There is increasing appreciation among researchers and clinicians of the value of investigating biology and pathobiology at the level of cellular kinase (kinome) activity. Kinome analysis provides valuable opportunity to gain insights into complex biology (including disease pathology), identify biomarkers of critical phenotypes (including disease prognosis and evaluation of therapeutic efficacy), and identify targets for therapeutic intervention through kinase inhibitors. The growing interest in kinome analysis has fueled efforts to develop and optimize technologies that enable characterization of phosphorylation-mediated signaling events in a cost-effective, high-throughput manner. In this review, we highlight recent advances to the central technologies currently available for kinome profiling and offer our perspectives on the key challenges remaining to be addressed.

Protein phosphorylation

Through their ability to generate a spectrum of functionally distinct protein variants, post-translation modifications serve to dramatically expand the magnitude and functional complexity of the proteome. The regulated and reversible nature of most of these modifications enables dynamic responses from a static molecular infrastructure, providing a time- and energy-efficient mechanism to achieve information transfer and/or regulate cellular responses. A variety of post-translational modifications have been described, including, but not limited to, methylation, ubiquitination, glycosylation, and phosphorylation [14]. In terms of the frequency of occurrence, importance of the associated biological events, and medical significance, phosphorylation is among the most important post-translational modification [3,5,6].

Protein phosphorylation is catalyzed by the class of enzymes known as the protein kinases. Within humans, 518 kinases, including 32 atypical varieties, have been described [7]. These kinases mediate the transfer of the terminal phosphate group from ATP to hydroxyl group containing residues (serine, threonine, or tyrosine) of specific positions of specific proteins. Within the human phosphoproteome, the relative prevalence of phosphoserine, threonine, and tyrosine residues is ∼1000:100:1 [8,9]. Estimates of the number of phosphorylation events in the human phosphoproteome vary from tens of thousands to a hundred thousand unique phosphorylation events [10]. In the context of the estimated 25 000 members of the human proteome [11,12], this provides important context regarding the frequency of these modifications. The action of kinases is functionally opposed by the protein phosphatases that mediate dephosphorylation of proteins through hydrolysis. There are an estimated 145 human protein phosphatases [13,14].

Kinase-mediated protein phosphorylation has a central role in the regulation of virtually every cellular process, including immunity, cell growth and division, and metabolism [15,16]. Supportive of the intimate relationship between kinases and phenotypes, there are many examples where kinase defects are associated with pathological consequences. For example, kinases have been implicated as causative or contributing factors in many cancers [17,18], inflammation [1923], neurodegenerative diseases [2427], infectious diseases [2831], and various other cellular and physiological disorders.

Understanding the contributions of kinases to pathophysiological states informs the molecular basis of the disorder, enables biomarkers corresponding to disease prognosis, and informs rational strategies and targets for therapeutic intervention. To this final objective, the critical role of kinases in regulating medically important phenotypes, coupled with their ‘druggability’, makes the kinases highly attractive targets for therapeutic intervention [32]. Kinases are currently the second most frequently targeted class of enzymes [33] with 40 kinase inhibitors licensed for clinical use by FDA [34,35]; an additional 500 inhibitors are under investigation in over 3000 clinical trials, and thousands of other inhibitors are under investigation in various stages of preclinical development [32,36]. Libraries of kinase inhibitors, covering ∼20% of the human kinases, represent a valuable resource for validation of experimental hypotheses as well as enabling rapid translation of experimental results toward clinical applications [35].

Collectively, given the role of phosphorylation in regulating cellular phenotypes, as well as their contributions to disease (both as causative agents and therapeutic targets), defining cellular patterns of protein phosphorylation through kinomics is a central priority for clinicians and researchers alike. An ultimate objective of any omic analysis is to define molecular events that correlate with, and anticipate, meaningful biological responses; in other words, to be able to explain phenotypic differences at a molecular level. In this regard, given the omnipresence of phosphorylation as a central and direct mechanism for the regulation and initiation of cellular responses, kinomic investigations have considerable potential to enable unobstructed insight and infuse predictive power into phenotypes.

Kinomics versus phosphoproteomics

There are two branches of the research efforts devoted to defining phosphorylation-mediated signal transduction. These include kinome analysis (which prioritizes the activities of the cellular kinases) and phosphoproteome analysis (which defines dynamic patterns of phosphorylation within the proteome). While there are obviously conceptual linkages in these approaches, they are based on distinct experimental techniques and priorities. A detailed comparison of the experimental differences and philosophies of kinome and phosphoproteome analyses is available elsewhere [37]. For the current discussion, it is important to emphasize that kinome analysis serves to define the activities of kinases that are responsible for mediating many phosphorylation events to regulate distinct, but probably complementary, biological responses. In contrast, phosphoproteome analysis seeks to provide information about the phosphorylation status of every phosphoacceptor site within the proteome. The conceptual difference between the two approaches is perhaps best demonstrated by the consideration that complete coverage of the kinome could be achieved with 518 data points, whereas comprehensive coverage of the phosphoproteome would require in the order of 100 000 data points.

Experimentally, the phosphoproteome is defined by either employing reagents that are specific for particular phosphorylation events, like phosphorylation-specific antibodies, or through the application of technologies that enable screening of the entire proteome in order to define specific phosphorylation events, such as mass spectrometry [38]. For each of these approaches, the costs and limitations of resources, such as the availabilities of phosphorylation-specific antibodies or mass spectrometry equipment, restrict the opportunity for routine and comprehensive characterizations. In contrast, kinomics, by prioritizing conserved enzymatic activities, is better suited for translation into high-throughput platforms, provided that appropriate substrates can be identified. In this regard, while proteins are the natural substrate of kinases, there is evidence that the phosphorylation sites can be effectively represented with short peptides reflecting the sequence immediately surrounding the phosphorylation site [39]. The use of peptides as surrogate substrates for kinases offers tremendous advantage in terms of their cost, availability, and amenability to array technologies [10,40].

Forces driving the development of new kinome technologies

The advancement and application of kinome technologies is an iterative process in which successful applications drive further advancements and further advancements enable applications of grander scale and importance. In their relatively short history, kinomics has evolved from a niche technology to widespread application in a variety of biological contexts, including investigations within humans [27,41], several livestock species [4244], plants [45], and insects [46]. Within each of these contexts, kinomics has proved a highly effective approach for understanding basic biology, such as elucidating novel signaling pathways [31,4749], identifying novel substrates [5052], understanding the molecular mechanisms of complex phenotypes and disease states [48,53], and characterizing host–pathogen interactions [54,55]. More specific to the realm of human applications, kinome analysis has provided valuable contributions toward clinical applications, including biomarker discovery, including prognostic biomarkers [56,57], therapy-predictive biomarkers [58,59], and pharmacodynamics biomarkers [60,61]. Furthermore, with the emerging priority for the use of kinase inhibitors as therapeutics, there has been considerable emphasis on the application of kinome analysis for identifying therapeutic targets [6264] as well as understanding the mode of action of therapeutic compounds [65,66]. Some of the conceptual applications of kinome analysis, and representative, but by no means exhaustive, supporting publications, are presented (Table 1). While this is an impressive resume of accomplishments for the kinomics approach, we believe that this is only beginning to scratch the surface of the true potential of this research perspective.

Table 1
Applications of the kinome array technology

The table enlists the publications based on the application of the arrays, which are further categorized based on the technology utilized.

Application References Technology 
Understanding biology 
 Pathway determination [31,47Peptide array 
[48,95,96PamChip 
 Compound mode of action [97Peptide array 
[65,98,99PamChip 
 Biochemical characterization [100Peptide array 
[101103PamChip 
 Substrate identification [52Peptide array 
[50,104,105PamChip 
 Host–pathogen interaction [29,54,72,106,107Peptide array 
[28,31PamChip 
 Kinetics [30Peptide array 
[108,109PamChip 
Microarray technique enhancement [110Peptide array 
[111,112PamChip 
Identifying biomarkers 
 Classification biomarkers [113Peptide array 
[114PamChip 
 Prognostic biomarkers [115Peptide array 
[56,116,117PamChip 
 Therapy-predictive biomarkers [61Peptide array 
[58,118PamChip 
 Kinotypes [80Peptide array 
 PamChip 
Therapeutic targets 
 Target discovery [43Peptide array 
[49,62PamChip 
 Target validation [119Peptide array 
[60,120,121PamChip 
Diagnostic [55Peptide array 
[122124PamChip 
Pathogenesis [125Peptide array 
[126,127PamChip 
Application References Technology 
Understanding biology 
 Pathway determination [31,47Peptide array 
[48,95,96PamChip 
 Compound mode of action [97Peptide array 
[65,98,99PamChip 
 Biochemical characterization [100Peptide array 
[101103PamChip 
 Substrate identification [52Peptide array 
[50,104,105PamChip 
 Host–pathogen interaction [29,54,72,106,107Peptide array 
[28,31PamChip 
 Kinetics [30Peptide array 
[108,109PamChip 
Microarray technique enhancement [110Peptide array 
[111,112PamChip 
Identifying biomarkers 
 Classification biomarkers [113Peptide array 
[114PamChip 
 Prognostic biomarkers [115Peptide array 
[56,116,117PamChip 
 Therapy-predictive biomarkers [61Peptide array 
[58,118PamChip 
 Kinotypes [80Peptide array 
 PamChip 
Therapeutic targets 
 Target discovery [43Peptide array 
[49,62PamChip 
 Target validation [119Peptide array 
[60,120,121PamChip 
Diagnostic [55Peptide array 
[122124PamChip 
Pathogenesis [125Peptide array 
[126,127PamChip 

Technologies for kinome analysis

Peptides for kinome analysis

There are inherent differences in the nature of the biological signal that impose particular advantages and challenges to the various omic approaches (transcriptomics, proteomics, and metabolomics). For example, the property for nucleic acids to hybridize with complementary partners serves as a simple, generic basis for quantification of gene expression. Similarly, while there is considerable diversity in terms of the number of kinases and their substrates, these modifications occur through a highly conserved catalytic mechanism. In particular, most kinases recognize and modify their targets based on the residues immediately surrounding the phosphoacceptor site, largely independent of higher-order structural influences. This enables the use of short peptides of specific sequences as surrogate substrates to monitor kinase activities. The use of peptides as substrates to assay kinase activity is a shared central principle of the vast majority of modern kinomic approaches. The differences between the specific technologies relate primarily to distinctions in peptide presentation with further specialization with respect to the generation and detection of the phosphopeptide signal. Representative, but not exhaustive, examples of the successful application of each approach are highlighted in Table 1.

Peptide arrays for kinome analysis

The peptide arrays used for kinome analysis are similar in design and philosophy as those employed for gene expression analysis. The key difference is that rather than employing nucleotide strands that are complementary to the gene of interest, each spot represents a population of identical peptides representing a particular phosphorylation site. The peptides, which are typically ∼15 amino acids in length with the phosphoacceptor site in the central position, are chemically coupled to a physical support, which is typically a glass slide (Figure 1). To facilitate evaluation of the technical reproducibility, each peptide is printed as multiple spots on the same array. Varying iterations of the arrays have used from three to nine technical replicates for each unique peptide sequence. The arrays are incubated with cellular lysates in which active kinases phosphorylate peptides whose sequences match the recognition motif of that particular kinase. The extent of phosphorylation of each peptide on the array, which is quantified either through the use of radiolabeled ATP or phosphorylation-specific fluorescent stains or antibodies, reflects the abundance and activation status of the corresponding kinase. Experiments are typically performed considering samples representing treatment and control groups, such that the emerging data represent relative change, rather than absolute values, of kinase activity.

Kinome analysis with peptide arrays.

Figure 1
Kinome analysis with peptide arrays.

(A) Samples can represent cell lines, isolated cell types (like peripheral blood mononuclear cells) or tissues. (B) Cellular lysates are incubated on the array in the presence of ATP [94]. (C) Arrays are either commercially purchased or generated through selection of peptides from online databases [3,74] or phosphorylation site prediction software such as DAPPLE 2 [75]. (D) The signal from phosphostained or radio-labelled arrays are detected and quantified. (E) The output of the scanned arrays is analyzed by using software platforms such as PIIKA 2 [89] or other approaches [88]. (F) Results from kinome analysis are depicted in different formats such as heatmaps and principal component analysis (PCA) plots. (G) Biological events suggested through kinome analysis are typically validated through independent approaches such as functional assays or phosphorylation specific antibodies. (H) Finally, results from the validation can be used for various applications.

Figure 1
Kinome analysis with peptide arrays.

(A) Samples can represent cell lines, isolated cell types (like peripheral blood mononuclear cells) or tissues. (B) Cellular lysates are incubated on the array in the presence of ATP [94]. (C) Arrays are either commercially purchased or generated through selection of peptides from online databases [3,74] or phosphorylation site prediction software such as DAPPLE 2 [75]. (D) The signal from phosphostained or radio-labelled arrays are detected and quantified. (E) The output of the scanned arrays is analyzed by using software platforms such as PIIKA 2 [89] or other approaches [88]. (F) Results from kinome analysis are depicted in different formats such as heatmaps and principal component analysis (PCA) plots. (G) Biological events suggested through kinome analysis are typically validated through independent approaches such as functional assays or phosphorylation specific antibodies. (H) Finally, results from the validation can be used for various applications.

PamChip

As an alternative to the array-based approaches, PamGene developed the PamChip system in which peptides are immobilized to an activated aluminum oxide surface that essentially represents a three-dimensional array. The peptides are of comparative length to those employed with peptide arrays; however, fewer peptides (phosphorylation events) are considered within an individual experiment. The PamChip arrays have 144 spots, whereas peptide arrays often have over 1000 spots. The cellular lysate solution is pumped through the matrix, providing kinases the opportunity to recognize and modify their peptide substrates in a manner analogous to the peptide arrays (Figure 2). Each round of pumping will result in further phosphorylation of the peptides that can be detected and quantified in real time using fluorescently labeled phosphospecific antibodies. The porous matrix, relative to the traditional flat array, is suggested to offer greater surface area for presentation of peptides, and the continuous flow is suggested to provide more sensitive and accurate assessment of kinase activity [67].

PamChip kinome analysis.

Figure 2
PamChip kinome analysis.

(A) Cellular lysate is pumped through the porous surface of the array [67]. (B) Kinases within the lysate phosphorylate their peptide targets. (C) The fluorescently labeled antibodies allow the detection of the phosphorylated peptides and the camera captures the intensities. (D) The readout of the intensities is recorded and tabulated. (E) The readout is in the form of the kinetic measurement, heatmaps etc. (F) Finally, the results from the validation can be used for various applications.

Figure 2
PamChip kinome analysis.

(A) Cellular lysate is pumped through the porous surface of the array [67]. (B) Kinases within the lysate phosphorylate their peptide targets. (C) The fluorescently labeled antibodies allow the detection of the phosphorylated peptides and the camera captures the intensities. (D) The readout of the intensities is recorded and tabulated. (E) The readout is in the form of the kinetic measurement, heatmaps etc. (F) Finally, the results from the validation can be used for various applications.

Bead-based kinase assays

Bead-based assays are a third option for presentation of peptides to assess kinase activity. In this system, peptides representing specific phosphorylation events are linked to beads through an acrylamide linker, such that each bead represents a population of identical peptides [68,69]. A mixture of beads, each representing a different phosphorylation event, is mixed with cellular lysates within a 96-well plate. Sylvester and Kron used Luminex beads in conjunction with phosphorylation-specific antibodies to enable simultaneous identification of the peptide target and its extent of phosphorylation through measurement of two-channel fluorescence [68,69]. This approach, however, has not seen the same extent of development or application as the peptide array or the PamChip approaches.

Validation of kinome results

Within any experimental approach, there is the potential to introduce biases or experimental artifacts that cloud the insight of true biology. This is also true of kinome analysis and includes variables that are unique to investigations of phosphorylation-mediated signal transduction. These influences, as well as measures employed to ensure the biological relevance of the emerging results, have been discussed extensively elsewhere [10]. Briefly, a central philosophy of our approach is that kinome analysis should be used to suggest biological events that are then validated through independent techniques. These independent techniques typically include phosphorylation-specific antibodies, kinase inhibitors, and functional assays.

Recent advances in kinome analysis

Customized arrays

There are many commercially available products that enable researchers to perform kinomic investigation with minimal investment of time or money. These include prefabricated peptide arrays as well as PamGene kits. These ‘off-the-shelf’ options have proved to be robust and effective tools and have enabled many successful kinomic investigations (Table 1). Some laboratories, however, have taken advantage of the opportunity to generate customized arrays through selection of peptides that represent phosphorylation events that are of particular interest. In the design of customized arrays, our approach has typically been to have approximately two-thirds of the peptides on the array representing phosphorylation events associated with a broad spectrum of the central signaling pathways in order to facilitate novel discovery. The remaining peptides reflect hypothesis-driven selections that enable better interrogation of priority phosphorylation events. For example, our group conducted many investigations of host–pathogen interactions utilizing peptide arrays that prioritize phosphorylation events associated with immune responses [70,71]. The emphasis toward immune responses evolved into the generation of ‘immunometabolism’ arrays based on the growing appreciation of the functional overlap and integration of metabolic and immune responses [72,73]. Most recently, arrays customized toward specific diseases, including breast cancer and Huntington's disease, have been developed and applied (unpublished results).

The development of customized arrays can be achieved through manual selection of phosphorylation events from publically available databases such as PhosphoSitePlus [3] or PhosphoELM [74]. These resources provide information on phosphorylation sites that have been characterized through low- and high-throughput approaches, like site-directed mutagenesis and mass spectrometry, respectively, as well as provide information on the supporting publications. Peptides can be selected from these lists based on biological interest in the phosphorylation event in combination with confidence in the occurrence of the modification, with priority given to phosphorylation events supported by extensive low-throughput characterizations. In addition, many computational tools have been developed that enable prediction of phosphorylation events based on sequence similarity to phosphorylation events described in other species [3,75] or as a function of the presence of kinase-associated recognition motifs [7678]. While the human phosphoproteome is among the most extensively characterized, many phosphorylation events have been defined in other species that have yet to be characterized in humans.

The manual selection of peptides can represent a daunting and time-consuming process. The latest iteration of DAPPLE 2, a software platform that predicts the phosphoproteome (or other post-translational modifications) of a species of interest, helps to facilitate this process by categorizing phosphorylation events, and the associated peptides, based on gene ontology terms, keywords, and signaling pathways [75,79].

While the priority of the current review is technological advances for human kinomic investigations, it is important to appreciate that the opportunity for customized arrays extends to the development of species-specific arrays that enable kinomic investigations of nonhuman species. To date, species-specific peptide arrays have been created for cattle [44], pigs [80], rodents [81], honeybees [46], chickens [82] horses, dogs, and others. Through the applications of DAPPLE 2, predicted phosphoproteome databases have been generated for hundreds of species and serve as an important resource for the development of arrays for these species [8385]. In addition to inherent scientific and economic interest of these species, many also serve as important models of human disease. The generation of research tools for these animals, in particular the large animal models of disease, such as pigs, enables levels of investigation that would otherwise not be possible.

Data processing

Some of the most significant recent advances in the kinome technologies have related to the methodologies employed for analysis of the emerging data. This is particularly true for kinome analysis through peptide arrays with the establishment of many new strategies and software platforms.

The data derived from kinome microarray experiments typically have several layers of complexity. The first layer consists of the unique peptides on the array, each of which is chosen to provide insights into the phosphorylation of a particular in vivo protein. For each of these unique peptides, there are typically between three and nine technical replicates on the same array (layer 2), allowing the technical reproducibility of the phosphorylation measurements to be ascertained. The third layer of complexity, which is not always present, involves the use of interarray technical replicates to further assess the technical reproducibility of the experiment. When experiments involve outbred animals, the fourth layer of complexity involves performing the array experiments for multiple animals, which allows differences in kinome responses that are due to genetic variability to be separated from differences in responses that are due to the condition being investigated. The final layer of complexity involves applying the kinome arrays to the different treatment and control conditions that form the focus of the study.

Given this complexity, sophisticated tools are required to analyze the data in a standardized and statistically valid manner. Early kinome microarray studies generally used software designed for analyzing DNA microarray data [22,86,87]. When we applied these methods to our own kinome array experiments, we found many problems, including overly stringent statistical tests (resulting in few peptides being identified as differentially phosphorylated), and normalization methods that both failed to retain the structure inherent in the data and did not result in normally distributed data, which many statistical tests rely upon. To address these issues, we developed a software package called PIIKA [88] and an improved version, PIIKA 2 [89], which were specifically designed for analyzing kinome microarray data. We compared PIIKA with the tools designed for DNA microarrays by stimulating monocytes with ligands that are known to activate certain signaling pathways, performing kinome microarray experiments on these monocytes, and then trying to recover the modified pathways. We showed that PIIKA was capable of identifying the known pathways with much greater statistical confidence than the tools designed for DNA microarrays. The latest version of PIIKA is available at http://saphire.usask.ca.

One of the most important elements of data processing is normalization, which seeks to bring all the arrays onto the same scale and ensure that the data exhibit a normal distribution. PIIKA 2, for example, uses an existing normalization method called variance stabilizing normalization (VSN). Recently, Scholma et al. [90] developed a new method for both intra-array and interarray normalization of kinome peptide array data that takes into account the peculiarities of kinome data when compared with gene expression data (e.g. lack of ‘housekeeping’ spots; the larger fraction of differentially phosphorylated spots in many comparisons). In addition to normalization, Scholma et al.'s method also includes novel quality control analyses of the raw image data. The possibility of integrating this method into PIIKA 2, perhaps as an optional alternative method to VSN, is currently being investigated.

Future directions

Idealized peptide substrates

As discussed, the primary technologies that are currently employed for kinome analysis (peptide arrays and PamChip) are similarly based on the assumption that biological protein phosphorylation events can be mimicked with peptide substrates. However, the accuracy of this assumption has not been thoroughly investigated across the range of kinase–phosphorylation site pairings. There are potential differences associated with specific kinases, as well as particular phosphorylation events, that probably influence the validity of the peptide-based approach. This could reflect different specificities and/or efficiencies of particular kinases in their ability to appropriately recognize and modify peptide targets, as well as differences in efficiencies of which particular phosphorylation events may be represented by peptides. Peppelenbosch et al. [91] propose identifying ideal peptide substrates for the various kinases and define many variables that could influence the ability of a particular peptide to serve as an effective kinase substrate. We are in agreement with this suggestion [10].

This will not be a trivial challenge, but it may be possible to employ bioinformatics approaches to define key recognition elements with the known phosphoacceptor sites of individual kinases or to employ peptide arrays presenting derivations of these recognition motifs as a mechanism to quantify the catalytic efficiencies of purified kinases against these substrates. Ultimately, the determination of standardized, if not idealized, kinase peptide substrates may be sufficient for advancement of the field.

In addition, it may also be important to consider that the selection of ‘ideal peptides’, which is based on the assumption of a static primary structure, may not reflect true biological complexity. For example, the ability of many kinases to recognize and phosphorylate their targets depends on the modification of neighboring residues, sometimes by other kinases. At this time, the extent to which these sequential phosphorylation events can occur within the confines of a short, tethered peptide is unclear.

Kinome versus phosphoproteome analysis

Greater consideration and rationalization are warranted of whether the extent of phosphorylation of a particular peptide is interpreted to reflect the relative activity of the modifying kinase or the extent of phosphorylation of that specific site in the context of the biological protein. In the first instance, the array is functioning as a kinome array (quantifying kinase activity), whereas in the second scenario it would be more appropriately described as a phosphoproteome array (anticipating levels of phosphorylation of specific sites of specific proteins). While the design of the arrays is consistent with quantification of relative levels of kinase activities, many investigations have demonstrated that the relative extent of phosphorylation of a particular peptide correlates with that of phosphorylation of that same site in the context of the proteome [37,92]. While there is obviously a link between the two, the distinction has some obvious functional importance. Specifically, could an array of 518 substrates, each representing an idealized substrate of the 518 human kinases, provide comprehensive coverage of the kinome? Or, would a comprehensive array require ∼100 000 peptides to represent each of the specific phosphorylation events within the proteome?

Kinotypes

There is evidence to suggest that individuals of the same species may possess distinct, temporally stable patterns of signaling activity. This hypothesis is based on a recent investigation that reported both species and individual-specific patterns of kinome activity, termed kinotypes, within the peripheral blood mononuclear cells of humans and pigs [80]. Most optimistically, this suggests that it may be possible to monitor the kinome within the context of personalized medicine in order to anticipate such critical characteristics as predisposition to disease, disease onset, and progression, as well as to provide a mechanism to select and monitor treatment efficacies. Notably, another group, using an alternate approach for data analysis and based on comparison of signaling profiles of the same cell type of two species, reported that signaling profiles are more dependent on cell type rather than species or individual [40]. We, however, do not feel that these findings are mutually exclusive of kinotypes. Indeed, while investigating a range of cell types, we also observed tissue-specific signaling profiles (unpublished data). This tissue-specific signaling, which in many ways seems intuitive given the clear functional differences across cell types, does not, however, preclude the presence of additional differences in signaling that occur in an individual-specific manner; that is, that differences in signaling across different tissue reflect a cellular specialization and that further unique distinctions are possible within these patterns in an individual-specific manner. Given the potential applications that could emerge from kinotypes with respect to biomarkers that anticipate various aspects of disease (predisposition, prognosis, and treatment options), it is critical to establish whether kinotypes represent a biological reality or technical artifact.

Standardization

In a recent review, Peppelenbosch et al. [91] proposed a co-ordinated effort between the central kinome laboratories to establish defined standards for kinome analysis. We are in whole-hearted agreement with this suggestion. As the technology evolves and is increasingly adopted by other laboratories, it is essential to establish methodical protocols that ensure that data are generated, processed, and stored in a transparent, efficient, logical, and standardized manner. This will serve to ensure the accuracy and reproducibility of individual studies, as well as to facilitate comparative analyses across studies. To address similar challenges, researchers employing transcriptional analysis drafted and implemented a set of standards for the reporting of transcriptional data collectively called Minimum Information about a Microarray Experiment (MIAME) [93]. MIAME has emerged as a critical resource in ensuring that microarray data can be correctly interpreted and independently verified. MIAME compliance is now a prerequisite for reporting gene expression experiments in nearly all peer-reviewed scientific journals. We believe that a similar set of standards, reflecting the unique challenges associated with kinomic investigations, is the most critical requirement for the ongoing advancement of the field.

Abbreviations

     
  • MIAME

    Minimum Information about a Microarray Experiment

  •  
  • VSN

    variance stabilizing normalization.

Competing Interests

The Authors declare that there are no competing interests associated with the manuscript.

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