The human genome project spurred a public frenzy and revealed to the public the potential implication of deciphering the code of life [62], and it impacts diagnostics in three aspects. First, having genome sequences can help define the complete list of all human genes and proteins, which can yield new drug targets for novel treatments. Second, the process uncovers common variants of the genome, which aids determination of hereditary factors in diseases. Third, genetic maps can guide experiments of structural genomics, thus enabling large-scale experiments of protein crystallization to determine protein structures [63]. More recently, next generation sequencing and single molecule sequencing technologies have promised to bring low cost genomic data and make genome-profiled medicine a reality. Whole genome sequencing can be applied in molecular diagnostics to analyze genomic disorders such as Mendelian disorders or common diseases in which the risk for disease development is modulated by multiple genes [64]. Genomic study techniques, such as genome-wide association studies [65], high-density genotyping, and exome sequencing [64], are only made possible by cost effective genome sequencing technologies [59,66,67,68].

One of the main focuses in genomic research is genotyping; in particular, single nucleotide polymorphism (SNP) which accounts for 80% of the variation between two individuals [69]. SNPs can cause structural changes in proteins, alter drug metabolism, and change gene regulations. The driving force behind the SNP studies is the identification of SNPs as disease markers, which is implemented by comparing the SNPs between the diseased phenotype and controls. It is estimated that around 1 million to 0.5 million SNPs would be required for a whole genome mapping of each individual patient, and 1.5 million SNPs are required per day for a reasonable pathological study (the variation arises from genetic and environmental heterogeneity of the population) [70]. In order to collect such a huge set of data, a platform needs to be robust, high throughput, and low cost. Microfluidic technologies developed for the large-scale analysis of SNPs suit this purpose very well. Recent developments in the detection of SNPs improved the existing microarray technology and use probes immobilized on microbeads to allow faster diffusion of the probes and raise the surface-to-volume ratio to increase the sensitivity and speed up SNP assays [71,72]. The advent of second generation sequencing marks down major cost of whole genome sequencing, and it expands the number of applications for the microarray technology. Sequencing coupled with microarray genotyping and bioinformatics studies have already allowed large-scale study of the SNPs [73,74]. Further cost cuts in single molecule sequencing technology will eventually pave the way for personalized diagnostics and therapeutics.

On top of genetics, epigenetic factors are equally important to consider. Epigenetic signals regulate gene expressions and determine cellular phenotypes; some epigenetic signals are shown to be important in the prognosis of diseases [75,76]. In the post-genomic era, epigenetics will become the fastest growing field of study in molecular biology. For example, DNA methylations of the tumor suppressor genes were shown to be important early diagnostic markers to predict carcinogenesis. Recently, Wang et al. reported performing early diagnosis for ovarian cancer as a proof-of-concept that microfluidics can facilitate epigenetic studies [77]. Aline et al. use micro-wells patterned by soft-lithography and meniscus force to trap and elongate single stranded DNAs, allowing high throughput epigenetic mapping [78].

Transcriptomics concerns the catalogue of all human RNA transcripts and the gene expression profiles under specific biological conditions, and it is related to the diagnostics of many diseases, including lung cancer [79], leukemia [80], breast cancer [81], acute ischemic stroke [82], to name a few. The studies of gene expression levels were traditionally accomplished with microarrays, but the toolbox for transcriptomics is rapidly expanding especially with the recent introduction of the next generation sequencing technologies. RNA-seq uses the deep sequencing technology and can directly quantify the expression levels of cDNAs of the transcripts as well as obtaining the sequences of the transcripts [83,84]. Due to the additional information of sequences, RNA-seq does not rely on preexisting genomic data and can study the subject without the complete genome. Also, with the sequences of the transcripts at hand, RNA-seq can also provide information for the connectivity between exons. As for the accuracy for the methods, compared to that of microarrays, RNA-seq has no upper bound for the dynamic range of transcripts and is less prone to background noise. Despite the clear advantage of RNA-seq over the traditional microarray technology, microarrays will more likely dominate the clinical setting in the short term for their relative maturity and lower cost.

Gene Sequencing: The rapid advancement of gene sequencing technology is the key enabler in changing the way diseases are classified and diagnosed [33,74,85,86,87,88]. The original Sanger sequencing uses chain termination chemistry to introduce random stops in the transcription events. Then, the collection of different length DNA oligos (which are labeled with the chain terminating dideoxyNTPs) are subsequently analyzed by capillary electrophoresis (CE). There are substantial efforts being made in the research community to miniaturize electrophoretic sequencing, enabling faster processing time and reduced reagent consumptions. Multiple processes for the sample preparations can be improved, including real-time PCR amplification, template purification, reaction of the extension of DNAs, and enrichment of DNA oligos. Despite the effort to adapt electrophoresis sequencing to microfluidic technology, the throughput still does not match that of the cyclic array sequencing. Even though Sanger sequencing has lower throughput compared to next generation sequencing it still remains as a good option for de novo sequencing of complex new genomes and low scale applications because of its long read lengths and flexibility in scale [89]. In the human genome project, CE is performed in a 96 or 384 channel format. With 3 billion dollars in public funding and an international consortium of labs, it still took 15 years to complete the project [90,91].

Second generation sequencing uses shotgun sequencing with the cyclic array method [66,92]. The cDNA is first randomly fragmented to create a library of single stranded DNA oligos. These fragments are then ligated with some common adaptors and immobilized to a planner substrate to form a 2 dimensional pattern of arrays. These single stranded DNAs are then amplified on-chip to form PCR colonies such that each colony on the substrate only represents one oligo in the library. Fluorescent labeled nucleotides are then extended one by one onto the single stranded colony by solid state chemistry, and images taken at each step record the bases incorporated into each colony in one cycle. The process iterates until all the oligos are sequenced. Finally, the acquired shotgun sequence is reassembled in-silico to form the complete sequences of the sample. The true power of the 2nd generation technologies lay in the great density of colonies one can form on a planer substrate. The major drawback for the second generation sequencing is the short read length of oligos and time-consuming washing steps.

Currently, the major focus for the third generation sequencing (TGS) is to shrink the sensors down to molecular scale [59,68,93,94], and thus is radically different from the second generation sequencing platform. The TGS techniques read signals generated from direct physical interactions of single molecule DNAs with sensors, as shown in Figure 2. Two kinds of TGS are the most promising in the field: one was enabled by nanopores [95,96,97,98] and the other by single molecule sequencing by synthesis [93,99]. Sequencing with nanopores takes advantage of the electric signals generated when DNAs are passed through hollowed nanostructures, which can be silicon based or protein based. The shape and size of the hollowed nanostructure is important for the signals and noises generated from electrical interactions between the structures and DNAs. For example, one of the most successful TGS is the Oxford Nanopore technology. In this technology, a hollowed nanostructure is fabricated from a genetically engineered α-hemolysin with the pore size modified by attachment of a synthetic cyclodextrin. Such nanopores are embedded in a lipid bilayer membrane, and an external electric field is applied across the membrane to induce DNAs to translocate through the nanopores. Electrical signals are generated in real time during translocations, allowing the sequences to be rapidly determined. The successful implementations of the TGS technologies relied on advanced knowledge of nanoscale phenomena. Due to the distinct physical principles involved, the applications of TGS technologies can go beyond sequencing and extend to the identification of epigenetic modifications [100], high throughput transcriptomics [101], and genome-wide translation studies [102].

Microarray: Microarrays have been a major platform for the parallel analysis of a mixture of DNA or RNA nucleic acid samples, with a variety of new microarray and chip devices and up-stream sample processing systems being developed and commercialized every year. The common applications of these devices are the analyses of gene expression level, or detection of single nucleotide polymorphisms (SNPs). In addition to the genomic and molecular biologically related applications, microarray systems are extensively used for pharmacogenomic research, disease diagnostics, and forensic identification purposes [103]. In General, a DNA microarray consists of single stranded DNA probe molecules immobilized on a solid substrate and patterned as an array of microspots. Sample solutions, containing marker modified ssDNA/RNA, was transported to microarray by either a bulk or microfluidic liquid handling method; the ssDNA/RNA was allowed to hybridized to the probe and the unmatched strands are washed away. The time limiting factor in the hybridization process is usually diffusion, which dictates the speed at which the analyte migrates toward the immobilized probes. The diffusion time scale is long compared to the kinetic time scales associated with molecular binding events. The low diffusivity of DNA (D ~10⁸ to 10⁹ (cm²·s⁻¹)) arises from its molecular length and conformation, making a typical hybridization step hours long. The excessive time required for hybridization presents a roadblock for high-throughput screening and sample-in-answer-out applications, and is an active research problem which the microfluidic technology can potentially solve [30].

Recent advancements in microfluidic hybridization techniques can significantly reduce the incubation time. There are two means to shorten the hybridization event: one is enhanced convection through active agitation and the other is to increase the surface-to-volume ratio. In the convective method, sample solutions are confined in microfabricated channels and flow through the area of probes. The confined nanoliter structures and high surface-to-volume ratio in microchannels greatly enhanced the sensitivity compare to that of the bulk solution method. To generate convective flows, different pumping techniques have been developed, and these include electrokinetic flow, vacuum or syringe pumping, and centrifugal force. Except for the electrokinetic flow, which can introduce complications in manipulating ionic mixtures, the others are contact flows which are more flexible because they do not consider the physicochemical properties of the solute [104]. In the second method, the surface-to-volume ratio is increased by using microchannels or microbeads. The microbead technique shows great promise [10,105,106]. Various microbeads can be used as low-cost solid supports, including latex, silica, and gold colloidal nanoparticles or quantum dots; they are in some cases functionalized with oligomers and fluorophores to be tracked and addressed [107]. By comparing a pixel area of 1 μm² for traditional DNA microarrays to a 100 μL sample of 1% microbeads, the beads offer a capturing area larger by eight-orders of magnitude [108]. With the incorporation of microfluidic technology, microarrays continue to improve in sensitivity and speed and are becoming a more economical research tool.

Proteomics studies the totality of proteins that are present in a given organism. Because proteins are the key governors of cellular function, monitoring and comparing the state of the proteome in normal and diseased cells will be important in unlocking the underlying mechanisms of disease phenotypes and in finding the best biomarkers for diagnosis and targets for therapy. However, proteomics studies are even more difficult than genomics due to the lack of a PCR analogue to amplify the already scarce source of proteins. The human proteome is the totality of proteins that are present in the human body, including proteins in different types of cells. All these proteins are encoded in the human genomic DNA but undergo post-translational modifications (PTMs). There are about 30,000 genes in the human DNA, but there are estimated to be around 300,000 types of proteins. In addition to the complexity of the proteome compositions, the expressed levels of proteins vary over several orders of magnitude, which present a significant challenge when the proteins of interest are scarce compared to the background. For example, albumin and immunoglobulins account for 55% of all proteins present in human plasma, but the protein markers of interest are a million to a billion times lower in concentration [109].

Despite the challenges faced by proteomics study, there are still strong reasons why we need to study proteins directly. Firstly, mRNA microarrays are found to correlate poorly with the protein expression levels [110]. Second, protein-protein interactions like PTMs can only be studied by direct protein experiments. Although the information contained in genomics is supposed to encode the activity of proteins, a method does not currently exist to decode this information from genomics directly.

The analytical methods of proteomics have not moved as quickly as genomics research due to the following reasons: first, unlike DNA, which does not degrade in response to a large range of environmental perturbations, proteins are less tolerant to change in environmental factors such as temperature, PH level, and salt content. The denaturing of proteins restricts the use of solvents in assays and poses technical challenges in every stage of protein analysis. Second, there is no PCR analogue that exists in protein analysis, meaning that the sensing method must be sensitive to low concentrations of protein.

Microfluidics and Mass Spectrometry Integration: One of the major uses of microfluidics in proteomics is to tether with mass spectrometry (MS) to do sample processing for the analysis. The limited sample starting amounts, the multistep analyses, and the operating cost of the expensive MS equipment necessitate a reliable microfluidics system to perform the chosen separation strategy. MS measures the mass-to-charge ratio by separating analytes with a Lorentz force. Due to the high sensitivity and robustness of MS, it is still a method of choice even though the instrumentation remains macro scale. The way microfluidics is integrated with MS is strongly dependent on the ionization employed. A popular on-line method, electrospray ionization (ESI), is tethered directly to the microfluidics exit while the analyte is ionized in real time. Yun et al. demonstrated the use of plastic microfluidics integrated with ESI that improved the detection sensitivity and reduced dead volume in the original format [111]. Matrix assisted laser desorption/ionization (MALDI), a major off-line method, uses microfluidics to pre-deposit sample matrices with analytes spots, and can be off-line processed by laser ionization. As highlighted in Figure 3, Digital microfluidic (DMF) systems, is capable of automating the sample processing in MALDI, and reducing contaminations of analytes [112,113,114]. For a detailed review, please refer to [30,115,116].

Protein Microarray: Protein arrays can impact our understanding of protein structure and function in the same way that DNA arrays provided insight into gene expression and regulation. There are commonly two types of array modality: functional and analytical protein microarrays, as illustrated in Figure 4 [118]. Functional protein microarrays are usually applied to analyze a genomic set of proteins. In this type of array, individual proteins are separately spotted on a surface such as a glass slide and then analyzed for activity. This approach has the potential for rapid high-throughput analysis of large collections of proteins and cellular proteomes, and promises to transform the pharmaceutical industry. A second type of protein microarray is the analytical microarray. Here, a genomic set of protein-specific ligands such as nucleic acid aptamers, antibodies, or chemical probes are spotted on a microarray, and then the proteins in an extract can be quantified in parallel by binding extracted proteins to the microarray. The scientific community is starting to realize the potential of analytical protein microarrays to monitor protein expression on a proteome-wide scale and in medical diagnostics. For example, protein microarray was recently applied in the diagnosis of complex disease such as acute myocardial infraction [80], autoimmune disease [81], and cancer [82]. A critical step in generating a practical protein array has been the development of general methods for arraying a large set of proteins without denaturing them and at enough density for detection of interactions.

Recent technological developments have addressed the difficulties associated with purifying a large set of proteins for array construction. The invention of DNA to protein arrays (DTPAs) using in vitro transcription/translation (ITT) circumvents many of the difficulties associated with cloning, expressing, purifying, and spotting of proteins, and is depicted in Figure 5. Currently, the in situ or on-chip protein array methods use cell free expression systems to produce proteins directly onto an immobilizing surface from co-distributed or pre-arrayed DNA or RNA, thus enabling protein arrays to be created on demand [119]. Integration of DTPAs and microfluidics has been demonstrated for the synthesis and characterization of S. pneumoniae proteins [120] and synthetic transcription factor mutants [121]. Besides advancement in the chip patterning, several detection methods were also developed to detect interaction of proteins with proteins and nucleic acids; these methods include FRET, FCS, MITOMI, SPR, and Nanowire. The integration of novel and existing methods for measuring molecular interactions allows proteins to be characterized quantitatively and with better sensitivity. The integration of DTPAs and novel detection mechanisms into a single microfluidic device platform is a significant step toward automating these methods, and increasing throughput in protein biochemistry. For more in depth reviews of protein microarrays, the readers are encouraged to refer to the following [118,122,123].

Metabolomics is the study of the totality of metabolite expressions as a snap shot or as a dynamic course in time. At the single cell level, metabolites play a critical role in intercellular communications, energy balance, osmoregulation, membrane dynamics and other cellular activities. Metabolomics can provide useful insight into the state of diseases [124]. Because metabolites can be extracted easily from cells, blood or serum, they can help to develop noninvasive diagnostics technologies. Biomarkers derived from metabolomics studies have been proven sensitive and efficient in helping stratify patient population and evaluate prognosis. For example, Sreekumar et al. identified a set of metabolites that can be used as biomarkers to distinguish the function of a normal prostate from that of a prostate cancer patient [125]. They found that sarcosine levels correlate very well with cancer progression and can be used as biomarkers for noninvasive diagnosis [125]. Metabolites are usually analyzed noninvasively by nuclear magnetic resonance spectroscopy and in vitro by traditional chemical analytical technologies, such as liquid chromatography (LC) or gas chromatography (GC), coupled with mass spectrometry; the typical workflow of metabolomics is covered in Figure 6. For a detailed review of metabolomic processes, please refer to [126]. Microfluidics were introduced only recently into the study of metabolomics. However, just as in proteomics, microfluidic systems have not displayed their full potential in metabolomic studies and have mainly served only as platforms for sample preparations [127].

Traditional bioanalytical techniques usually take up a group of cells to enrich the analyte content to a detectable range and record the ensemble average of measurements. Recently, with the advancement in the sensitivity and ability to multiplex in assays, single cell analysis is beginning to revolutionize traditional batch format experiments by recovering the hidden information arising from the heterogeneity of populations of cells. Cell samples taken from tissue samples contain many different phenotypes of cells. Furthermore, cells taken from culture of same type of cells also contain different expression profiles, representing multiple levels of heterogeneity [129]. The heterogeneity of cells is important because the value of interest often does not follow a simple statistical distribution, so an ensemble average is not a representation of any individual in a population, as exemplified by the mRNA expression of GAPDH of Jurkat cells after siRNA knockdowns [130]. Also, the environmental changes are likely to induce redistribution of gene regulatory network states in the cell population, in which the dynamics can only be traced with single cell analysis [18]. Recently, with the advancement of sequencing capability, intra tumor heterogeneity, which dictates tumor evolution and adaptation and disrupts therapeutic strategy planned with single tumor biopsy sample, has just begun to surface [131].

Rational design of drugs follows the bottom-up approaches and identifies disease mechanisms by searching through aberrant molecular machineries. With experimental high throughput screening and numerical modeling, a molecule complimentary in structure and electrical charge to the disease causing cellular molecule is designed and is used to treat the disease. Efficient docking of drugs to targets or ligands requires accurate knowledge of the 3-D molecular structures and charge distributions. Large-scale computer simulations and fast progresses in parallel processing fluidic instrumentation have facilitated the tasks of searching for the desired drug molecules from a large pool of possibilities.

Targeted therapy and rational drug design oversimplified the interconnected nature of diseases, and is in accord with the reductionist paradigm which drove medical practice in the modern era [16]. The fact that most disease phenotypes cannot be reversed by a single node or single edge treatment in the network best exemplifies the difficulty faced by the reductionist paradigm. Under the reductionist framework, biological information was trimmed into a single mechanistic pathway, and the associated treatment was reduced to usually a single component in the pathway. With ingenious ideas and diligent work, targeted drugs have made significant impacts in treating cancers and many other diseases during the past several decades. A rationally designed drug is successful in coupling with monogenic diseases, but is not sufficient for certain diseases that are more complex in nature. In addition, robust biological systems can often develop resistance by bypassing the targeted pathway, which reduces the efficacy of treatments.

Network medicine is an emerging field which incorporates concepts in systems biology and takes into account the structure and dynamics of a complex network in the interpretation of disease. Cancer, aging, Alzheimer’s disease are all complex diseases which cannot be reduced to a single molecular cause, and can be understood more easily under the context of biological networks. Instead of pointing the molecular cause of a disease to a single pathway, a disease is interpreted by the concepts of network medicine as network perturbations in a dynamic system, so diseased states can be defined more precisely [16,18]. Useful clinical and biological applications can be derived from network approaches. The increased understanding of the interconnectedness of cellular network will lead to understanding of the causes of disease pathogenesis, identification of more precise sets of biomarkers for diagnosis, and eventually therapeutics.

Recently, combining the concept in network biology and polypharmacology, multi-target therapy is gaining traction as the main strategy in drug discovery [133], and has been demonstrated successfully in systems such as HIV infection [134], and cancer therapy. Combination of multiple drugs has many advantages over single drug therapy: (1) drug combinations enhance the potency of the therapy by taking advantage of the drug synergistic or additive effects; (2) the synergies among many drugs may reduce the dosage levels, which usually imply low toxicities and side effects; (3) it can avoid emergence of drug resistance by combining drugs with minimal cross-resistance [135]. However, compared to disease identification, the application of network medicine concept directly to multi-target therapeutics still suffers the inherent difficulty of the bottom-up approaches.

The network medicine approach [17] of choosing drug targets is based on the knowledge derived from genomics, proteomics, pathways and pathways’ interactions through the network connections. This type of bottom-up approaches can provide fundamental understanding of the disease-causing and drug-interaction mechanisms, only if the complete molecular activities and pathway information are available. Given that the datasets from genomics to proteomic and from pathways to phenotype are currently far from complete, the application of bottom-up approaches in the clinics would still be too difficult and expensive. We surmise that the wide spread applications of bottom-up approaches can only be achieved once reliable databases, robust high-throughput platforms, and petabyte processing capabilities are met.

The ultimate goal of therapy is to cure the disease. In order to control and eventually reverse the pathophenotypes, the Feedback System Control (FSC) technique was developed to search for the optimal dosage for combinatorial drug therapy. The phenotype was taken as parameters to construct an objective function, in which the goal is measured. The objective function sits in an engineering feedback loop, which is used to direct engineering systems to tune the external stimuli. In the top-down approach, the bio system is driven to the desired state without measurement of the detailed molecular activities inside the cell, thus, avoiding the overloading of information common in bottom-up approach.

FSC consists of four modules as shown in Figure 7. Let us take the Herpes Virus Simplex 1 (HSV-1) infection experiment as an example [24]. The first module is the input stimuli, e.g., the drug-dosage combinations. The second module is the bio-complex system of interest, e.g., virus and host cell. The third module is the objective function, e.g., the percentage of infected host cells, and/or toxicity/side effects. The fourth module is the search algorithm, which provides the next set of stimuli and dosages for directing the biological complex system toward the desired state.

If the dynamics of the plant, e.g., the cellular activities, are known in full detail, analytical modeling of the entire feedback loop can be performed. The FSC approach aims to circumvent the need for detailed information of the system of interest, so that a priori knowledge of the bio-system is not required, i.e., treating the cell as a black box. In the first test, the inputs are arbitrarily chosen drug-dosage combinations. The cells will respond to the input stimulants and exhibit system outputs. The system responses are usually non-satisfactory because the inputs are not optimized. Based on the first drug-dosage combination and the outputs, the search algorithm will come up with new and perhaps better combinatorial stimulants and feedback to the bio system. The feedback loops will be continued until the desired phenotype occurs.

An optimization of a drug-dosage combination that involves M stimulants at N levels generates a pool of N^M total testing cases. A screening of the entire pool, N^M cases, is prohibitive. In other words, if the feedback loop approach cannot find the optimal drug-dosage in a small number of iterations, then the FSC technique (even bypassing the need of detailed dynamics of the plant) still will not be a feasible method for meeting our requirements.

In the experiment of inhibiting HSV-1 infection, we used FSC technique to search for the optimal drug combination from a pool of 1,000,000 combinations [24]. Only about 12 iteration loops were needed. The search started with 6 drugs. Only three drugs with much lower dosages compared with single drug therapy were needed to achieve close to 100% inhibition.

FSC is a black box or modeless approach. Only a small number of iterations in the feedback loop are needed to find the most potent drug combination. Counts of 10–15 iterations to hone-in on the optimal drug cocktail have also been achieved in eradication of cancers, maintaining human embryonic stem cells and in inhibiting viral infections [22,25]. The key finding in the FSC-based studies is that the system’s response surface to the drug stimuli is very smooth. Therefore, not many iterations are needed to reach the best performance in the high dimensional (multiple drugs) space. Due to the smoothness of the response surface, the choice of search algorithm is not very sensitive in affecting the number of iterations needed to reach optimal combination in all our tested models. In addition, the low dosage of each drug in the cocktail is a result of the synergetic effects among drugs.

Disease is a system problem and needs to be studied at the system level. Cells are the first level of this system. For instance, if we can identify and collect cancer cells from blood, then we may be able to detect and to treat the cancer at an early stage. Microfluidic sample preparation processes offer an emerging technology for sorting rare cells from bodily fluids. Flow cytometry and fluorescence-activated cell sorting (FACS) are powerful instruments in sorting minute amount of cells with specific membrane markers from large number of red and white blood cells [136]. The commercial FACS can sort at the speed of 10,000 switching events/sec. However, the switching rate of microfluidics based FACS was a few orders of magnitude slower until a high speed pulse laser triggered micro bubble switch was invented (Figure 8) [137]. The pulse laser induces a bubble near the elastic fluidic channel. The rapid deformation of the channel wall can change the direction of the motion of a cell marked by fluorophore conjugated with a specific cancer membrane antibody. The switching rate matches or is even higher than that of large commercial products. In addition, cells are maintained in a friendly buffer environment so that high cell viability can be obtained. Furthermore, only a very small amount of fluid volume is needed for processing in a microfluidic system. This is particularly important in clinical applications. In most cases, the biopsy sample from patients is so small that only microfluidic devices can offer efficient sample preparation.

It is well known that a cellular response to a stimulus involves several actions, particularly protein modification through post-translational processes. These include protein cleavage, protein splicing, acetylation, coupling to small peptides such as glutathione, and phosphorylation, this last being an important regulator of signal transduction pathways [138]. Phosphorylation is a transient and reversible metabolic process consisting on the addition of a phosphate group to a protein to either activate or deactivate its signal capability. Therefore, by measuring the phosphorylation state of proteins, it is possible to determine which signaling cascades are used in response to specific stimuli, the kinetics associated to this signaling activity, and the downstream targets that are transcribed. Furthermore, the comparison of phosphorylation activity of diseased cells to healthy samples facilitates the identification of aberrant signaling events, a useful trait to characterize diseases [139].

Over the last decade, a trend in flow cytometry measurements emerged to investigate the phosphorylation state of intracellular proteins, commonly called Phosphoflow and presented in Figure 9 [139]. The Phosphoflow technique allows the monitoring of effects of targeted kinase inhibitors within specific cellular populations, the detection of alteration of pathways through the use of signaling potentiators, and the monitoring of the pharmacodynamics resulting from the combination of the potentiation itself [140].

To measure these phosphorylation events, antibodies that are specific to the phosphorylated form of the protein of interest must be raised; these phosphor specific antibodies are coupled to fluorophores that can be detected and analyzed by flow cytometry, where an increase in fluorescence reading is correlated with an increase in phosphorylation [141]. Depending on the flow cytometer used, more than 13 parameters can be simultaneously analyzed in a single cell. Moreover, the multiparameter nature of flow cytometry, coupled with the possibility of both intracellular and extracellular readings, allows for subset-specific analysis to be performed in heterogeneous populations, such as whole blood samples [139]. These readings can also be performed in multi-well plates in parallel, making it a suitable option for high throughput experiments [142].

As a prime example of phosphoflow being used to understand different drug mechanisms, Shachaf and coworkers found that atorvastatin, a 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor, can prevent and reverse MYC-induced lymphomagenesis by inactivation of Ras-induced ERK1/2 phosphorylation, but it could not do so in the presence of activated K-RasG12D [143]. These findings suggest that heterogeneous cancer cells could have different responses to a single drug. Hence a better treatment would require multiple drugs.

Phosphoflow has also been used to identify potential drug combinations, as is suggested by the work carried out by Galligan and coworkers [144], who analyzed peripheral blood mononuclear cells (PBMCs) from individuals diagnosed with early stage and late stage Rheumatoid Arthritis, as well as from healthy individuals and patients diagnosed with osteoarthritis. Fifteen different phosphor-epitopes were analyzed and compared between the different populations; the results gave new insights into the signaling pathways for arthritic lymphocytes: they suggest that a combined therapy of a p38 inhibitor with a STAT3 inhibitor could potentially target these cells with doses much lower than those needed for a single inhibitor therapy.