrate, and antibody substrate profiling Helps in screening and validation of autoantibody markers
Thermo Fisher Scientific
S. Gupta et al.
Immunome protein arrays
ProtoPlex immune response assay featuring Luminex xMAP technology Yummy! dietary wellness and environmental allergy microarray tests and testing services AllerSpot companion animal allergy testing service Pathogen antigen arrays
Detect relative protein expression/biomarker discovery
Ray Biotech
Abnova
Biomarker discovery Protein expression profiling, biomarker identification, phosphorylation studies, disease versus healthy comparisons NA
Applications
Arrayit FULL MOON BioSystems
Antibody array
Company
Plasma scan 380 antibody arrays Apoptosis, cancer biomarker, AKT pathway, phospho antibody arrays Cell cycle, angiogenesis, AKT-phospho arrays Human angiogenesis array C1000, cytokine array Q2 Human cytokine array BD Clontech antibody array Pathway and phosphorylation antibody microarrays Apoptosis, cell cycle, and signal transduction arrays Panorama antibody arrays PEPperCHIP MERS-CoV Proteome Microarray Pamchip 4 HuProt arrays
Product name
Type
a) Commercially available protein array chips
Table 1. Commercially available modalities for setting up a protein array experiment: chips and arrayersa)
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Pepscan
Pep chip protease profiling
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NanoPrint 2 LM60-2 printer NanoPrint2 LM210-2 printer NanoPrint LM210 SpotBot Titan high-capacity DNA and protein edition microarrayers SpotBot 4 personal microarrayers SpotBot 3 personal microarrayers SpotBot 2 personal microarrayers SpotBot 2 protein Marathon and super marathon microarrayers Ultra-marathon I and II microarrayers Glass slide microarrayer Compact disc microarrayer XactII compact microarray system TopSpot D microarray printers Microgrid II Omnigrid micro Omnigrid accent VERSA spot printing workstation 2470 arrayer microarray printing platform sciFLEXARRAYER PiXDRO IP410
Arrayit
a) Extended information in Supporting Information Tables.
Aurora Biomed Aushon Scienion AG Meyer Burger
Labnext BioFluidix Digilab
V&P Scientific
Arrayjet
Microarrayer
Company
Biomarker discovery, protein–protein, protein–drug, protein–nucleic acid interaction NA
All applications Protein, DNA Protein or DNA CDs Various soluble molecules NA DNA and proteins DNA and proteins DNA and proteins NA DNA, proteins, cell lysates, and other molecules Diagnostics, genomics, proteomics Photovoltaics (CIGS, O-PV, Si wafer-based) PCB (classical and multilayer, 3D) semiconductors (sensors, power devices, (bio)MEMS) OLED (lighting, display, 3D) touch-screens printed electronics
Protein, DNA, and other biomolecules Protein, DNA, and other biomolecules NA Proteins All applications
Protein, DNA, and other biomolecules Protein, DNA, and other biomolecules Protein, DNA, and other biomolecules Protein, DNA
Applications
Promega
HaloLink protein array systems
b) Commercially available arrayers
Table 1. Continued
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proteins [38]. Thus, a variety of methods have evolved over the years to improve the surface chemistry of microarray platforms [28, 32, 39]. Some of these platforms have been commercialized to provide accessibility of these arrays at a mass scale, and subjected to standardization for improving the overall performance (Supporting Information Table 2). Protein production and purification are important aspects of array fabrication. Scientists utilize recombinant DNA technology to produce proteins on a mass scale for protein printing. However, downstream complexities in protein purification have led to improvisations in printing DNA or mRNA on the chips, which can be translated in situ using cell-free expression systems (CFES). These efforts have now opened avenues for cell-free expression arrays. Although, CFES found their applications in the area of protein microarrays relatively recently, their roots date back to the landmark experiment by Marshall Nirenberg and Heinrich J. Matthaei in 1961. In their study, crude cell extracts were used to translate a poly-Uracil chain to help elucidate the genetic code [40]. These rationales have been extended toward production of high-throughput protein arrays by reducing the rigorous downstream process of protein purification. Cell extracts of wheat germ cells, rabbit reticulocytes, E. coli cells, and insect cells have shown great promise, in vitro, in translating proteins from template DNA [41]. Due to its tremendous applications in simplified protein production, CFES have been widely commercialized by Promega, Thermo Fisher Scientific, New England Biolabs, and Takara Clontech (Supporting Information Table 3). In addition, they have found great applications in the construction of cell-free expression arrays, such as Protein in-situ arrays, Multiple Spotting Technique, DNA array to Protein array, and so on. Pioneered by Joshua LaBaer, Nucleic Acid Protein Programmable Array (NAPPA) is among the most widely explored novel platforms for protein array fabrication [30]. Thermo Fisher Scientific’s 1-Step Human in-vitro Transcription and Translation system has gained immense popularity among the pioneers of NAPPA technology [39]. Another popular commercial innovation in this area has been in the surface chemistry involved in Promega’s HaloLink Protein Array System [42]. This system utilizes a mutated hydrolase protein, HaloTag protein, which binds covalently with its ligand on a hydrogel coated slide. This makes it a powerful platform for researchers to use CFES to produce purified recombinant proteins, capture them, and perform stringent assays. It can thus be seen that, a remarkable progression of assay strategies has been achieved and surface chemistry maneuvers have accordingly evolved to suit these developing applications of protein arrays (Supporting Information Tables 1–3). Certain platforms have gained immense popularity depending on their applications while some assays have failed to make a commercial impression. This may be due to the complexities in chip fabrication, standardizations implied in an industrial setting, or isolated research goals that they may help in achieving. C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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3
Protein arrayers
Another crucial step in high-throughput chip development is protein printing, which involves sophisticated robotics. The protein-printing instrument, also termed as protein arrayer, is primarily a robot, which prints the analyte or lysate on the chip. Protein arrayers are made of a few basic components. These consist of a source plate with protein samples in a multiwell format. A computer-controlled machine head containing pins takes up the sample from the source plate and is directed by the Cartesian coordinate axis motion control to help spot the proteins in destination zone. The machine head spots the protein in the destination zone, at a predefined location, all at once, or in multiple cycles, depending on the specification of the arrayer [15]. The two types of protein arrayers, contact and noncontact, differ primarily in their machine heads. Contact printers consist of pins that could either be solid pins or split pins. The pin of a single contact printer pin is dipped at a time, into a single well from multiwell plate enabling it to take up a defined volume of the sample [15]. These pins then move to the destination zone, where the pins make contact with the array slide and the protein is spotted. Split pins are hollow and have been developed to take up larger volumes to ensure that the volume taken up can be split among slides, thus negating the need for reloading. Although split pins increase the throughput of the arrayer and reduce the time to print arrays, they are more prone to blockage due to their hollow nature. Solid pins on the other hand are more robust and resistant to damage [15]. Proteins are often solubilized in various buffers or solvents, causing variations in viscosity of samples. A major drawback of the use of split pins is the variations in volume of samples taken up and dispensed owing to differences in viscosity of the samples. The inability to overprint due to the limited volume range aspirated at a time is an inherent disadvantage of contact printer. Uneven surfaces hinder arrayers from making contact with the chip surface and result in “missing spots.” In spite of these limitations, contact printers continue to remain popular, especially in the commercial settings, achieving up to 500 nm resolutions, dispensing volumes in nanoliter, and printing over 210 chips per batch (Table 1b). The shortcomings of contact printers encouraged researchers to opt for a faster method for protein spotting, such as noncontact printers or ink jet printers [15]. A major advancement in contact printing is the microcontact printing technology. This technology allows for printing of several features at a time. Modifications of microcontact printing include reactive microcontact printing, supramolecular micro contact printing, dip pen nanolithography, and polymer pen lithography [43]. Noncontact printing is essentially piezo electric printing, where the machine head contains a borosilicate glass capillary surrounded by a piezoelectric element collar. On voltage application, a droplet of 0.5 to 20 nL volume is spotted on the chip. These printers are highly reliable, accurate and help in www.proteomics-journal.com
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Proteomics 2016, 16, 1297–1308 Table 2. Commercially available modalities for data acquisition and analysisa)
a) Commercially available microarray scanners Company
Scanner
Applications
Molecular devices
GenePix 4300A /4400A GenePix 4100A GenePix 4000B Tecan LS reloaded Tecan power Arrayit InnoScan 1100AL
Nucleic acid, protein, tissue, and cells
Tecan Arrayit
Arrayit InnoScan 910 and 910AL
Innopsys
Agilent Technologies
SpotLightTM Two-Color and SpotLightTM Turbo Six-Color Microarray Fluorescence Scanners ArrayPix Microarray Fluorescence and Colorimetric Plate Scanners InnoScan 1100 AL InnoScan 710 IR InnoScan 910 InnoScan 710 InnoScan 300 SureScan Microarray Scanner
DNA and protein arrays—printed in 96- or 384-well formats High density DNA and Protein microarrays Low-, medium-, and high-density microarrays containing nucleic acid, protein, transfected cells, etc. Low-, medium-, and high-density microarrays containing nucleic acid, protein, etc. Compatible for surface chemistries such as glass, plastic, hydrogels, nitrocellulose, nylon
NA
Proteins, DNA, peptides, and glycans Protein arrays, ELISA, RPPA, enzymatic microarrays Proteomics, genomics
b) Commercially available microarray software Company
Software
Applications
Molecular Devices
GenePix ProMicroarray Image Analysis Software Array Pro Analyser 6.3
Detection of nucleic acid, proteins, cells, and tissues
Media Cybernetics Innopsys Thermo Fisher Scientific Bio-Rad Laboratories
MAPIX ProtoArray Prospector Software v5.2.3 VersArray Analyzer Software
Microarray gene expression, typing, sequencing analysis, and HTS Nucleic acid, protein Protein studies DNA, protein, tissue
a) Extended information in Supporting Information Tables.
recovering unused samples. However, the printing pins are known to be very delicate, expensive, and difficult to replace. Noncontact printers sometimes pose problems of merged spots due to which contact printers are preferred over noncontact printers. There are however, few commercial noncontact printers, which are fast, and can print up to 1000 slides in a high-density format, per batch (Table 1b).
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Microarray readers
Interpretation of microarray data critically depends on detection probes involved in experimentation. Protein microarray detection strategies have widely been categorized into labeled and label-free methods [16, 42]. The strong foundation laid by DNA microarray technologies in the area of confocal laser scanners made it the most appropriate starting point for con C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
structing protein array scanners. Consequently, label-based methods continue to be the most used protein microarray detection technique. Scanners, which can capture fluorescent labels, also include cooled CCD cameras [15]. Most commercial scanners employ excitation by lasers and a PMT detector (Table 2a). Alternatively, a uniform illuminating device emitting filtered white light from an LED source is detected using a cooled CCD in combination (Table 2a). While confocal lasers have been advanced to deploy multiple lasers to detect a wider range of dyes, the CCD-based scanners use multiple filters to achieve appropriate excitation and emission for the same purpose. Few commercial scanners also feature a multiple slides loading function for reducing the manual labor involved in analyzing a large number of slides individually. In these cases, the spot intensities reflect the extent of interaction between the sample and analyte. Detection of bead arrays requires fluid-handling components such as a liquid-handling www.proteomics-journal.com
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robot, which aspirates the beads into the reader. This is followed by excitation of the fluorescent-labeled or coded beads using a laser emitter, which is sensed using PMT detectors. Other label-based methods include colorimetric substrates, gold nanoparticles, quantum-dots, dye doped nanoparticles, and bio-barcodes [42], which utilize similar scanning devices as described for detecting fluorescent-labeled proteins. A major concern with the use of protein labels such as fluorescent labels, radiolabels, and fusion proteins was the fact that the native protein structure can get modified during the labeling process. To circumvent these drawbacks, labelfree detection approaches using MS, biosensor technology such as surface plasmon resonance, microcantilevers, carbon nanotubes and nanowires, and SELDI-TOF were devised to negate the use of any secondary reactants [42,44,45]. Commercial devices dedicated for scanning protein interactors in an array format have particularly been popular with the SPR platform. SPR imagers detect the binding of a ligand to its respective analyte through a shift in refractive index that occurs during the binding process detected by surface plasmons in dielectric interface. One of the first few reported studies used SPR imaging to identify protein interactions in human papilloma virus E6 protein to detect triple interaction between p53 and ubiquitin ligase E6AP in a SPRi-microarray format [46]. In the recent past, there have been several studies incorporating SPRi for large-scale analysis of protein interactions [46–49]. There are a few commercially available SPR detection platforms. Biacore remains a popular solution for most researchers working in the SPR arena. The octet platform based on biolayer interferometry is another promising platform for high-throughput analysis in a cost-effective setting; however, other label-free detection platforms still require attention [45] (Supporting Information Table 6). Other detection platforms at the proof-of-concept level include, quartz crystal microbalance (QCM), which has been used extensively to study protein or macromolecular binding to films. It is one of the most cost-effective methods for studying biomolecular interactions using the fundamental properties of piezoelectric effect [50]. OLED-based biochips, now extended to protein microarray applications, are another next-generation method of detecting protein interactions [51]. Tabakman et al. in 2011 used nanostructured plasmonic gold film coated biochips, which could detect cancer antigens from patient sera by fluorescing in the near-infrared spectrum to detect protein interactions at femtomolar range [52].
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Data analysis software
In the multi-omics era, high-throughput data acquisition has been relatively easy and has been obtained in a large volume; however, it has been a hurdle to analyze and interpret such datasets. DNA microarray has laid the foundation for microarray data analysis, and similar analysis strategies are now utilized for protein microarrays. The ultimate goal of data analysis for any given protein microarray experiment is C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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to mark the intensity of a given spot as the function of the interaction between the sample and analyte. To achieve this target, sufficient proficiency in image processing and data acquisition is needed. A microarray slide is made of numerous subarrays, which are theoretically identical and spatially distributed in an equidistant manner. However, in practice, this is not always precisely accurate and hence it is not as straightforward to superimpose a program-based array gridmap, predefining a given spot position, to extract data from that given spot. It is often observed that the true signal may lie beyond a grid spot resulting in background noise capture. The protein alignments across various subarrays are not always uniform. Artifacts due to contaminants such as dust particles are another major hurdle in image processing. These inherent challenges make automation in image processing and data acquisition difficult. Even though there are several segmentation algorithms available to reduce manual interventions, a degree of manual flagging is necessary to mark any “low-quality” spots. Once data acquisition is performed, it needs to be pre-processed for background correction and normalization. Software such as GenePix ProMicroarray Image Analysis Software and ProtoArray Prospector Software are few commercially available applications available along with scanning devices for image processing, data acquisition, and pre-processing. The dynamic nature of protein arrays makes every individual experiment unique. Data analysis strategies for protein microarrays have mainly evolved from DNA microarrays [29, 53]. A general outline of data analysis and interpretation of any protein microarray experiments is as follows. (1) Data acquisition: Data are exported in .gpr, .CEL, or .txt format. These contain quantifiable values obtained post image acquisition for each protein spot (feature). Statistical tests applied on these values are central to these data analysis strategies. (2) Pre-processing of the data—background correction and normalization: The true value of a feature is obtained after subtracting the background intensity from the foreground. This step is called background correction. After background correction, the (normexp + offset) from LIMMA (linear model for analyzing differential expression) is one of the methods used to normally distribute the background intensities. The foreground signal is treated as an exponent to stabilize any resulting variance [13]. This is followed by data normalization. Normalization is essential for removal of bias resulting from variables such as printing aberrations, dye-bias, or day-to-day variations. Quantile normalization, variance stabilizing normalization, cyclic loess, and robust linear model normalization are few commonly used normalization strategies employed for microarray data analysis [53, 54]. (3) Differentially expressed proteins: Statistical tools such as Student’s t-test (normal distribution of data), rank product (nonparametric), Wilcoxon rank-sum test (nonparametric, normal distribution approximation), significance www.proteomics-journal.com
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analysis of microarrays (SAM), linear model for microarray, and M-statistic are commonly used to build a list of differentially expressed proteins [53] with the null hypothesis being that no gene is differentially expressed. In consideration of the dynamic nature of the protein array platform, statisticians often chose a combination of these algorithms to improve the output of the data, especially if there are underlying assumptions of data distribution. Open source platforms such as Bioconductor and BRBarray can be flexibly used in accordance to each individual experiment. However, unlike DNA microarrays, software that could holistically perform data analysis without a platform or compatibility barrier has not been commercialized. Tools such as ProCAT have been developed especially with protein microarrays in mind. Other softwares that enable preliminary data processing are listed in Table 2b. (4) Short listing of differentially regulated proteins: Benjamini–Hochberg correction improves the stringency of short-listing proteins. This can be further improved by employing a fold change cutoff along with the corrected p-value cutoff. The data can then be subjected to dimensionality reduction techniques such as correspondence analysis [13]. Correspondence analysis provides a shorter list of differentially expressed proteins that are statistically more significant. Recursive feature elimination models such as support vector machine can be used to elucidate a list of significant classifier proteins from this list [13]. Multidimensional scaling plots are used to visually check for differentiation between any two cohorts [13]. In addition to this, protein interaction, metabolic networks, GO and gene set enrichment analysis can be performed to completely understand pathobiology of the disease under study [13]. Bioinformatic analysis typically involves integration of proteome data with annotational databases, such as GO [55], protein domains (InterPro, PFAM) [56], and pathway database (KEGG) [57] to determine if any of the biological properties, domains, or pathways are over- or underrepresented. DAVID [58], GoMiner [59], and Cytoscape [60] plug-ins such as BINGO [61] are examples of readily available software that can be used for these purposes. In addition, Bioconductor [62] within the R statistical platform [63] is used frequently, which requires some more programming expertise but offers broader capabilities and flexibility [64]. Thereafter, biochemical network models are built in a way so as to explicitly represent the mechanistic relationships between genes, proteins, and the chemical interconversion of metabolites within a biological system. A detailed description of the process of reconstructing, curating, and validating these biochemical network models has been reviewed by Feist et al. [65]. Gene sets can now readily be used to conduct gene set enrichment analysis (GSEA) studies to determine whether a priori defined set of genes show statistical significance, concordant differences between two biological states. Metacore GeneGo by Thomson Reuters, Ingenuity Pathway Analysis by C 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Qiagen, and Pathway Studio by Elsevier are some widely used commercial pathway analysis and networking tools [13, 66]. An overview of the commercially available software indicates that currently there are no comprehensive tools available in a singular pipeline for performing image processing, data acquisition, normalization, pre-processing, statistical analysis, listing differential proteins, building classification models and providing a holistic biological understanding of the data. The vast variation between different assays makes it extremely challenging to automate downstream data analysis. However, design of software with integration of some of the above-mentioned features could avoid the variation arising from multiple reference databases and parameter definitions.
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Conclusions
Protein interaction has been a subject of great interest among researchers from multiple disciplines and is at the heart of problems of societal value. However, the modalities available to study them are complex and require great sophistication in technologies. The complex nature of proteins has posed hurdles in areas such as array fabrication, printing, scanning, and data analysis. Commercialization has made these platforms easily accessible to researchers. While chip fabrication and printing have been the focus of innovations, the industry has failed to tap into the complete potential of the scanning and data analysis software. DNA microarrays have been an instrumental forerunner for protein arrays and had laid the foundation in developing experimental and analysis strategies, albeit with certain limitations. As a result of these staggered innovations, where the industrial limelight has been on developing functional and antibody arrays, the need for automation in the downstream processing of microarray slides has emerged. Detection strategies have witnessed great ideas as discussed previously, in spite of which the commonly used scanners continue to employ optics capable of detecting fluorescent cyanine labels. Platforms that could inherently detect and eliminate background noise, auto-fluorescence and utilize novel label-based techniques could be the beginning of such ventures. It is also important to focus efforts in coupling protein microarrays with label-free biosensors, miniaturizing these platforms through next-generation engineering, and increasing its throughput for future alternatives. Data analysis strategies and interpretation software development have been a daunting task for protein microarray studies due to their complexity and variation in experimental goals. However, there is a void in the area of a self-sufficient, user-friendly interface for data processing, analysis, and interpretation. If one were to review the progress of protein arrays in the past decade, it would become apparent that the technology has evolved tremendously to meet several of its challenges. Diversity in protein size, spatial location of proteins in the cell, PTMs, and hydrophobic nature of the proteins pose challenges in protein purification, maintenance of activity, www.proteomics-journal.com
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and orientation for functional assays using protein arrays. The emergence of CFES has largely been responsible for overcoming the challenges of high-throughput expression and purification for a diverse class of proteins. With CFES available from a variety of organisms, the challenge of obtaining “near-native” configuration of proteins also seem to have now been combated. NAPPA and Halotag technologies have helped in presenting hidden or buried epitopes in a protein, thus aiding the development of better assays [67]. A large number of biochips are now available which allow the study of posttranslationally modified proteins (Table 1, Supporting Information Table 1). Researchers also opt to purify proteins of interest and modify them in vitro to design custom-made chips. In the past few years, various avenues to study PTMs using protein array platforms, have allowed the identification of nitrosylations [68], phosphorylations [69], and understanding of enzyme activity [70, 71]. Industry plays a crucial role to propel any technology by easing its accessibility and maintaining standards. Similarly it has created a huge impact in propagating protein arrays across the globe, making it a useful tool for researchers studying signaling networks, protein interaction, drug target development, and biomarker discovery. Protein microarrays have been successfully used in applications ranging from identification of allergens in animal products as prognostic markers for building tolerance [72], screening toxins, and identification of virulence and heat tolerance in plant pathogens [73]. The most promising applications of protein microarrays have been in the area of clinical biomarker discovery, which is also the focus of many researchers utilizing this platform. Over the last 5 years, several studies have shown successful application of this technology in providing clues into the pathophysiology of many diseases and putative biomarkers for both infectious and noninfectious diseases [74–82]. However, the need for more cost-effective, robust, reproducible instrumentation and holistic platform for data analysis is required to extend this technology to the larger scientific community. While meeting these challenges remains a Herculean task, technologies such as next-generation sequencing (NGS) and its emergence continue to inspire innovators to fill the lacunae in array technology. With future innovations, instrumentation and data analysis strategies, protein microarrays can achieve their true potential of being a powerful platform for studying functional biology. This work is supported by the grant from Department of Biotechnology, Government of India (DBT; Project number: BT/PR4599/BRB/10/1042/2012). The authors thank Dr. Veenita Grover Shah and Saicharan Ghantasala for their input in improving this manuscript. The authors have declared no conflict of interest.
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