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Historical background: from cell theory to cellular heterogeneitySince J Schleiden and T Schwann formulated the ‘cell theory’ [1], much effort has been made to develop measurement technology that allows us to investigate biology at a single-cell level [2], but only with the advent of proteomics and genomics there was an improvement in analytical meth-odology and data acquisition [3]. Traditional technologies are limited by the detection of parameters resulting from the averages of large populations of cells, missing cells produced in small number and attempting to uniform the heterogeneity [4].

Biomolecular approaches allowed our initial comprehension of cellular functions, through assays often based on lysis or disaggregation of cell mixtures, in order to enable purification of their components; these analyses have encour-aged us to uncover and characterize cell popula-tion heterogeneity [5].

The renewed interest in cellular hetero-geneity and related complex signaling path-ways in physiopathological conditions, together with recent advancements in measurement

technology, have given rise to the single-cell analysis-omic.

It has been well demonstrated that cellular heterogeneity is closely related with numerous stochastic transcriptional events leading to var-iations in patterns of expression among single genetically identical cells (Figure 1) [6–10].

The stochastic nature of gene expression and cellular activity, in general, can be attributed to a combination of extrinsic and intrinsic factors; the first related to mechanisms regulating gene expression (regulatory molecules), the second depending on the intrinsic randomness of cel-lular processes (transcription and translation of genes encoding proteins essential to cell func-tions [11–13]. Even if extrinsic factors involve the entire cell, while the intrinsic ones are referred to specific genes or processes, these two phenomena are deeply inter-related (Figure 1).

Cellular heterogeneity is indispensable in order to respond to the continuous changes in the cell microenvironment (pH, temperature, nutrient availability and so on) providing a fitness advantage to the cell populations. For example, in multicellular organisms, different

Ferdinando Mannello*1, Daniela Ligi1 and Mauro Magnani21Department of Biomolecular Sciences, Section of Clinical Biochemistry, Unit of Cell Biology, University “Carlo Bo”, Via O. Ubaldini 7, 61029 Urbino (PU), Italy2Department of Biomolecular Sciences, Section of Biotechnology, University “Carlo Bo”, Via Saffi 2, 61029, Urbino, Italy*Author for correspondence: Tel.: +39 722 351 479 Fax: +39 722 322 370 ferdinando.mannello@uniurb.it

Traditional technologies to investigate system biology are limited by the detection of parameters resulting from the averages of large populations of cells, missing cells produced in small numbers, and attempting to uniform the heterogeneity. The advent of proteomics and genomics at single-cell level set the basis for an outstanding improvement in analytical technology and data acquisition. It has been well demonstrated that cellular heterogeneity is closely related with numerous stochastic transcriptional events leading to variations in patterns of expression among single genetically identical cells. The new generation technology of single-cell analysis is able to better characterize a cells’ population, identifying and differentiating outlier cells, in order to provide both a single-cell experiment and a corresponding bulk measurement, through the identification, quantification and characterization of all system biology aspects (genomics, transcriptomics, proteomics, metabolomics, degradomics and fluxomics). The movement of omics into single-cell analysis represents a significant and outstanding shift.

Deciphering the single-cell omic: innovative application for translational medicineExpert Rev. Proteomics 9(6), 00–00 (2012)

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Keywords: biomarkers • cell heterogeneity • laser capture microdissection • microfluidics • molecular diagnostics • omics • single-cell analysis

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cells that express biomolecular variants may better survive under different environmental and biological conditions (e.g. the mecha-nisms and affects involved in apoptosis, immune response and adaption to a variety of ‘stress’ insults). By adjusting their bio-chemical networks and regulatory enzymatic activities, single cells can easily take advantage of the inherent stochastic variability in gene and protein expression to improve/increase their survival

at the expense of the rest of the clonal cell population, ensuring their survival and proliferation [14].

This general mechanism [4,5,15] is well observed (for instance, but not only) in Bacteria (and also in some Protozoans) during phenotypic switching related to antibiotic-resistance and/or linked to unfa-vorable microenvironmental conditions. Furthermore, in multicellular organisms (like in Metazoans), cellular hetero geneity has also been reported in embryonic devel-opment, immune response and cancer progression. In the latter, it has been sug-gested that genetic mutations may not be strictly required to drive tumor progression and reoccurrence, but is indispensable in the existence of a small fraction of cancer cells with stem-like properties, represent-ing, with their self-renewal capacity, the source of tumor cell heterogeneity [16,17]. Heterogeneity is a documented natural occurrence in cancer cell populations, and represents one of the major obstacles to the successful treatment of cancer [18]. In the same way, cell heterogeneity sets the basis for the almost dichotomy between responder and nonresponder patients affected by autoimmune diseases [19,20].

Therefore, for a better understanding of the specificity and complexity of the cel-lular tissue microenvironments in physi-opathological conditions, it is necessary to evaluate the molecular signatures with single-cell resolution.

System biology: from conventional technologies to single-cell analysesA wide spectrum of methods and microtechnologies have been and are being developed to study systems at a single-cell level with the aim of solving several clinical problems.

To investigate biological systems, the ‘old’ conventional methodologies are entrapped by the traditional technolo-gies with well known limitations of reli-able and quantitative evaluation data. The

single-cell analysis (SCA) are among the most challenging and informative in all systems biology providing the outstanding way to detect, isolate and analyze individual cells.

SCA measurements are informative because every analyte pre-sent in, or extracted from, a single cell provides a complete profile of data with the highest possible sensitivity, that may be combined with data obtained from other individual cells; on the contrary,

Figure 1. Single-cell analysis allows us to unearth cellular heterogeneity and improve omics translational approaches. Heterogeneity between individual cells, a common feature of dynamic cellular processes, including signaling, transcription and cell fate, is closely related with numerous stochastic transcriptional events leading to variations in patterns of expression among single genetically identical cells. Cells take advantage from their inherent stochastic variability in gene expression and their protein/enzymatic tools to respond to the continuous microenvironmental and intracellular variation, in order to survive at the expense of the rest of the clonal cell population. Cellular signals originate by a genetically clonal, but phenotypically heterogeneous, population promote different and individual responses that cannot be revealed by conventional techniques, such as traditional flow cytometry, investigating biological samples. Conventional tools can provide only averaged measurement on a heterogeneous cell population, and single cell responses/features are hidden within an unspecific cellular background of cells, with have different behavior. On the contrary, SCA technologies are able to recover, monitor and characterize population heterogeneity with single-cell resolution and, finally, to provide data for a molecular signature. Other than detect and characterize a single-cell, SCA devices have to simultaneously analyze a large number of individual cells in order to determine the distribution of responses really due to cell heterogeneity, avoiding incorrect conclusions from rare cells or stochastic biological noise. Omics sciences, identifying, quantifying and characterizing all cellular components (genes, transcripts, proteins, metabolites) itself and in their interaction with spatiotemporal resolution, represent a powerful tool to investigate systems biology. Thanks to the advancement of devices for single-cell detection and separation, and the miniaturization of technologies for genomic, transcriptomic, proteomic, metabolomic, degradomic, fluxomic assay, omic sciences reached applicability in individual cells and in their subcellular compartments. Single-cell omics, representing the successful combination of technology and biology, is beginning to find application in stem-cell research, cancer research, developmental biology and drug development, where the comprehension of intracellular and extracellular networks is the mytic and ever sought ‘Holy Grail’.

Conventionalflow cytometry

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traditional methods furnished only the average values in which large numbers of cells are analyzed in each assay, with lower sensitivity and reliability. Although one could certainly argue that sensitivity and reliability of data for large populations of cells is much higher than that possible for a single cell (given technical issues such as signal to noise), the weakness of traditional methods and the strength of new single-cell approaches is linked to understanding ‘Who makes what’: the conventional tech-niques obtain only average concentrations of molecules without discrimination among producing-nonproducing cells, whereas single-cell analyses help to discern and quantify what kind of cells are able to pro-duce and secrete a particular molecule also linked to a low-gene expression, improving sensitivity and reliability suggesting finally ‘Who rules who’ in different physiopatho-logical conditions.

An essential prerogative in studying bio-logical systems at the single-cell level is the simultaneous analysis of a large number of individual cells in order to determine the distribution of responses really due to cell heterogeneity, avoiding incorrect conclu-sions from rare cells or stochastic biologi-cal noise [21]. In fact, fluctuations in protein numbers (i.e. noise) due to inherent sto-chastic effects in single cells can have large effects on the dynamic behavior of a gene regulatory network [22]. In the same way, the production of a single-protein molecule from low-level gene expression in individual cells demonstrates the potential of single-molecule studies in elucidating the working of fundamental biological processes in single living cells [23].

Until the advent of newer and more sophisticated investigative techniques, sev-eral lower throughput methods were used to provide more information at a single-cell level, such as flow cytometry (FC), microscopic techniques and capillary elec-trophoresis (CE) (Figure 2). Although these ‘conventional’ approaches are already con-siderably ‘technologically advanced’, they are missing some important information at cellular level.

For example, traditional FC provides information only about a single time-point in each cell, corresponding at the instant of the detection, therefore making it useful for applications where we want to search for a specific known property, but limited in

monitoring the cell long-term or assessing real-time response to stimuli and lacking morphological/intracellular signaling infor-mation about the cell [24]. Another limitation of light-scattering FC is represented by the size detection limit, which is currently about 0.5 μm [25]. Moreover current technologies based on

Figure 2. Different resolution and performance of technology to investigate cellular heterogeneity: comparison between methods. Traditional techniques proposed to investigate system biology at single-cell level, including FC and automated microscopy (referred also as high throughput microscopy), despite their capability respectively to analyze many individual cells per minute accordingly to their size, intracellular granularity and fluorescence properties, and to provide data on intracellular compounds localization and dynamic behavior, and not last to offer live-cell images, are limited by several disadvantages. FC, providing information only about a single time point of each cells, is limited in monitoring cell long-term, assessing real-time response to stimuli and lack of morphological/intracellular signaling information about the cell. Similarly, high throughput microscopy is often unwieldy to monitor and track cell division and unsuitable for multiparametric assays. Capillary electrophoresis, a more throughput technique due to its high speed, flexibility and portability, is well suitable for the analysis of multiple enzyme in a single cell and can be applied to acquire chemical information from small molecules to larger proteins. Despite these significant advantages, CE is lacking in high reproducibility and sensitivity. Advanced technologies, based on miniaturized tools that in a single chip integrate sorting methods generally performed with bigger tools, are named Lab-on-Chip. Lab-on-Chip devices, exploiting well, trap, pattern and droplet-based methods to screen, identify and capture individual cells, can be considered, to date, the more innovative and sensitive tools to evaluate cellular heterogeneity and to detect specific cell types. Among some of these high throughput platforms, the one proposed by ScreenCell provide a microfluidic device, based on filtration method, using different buffers according to the specific analysis has to be done. It can be applied for molecular biology assay (ScreenCell MB), cell culture assay (ScreenCell CC) and for cytopathological studies, including cell enumeration, cytology, immunochemistry and FISH assays (ScreenCell Cyto). More recently, Fluidigm introduced the C1™ Single-Cell AutoPrep System, a microfluidic technology, enables researchers to isolate and process individual cells rapidly and automatically to evaluate genomic signatures generated from a single cell. C1 System can be applied for stem cell, cancer and immunology research, evaluating cell differentiation, measuring single cell responses to appropriate stimuli, verifying critical disease biomarkers, and sequencing individual cells. Finally, Silicon Biosystem, developed a microarray platform (DEPArray) able to sorting and trapping single cells in individual dielectrophoretic cages. DEPArray maintaining unaffected cell viability, proliferation capability and DNA integrity, is useful for several applications such as prenatal and cancer diagnosis, cell therapy and single-cell resolution biology.

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fluorescence require prior in vivo or in vitro labeling, and are generally limited to 10–20 simultaneous measurements because of the spectral overlap [26].

On the other hand, the complex single-cell sample preparation required limits throughput of analyzed cells, and thus the tradi-tional ‘destructive’ chemical FC methods cannot compete with the high-throughput analysis of noninvasive single-cell analysis in terms of evaluating the heterogeneity of a cell population [27].

Similarly, automated microscopy, a technique potentially useful for investigating cell heterogeneity, though well suited for intracel-lular time-dependent studies providing images also on intracel-lular compartmentalization, does not allow the identification of cell boundaries for rapid and efficient analysis. It is often unwieldy to monitor and track cell division, if one considers the effective field of analysis required for the observation of a single cell [28].

Furthermore, the cell separation through CE is based on the peculiar electrophoretic mobilities of charged molecules within a narrow-bore capillary in an electrical field. Although the most frequent detection methods employed for CE include absorbance, fluorescence and electrochemistry, the most widely used detection methods for single-cell separation are laser-induced fluorescence and microelectrode-based electrochemical methods [29].

CE is well suited for the analysis of multiple enzymes in a single cell and can be applied for the acquisition of chemical informa-tion from small molecules to larger proteins [30]. Although having several advantages (such as speed, flexibility, portability, sample and reagent requirements and cost), CE also lacks high reproduc-ibility and sensitivity [31].

The limits of conventional techniques have necessitated the introduction of sophisticated, miniaturized and more sensitive devices to analyze real-time changes at the single-cell level and not just reporting information on the average behavior of a large numbers of cells.

Recent developments in microfabrication and nanofabrication technologies, mainly based on microfluidic analytical devices integrated with several advanced features (including environ-mental control, measurements on fast time scales and image processing [32]), led to consider cells as a laboratory to perform complex biochemical operations, providing not only a biochemi-cal, electrical, mechanical and optical characterization of single cells [33], but also additional information on how cells interface with the outside world, including microenvironmental control, cell-to-cell relationship and so on.

The basic principle exploited by these microplatforms is to iso-late individual cells with specific characteristics by mechanical or chemical-physical tools. This sorting can be obtained through the use of microwells multiplates, which allows us to distinguish individual cells from cell aggregates, or arrays obtained by micro-patterning of surface, offering dynamic surface properties and cell patterns, with the ability of recognizing different cell types [21].

Such technology, useful to investigate protein patterns, gene expression, nucleic acid mutations and intracellular signaling, has the benefit of providing high throughput, rapid and highly sensi-tive analysis by requiring small sample volume, decreased reagent consumption and consequently reduced costs of the experiment

[5]. Among the more innovative cell methodologies with high resolution and throughput, the new generation technology of SCA is able to better characterize cell populations, identifying and differentiating outlier cells, in order to provide both a single-cell experiment and a corresponding bulk measurement [5].

Microfluidic improved technologies enable SCA to also analyze large numbers of cells, representing a powerful platform for prob-ing single cells due to both the intrinsic micrometer length and picoliters volume scales, quite similar to the size and volume of single cells, integrating into a microdevice all processing steps [34].

Starting from the first microfluidic systems (coupling PCR to capillary electrophoresis), the actual integrated microsystems provide a significant increase in detection sensitivity, handling crude samples and obtaining crucial informations of cell organi-zation using ad hoc arrays and softwares [35]. Interestingly, a capillary-based vacuum-assisted microdissection device has been developed that is able to reduce the potentially harmful treat-ment of single cells allowing almost all cell omics at the cellular resolution, demonstrating high accuracy and quality and sug-gesting it as an alternative tool for laser capture microdissection [36], one of the methods with a high throughput similar to SCA (Figure 2).

The development of throughput microfluidic systems is lead-ing to a multitude of single-cell devices and analytics allowing us to obtain crucial data about gene expression, protein analysis, signaling response and growth dynamics [37,38].

SCA clearly provides knowledge of the distribution and statistical significance of values over a population of cells detecting the cellular variabilities during different states (e.g. cell heterogeneity in both healthy or diseased conditions and so on), setting the basis and allowing us to differentiate between deterministic and stochastic events in cells [8,39].

Among the biological data characterizing cells at a single level through SCA technologies, we can mention mechanical char-acterization (e.g. cytoskeleton and extracellular structure modi-fications under mechanical stimuli), electrical characterization (e.g. to assess cell membrane integrity in order to evaluate cell death [40,41]), optical characterization (e.g. to measure the effects of cell-to-cell interactions by quantifying oxygen consumption rates of individual, noninteracting and interacting cells under normoxic and hypoxic conditions [42]), intracellular-network characterization (e.g. to evaluate variation in cellular respon-siveness to signaling modulation in the biology of pathological conditions [43] or to monitor specific molecular interactions in the nucleo-cytoplasmatic molecular traffic [44]), spatiotemporal characterization by assessing intracellular specific localization of molecules [45] and by monitoring cell time-response after stimuli, for example, different rates of apoptosis in the same clonal cell population [46,47].

Thanks to its capability to provide a complete and dynamic pic-ture of intracellular processes and regulatory circuits with spatio-temporal resolution, SCA insert more effectively in the field of systems biology through the identification, quantification and characterization of all biomolecular aspects (genomics, transcrip-tomics, proteomics, metabolomics, degradomics and fluxomics

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[26]). The movement of omics into SCA represents a significant and outstanding shift (Figure 1).

Potential biological applicationsGenomicsGenomic research represents the basis of any systematic approach for the understanding of cellular functions because of its relevance in determining cells metabolic and regulatory extent.

Single-cell genomics is predominantly applied for the explora-tion of the genomic diversity and variability in the microbiologi-cal biosphere because of its major molecular ‘simplicity’, and is crucial for several applications from microbial screening to climate simulation and medical diagnostics [2,48].

Although only a few microbial genomes have been identified and classified [49], this has led to both the development of culture sequencing technologies and to the identification of single-cell genetic variation within clonal microbial populations [50].

With this in mind, numerous techniques have been and are being developed to investigate single-cell genomes. These include Whole-genome amplification (WGA) techniques based on ran-dom-primed PCR, such as the Degenerate-Oligonucleotide-PCR [51], the Primer Extension Preamplification [52] and linker-adaptor PCR [53]. These protocols are limited by short size of amplified products, which are useless for many applications, and incomplete genomic coverage [54].

A more recently introduced WGA method is represented by the multiple displacement amplification (MDA), which has the prerogative to amplify both circular and linear DNA [55,56], con-tained in a sample without the use of PCR and without a prior sequence knowledge [57]. MDA exploits the high processivity of the Φ29 phage DNA polymerase, reportedly able to syntethize DNA longer than 70 kb by a strand-displacement mechanism [54], providing a more uniform locus representation [27]. The high sensitivity, precision and amplification rates of MDA could be combined with SCA in order to obtain a powerful and outstand-ing method to analyse nucleotide polymorphism at the single-cell level [58].

MDA strategy is easily applicable to large numbers of samples for example crude cells, blood and bacterial culture lysates, buccal swabs, whole insects, single sperms and individual blastomeres in preimplantation diagnosis [59,60].

As highlighted in previous reviews [54,60], MDA-generated DNA has been used for several applications, including microbial genomes sequencing, single nucleotide polymorphism genotyp-ing, real-time PCR, southern hybridizations, detection of cancer-associated genome rearrangements, arrays probing and compara-tive genomic hybridization.

The need to reduce problems related to contamination with for-eign DNA strands (found everywhere in laboratory reagents and surfaces) has been solved using integrated microfluidic platforms for simultaneous cell sorting, cell handling, sample preparation and DNA amplification [48,61].

However, none of the above reported technologies actuall allow the study of complex biologic processes within living sin-gle cells, linked to the cell lytic phenomena associated to cell

manipulation. The noninvasive and novel characteristics of SCA technologies have been successfully established for single-cell genomics as well. The application of SCA to the human genome has recently been investigated revealing the correct analysis of haplotype phase [62]. Noteworthy is the useful approach for numerous medical applications involving rare cells (e.g. circu-lating tumor cells and circulating fetal cells [2,57]). Evidence sug-gesting that tumor cells show a clonal heterogeneity, means that genome analysis at the single-cell level may be crucial for success-ful molecular-targeted therapy [63]. In the same way, a method has been developed enabling multiple analyses at the single-cell level from rare fetal cells (circulating in the blood of pregnant women); this approach utilizes WGA products isolated from single cells to improve single-cell-based noninvasive molecular genetic and cytogenetic prenatal diagnosis [64].

TranscriptomicsA better and more powerful strategy to connect genotype to cell phenotype is transcriptome analysis. Starting from the axiom that all cells in an individual organism have an identical genotype (isogenic cells), it is well known that different transcriptomes reflect the time-dependent expression, under certain conditions of crucial genes [27]. Peculiar transcriptomes have been identified in different cell types during cell development, differentiation and proliferation, reflecting the complicated network of gene regula-tion under different physiopathological conditions [65].

Because of the short turnover times of transcript and its insta-bility due to degradation with RNAases, all transcriptomic tech-niques need steps for RNA isolation and analysis, which usually include a reverse transcription PCR (RT-PCR) step into a single device, to reduce the necessary reaction times and to prevent RNA degradation [66].

Techniques for transcriptome analysis at single-cell resolution are based on the amplification and analysis of cDNA [67–69] or through the recent use of Smart-Seq (an mRNA-Sew protocol [70]).

Integration between next-generation techniques and improved single-molecule sequencing could facilitate direct sequencing of complete mRNA molecules from a single cell without error-prone and elaborate transcript processing [65,71,72]. Most recent approaches in whole-transcriptome analysis showed a significant variability in the gene expression profiles of individual neuron cells [73], in circulating tumor cells [74], suggesting this method (also called ‘next-gen sequencing based single-cell transcriptom-ics’) as a powerful tool for the identification of new drug targets [75].

Because expression changes are often very rapid, mRNA levels and composition can vary considerably in a single cell. For this reason, most of the transcriptomic techniques share the central aspect of cell consumption/lysis upon analysis [76–78]. To avoid this destructive approach and to resolve dynamic changes in the cellular phenotype, continuous in vivo monitoring of the target cell is indispensable. These can be done by integrating technolo-gies for single-cell transcriptomics with optical strategies, includ-ing FISH or fluorescence correlation spectroscopy. Such noninva-sive techniques were previously used to demonstrate that induced

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RNA levels within a single bacterium exhibit a pulsating profile in response to a steady input of inducer [79].

Among drawbacks of single-cell omics, there is not only the difficulty of identifying and isolating the specific cells from a mix-ture of a heterogeneous population, but also the need to develop a single-cell screening assay in order to obtain reproducible data on cells, in conjunction to the essential aspect of a reliable and nondestructive purification of cells [80]. Further drawbacks of the single-cell genomics and transcriptomics are linked to their costs and the limitations to analyze small number of cells, and to their uncontrolled amplification bias.

Single-cell transcriptome analysis can be used for determining gene regulatory pathway at whole-genome scale and can be com-bined with overexpression, knockout or knockdown of a gene of interest to reveal how it regulates gene expression in a target cell. This can be applied to the study of cancer and noncancer stem cell and cancer cell heterogeneity [74,81].

Actually, the major goal of SCA is the simultaneous analy-sis of genome sequences and corresponding transcriptome and proteome profiles in individual cells, in order to correlate both genotype and phenotype to the ability of single cell to modify its biomolecular machinery to the microenvironmental and mac-roenvironmental conditions (e.g. the adaptive responses of normal and cancerous stem cells to intracellular and extracellular stimuli, generating cell homing, growth and different proliferation rates). Moreover, the most powerful analytical techniques of SCA may easily integrate the genomics with transcriptomics, suggesting an outstanding biologic potential to understand the mechanisms and cellular origin of physiopathological processes.

SCA may allow in a large number of individual cells (without stochastic variations and biological noises) identification of the single gene, to amplify target transcripts and finally to provide unambiguous peculiar biomarkers, leading to a better characteri-zation of system biology, setting the biological basis for a more targeted therapeutic approaches [27,75].

Proteomics & degradomicsThe identification and characterization of both quantity and activity of all metabolytes is therefore critical for understand-ing biomolecular mechanisms of cellular processes, especially the proteins and proteinases that prone stem and differentiated cells to physiologic and disease fate, shedding light to the discovery of possible diagnostics, as well as therapeutic targets and novel drugs [82].

Starting from the classic definition of proteome, the entirety of all cellular proteins that determines the functional capacity of a cell at a particular point of its life, it is well known that human cells have got a general proteome profile involving about 26,000 different proteins, which differences are increased by several unusual cell metabolic activities (e.g., post-translational modi-fications including phosphorylation, acetylation, glycosylation, protein degradation and so on), which provide specific protein folding able to influence and guide both protein–protein and protein–substrate interactions [83]: all these metabolic activities set the basis for the cellular heterogeneity [27,84].

Due to the complexity of proteome evaluations (e.g., measurement of protein concentration, expression, modifications, degradation, translocation, interactions and specific activity), actually no single analytical method can measure all of these parameters in a single cell. Therefore a complex orchestra of biochemical, biophysical and molecular approaches have been developed in order to understand both proteome and degradome cell and tissue profiling in health and disease conditions [82].

Although a lot of analytical methods have been developed to analyze proteins and proteinases (e.g., gel electrophoresis, immuno assays, chromatography, mass spectrometry, cytometry and microscopy), the major limitations of these methods are linked to the requirement of large number of cells, obtaining important data but only from population-averaged measurements.

In this respect, SCA offers single-cell level measurement of constitutive and induced protein profiles, providing outstanding insights into cell mechanisms, paving the way for the understand-ing of heterogeneity in cellular response to both internal and external stimuli [82].

However, the more informative techniques for single-cell pro-teomics analysis are represented by mass spectrometry (MS) and CE. In conjunction with SCE, both may represent the highest sensitive analytical methods for the analysis of single cell pro-teome, providing excellent discriminative efficiencies but failing to identify unknown proteins; moreover, both MS and CE-based technologies involve the lysis of the target cell, and therefore they enable neither time-resolved nor spatial analysis of the single-cell proteome [27,84–86].

Even though the traditional tool to semi-quantitatively ana-lyze pathways and to study the proteome at single-cell level was by definition the flow cytometry (providing correlations among multiple proteins [87,88]), it is actually limited in analyzing samples (such as cells recovered from a biopsy, tissue or small volumes of biological fluid). This limit has been overcome by the develop-ment of microfluidic device that integrate sample handling and sorting [82]. The further limitation of flow cytometry is also linked to the number of proteins simultaneously available (not more than 15 proteins), even though developed the so called ‘mass cytom-etry’, a throughput complex combination of flow cytometry with the ultrahigh dimensionality and sensitivity of mass spectrometry has been developed [89,90], recently applied to map the cell cycle phases and to identify proteins on single-cell basis in healthy and cancerous hematopoietic cells [91].

Despite the recent achievements, complete single-cell prot-eomics has not been fully realized, mainly due to the combi-natorial complexity of post-translational modification, likely leading to a lot of different protein states present in each cell; this is to a lesser extent also true for the transcriptome, given alternative splicing and editing. On the other hand, it is totally different from genomics, where there is a unique genetic sequence that can be determined, also with a lot of potential different polymorphisms. Although SCA is in its infant age, in the future it should become possible to apply single-cell omic tools (e.g. genomics, transcriptomics, proteomics, degradomics and metabolomics) to discern subsets of cells with different

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biomachinery within individual cells (e.g. cancer and stem cells), which would complement conventional methods used for the evaluation of phenotypic heterogeneity of single cells and possibly lead to further development in biological and bio-medical research.

An interesting novel aspect is represented by the noninvasive single-cell enzyme assays, based on optical analysis through fluorogenic substrate (e.g., the fluorescence resonance energy transfer), able to provide enzyme location and activity in sub-cellular structures [92]; also the high-throughput technology of microarray has been adapted for the detection of proteins secreted by individual cells [93], but they may only underline the heteroge-neity of cell populations not providing a useful set/profile of the single-cell proteome.

Up-to-now, only the future integration of cell cultivation and processing, protein separation and extraction, all coupled to ana-lytical microdevices, might represent the unique possible chance to pave the way to success in single-cell proteomics [94].

Degradomics aims to identify the protease and protease sub-strate repertoires (or the so called ‘degradomes’), on an organism-wide scale, promising to uncover new roles for proteases in vivo. The study of the degradome at single-cell level is directly related to measurements of enzymatic activities and will facilitate the identification of new pharmaceutical targets to treat disease.

Single-cell capillary electrophoresis coupled with laser-induced fluorescence is a powerful approach for separation and quantita-tion of protease present in small samples and single live cells, although it does not provide a precise identification of the reaction products. On the other hand, MS is able to identify analytes but is lacking in sensitivity for most SCA applications [95].

Because enzyme activities are dependent on the interplay between target proteins, scaffold proteins, ions and other regula-tory factors, studying the degradome in cell lysates could alter intra cellular signaling, activate stress pathways before cell death and dilute cellular components [96]. For these reasons, even if flow cytometry is lacking in time-resolved biological data, it is actually the method of choice when temporal resolution of enzyme activ-ity, quantity and location are not required [27].

Other than the enzymatic activity and enzyme amounts of a single cell, noteworthy is the relationship between enzyme activ-ity and fluctuations in transcription/translation processes, able to provide oustanding information at the single-cell level on crucial biological pathways in different live cell [97,98].

Recent evidence shows that even clonally propagated cells in an in vitro population do not express the same set of cellular epitopes, and therefore the most attractive approach towards single-cell proteomics are linked to the antibody-based methods. Recently, single-cell technologies involving the use of multiple mono clonal antibodies (generated in the same species) may represent an out-standing tool for cellular detection of antigens [99]. Moreover, recent technological advancements (including the combination among LCM, SCA, immunohistochemistry proteomics) have uncovered a vast array of specific markers providing a platform for the discovery of new therapies directed against various malig-nancies [100].

According to the Human Protein Atlas [101] and the Antibodypedia [102,201,202], the specific antibodies are commer-cially available for almost all proteins, and these may be useful tools in conjunction to SCA methods. In fact, it has recently been demonstrated that single-cell proteomics might be accurately analyzed using a antibody-based microarray platform, providing a comprehensive picture of altered signal transduction networks in tumor cells and highlighting the effects of targeted therapies on protein signaling networks [103].

MetabolomicsThe identification and characterization of low molecular weight metabolites (below 3000 daltons) is the main goal of the rap-idly growing field of metabolomics, actually considered the best indicator of an organism’s phenotype, able to highlight the cru-cial organism’s response to genetic modification, disease, micro-environmental and macroenvironmental influences. Nevertheless, cellular metabolomes do not allow us to obtain insights into bio-chemical heterogeneity of cell populations suggesting to establish reliable methods for single-cell metabolomic studies despite using classic approaches.

The major limitation is linked to the possibility to investigate cellular physiology only on an average level, which may involve both noninvasive approaches (preserving the anatomical and functional integrity of the cell and enable in vivo analysis) and invasive methods (requiring extraction of analytes from the cell and implying the destruction of cellular integrity [104]. Starting from the first attempt to detect the physiology of single cells through microscopic analysis

and the so-called image-based detection of enzyme kinetics with fluorescent substrates, actually molecular sensors capable of metabolite analysis on the single-cell level are available. For example, aptamer-based technologies [105] and FRET sensors [106] are the most prominent approaches employing molecular sensors.

Actually, both sample preparation and processing are two cru-cial points to be resolved for single-cell metabolome analysis. One of the major limitations is linked to the adequate preserva-tion of the original metabolome (e.g., presence of enzymes with fast metabolic turnover rates). Currently, the trend is moving toward miniaturization and integration of the classic metabo-lomics approach with combined microfluidic quenching, lysis and separation of the metabolites.

Among all methods for metabolome analysis, MS surely plays an important role because of its ability to handle the chemical diversity of the involved compounds, revealing a detection limit reaching the attomole level, which is the required range for single-cell metabolome studies [107]. Furthermore, a recent study, based on capillary electrophoresis combined with quantitative laser-induced fluorescent detection, confirmed the possibility to detect the single-cell metabolome in neuronal cells [108].

Biomedical & clinical applicationsTranslational medicineThe clinical value of SCA methods and their suitability for medical-diagnostic applications are strongly related to the

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possibility, not only to detect the most minimum level of a spe-cific biomolecular component in an individual cell, but also to provide images about the cell, the intracellular molecules and to figure out their interactions. Actually, several techniques com-bine quantitative analyses at single-cell resolution with optical observation devices, even though the major limitations are linked to the number of target molecules (very low in a single cell). For these reasons, only the combination of imaging and whole-genome, transcriptome analysis with next-generation DNA sequencers might be the more promising combining approaches at the single-cell level.

The fundamental basis underpinning the various clinical applications of the SCA is represented by cellular heterogene-ity, a feature present in all cell populations apparently clonal, that arise from stochastic processes and particularly from noise generated during gene expression. The precise comprehension and characterization of the single cells composing an heteroge-neous population in physiopathological conditions, generally investigated in a small sample, can provide precise information on gene-regulatory networks, intracellular signaling, extracel-lular interactions that improve the knowledge on the origins of a large number of diseases (including cancer, autoimmune disease and so on), differentiation mechanisms, stem cells and cancer stem cells pathway, embryogenesis. Not only this but analyses with single-cell resolution could explain the different responses to drugs in patients, and could be the basis for the development of a personalized medical treatment [42,109–111].

The number of the target cells of SCA are actually significantly growing and include fetal cells, nucleated red blood cells, white blood cells, circulating tumor cells, stem cells, oocytes and blas-tomeres, as highlighted in a recent complete review, suggesting possible applications both in single and several cells [112].

AutoimmunityA greater understanding of the function of the human immune system at the single-cell level in healthy and diseased individuals is critical for discerning aberrant cellular behavior that occurs in pathological conditions such as autoimmune diseases.

Technological advances obtained by miniaturized devices allow high-throughput, multiplexed analysis of single cells and provides significant benefit in the study of immune cells, where the num-ber of cells available for testing is usually limited. Furthermore, the different cellular functions, lineages and clonotypic breadth among immune cells found in clinical samples, requires imple-mented bioanalytical approaches for monitoring human responses with single-cell resolution [113].

Using a multiparametric flow cytometry-based approach on peripheral blood mononuclear cells from healthy donors, the signaling responses have been demonstrated in multiple immune cell subpopulations, revealing age-associated and race-associated differences in immune signaling pathway activation [114]. Using a similar technological approach, the intracellular signaling of STATs mediated by cytokines has also been investigated, in three immune cell types of peripheral-blood mononuclear cells from systemic lupus erythematosus patients [115].

Single-cell resolution enzyme-linked immunospot assays was used to study ex vivo, proteolipid protein-specific memory cell reactivity from multiple sclerosis patients, by measuring IFN-γ and IL-5 production by T lymphocytes. This study performed by SCA showed a greatly increased number of Th1 effector cells in multiple sclerosis patients, suggesting why therapeutic strategies that aim at the induction of immune deviation show little efficacy in the established disease [20].

More recently the complete sequences of both α- and β-chains of TCR from single human T cells, in two T-cell mediated auto-immune diseases, psoriasis vulgaris and multiple sclerosis has been analyzed, showing heterogeneity in the clonal expansion defined by different TCR-α and TCR-β chains rearrangements [116].

Multiplexed measurements are able to provide data on differ-ent functions of immune cells including cell proliferation, secre-tions and stimulated cytotoxicity by effector antigens or APCs. In spite of this, a genomic profile including genotype variations such as single nucleotide polymorphism, copy number variation, in individual immune cells might be helpful in discovering poten-tial associations between genes and disease and developing target therapy [114,117].

Cancer researchAs the comprehension on the molecular basis of cancer dramatically increases, the development of technologies for multiple biomarker measurements from small clinical samples will be essential for the success of clinical trials, allowing stratification of patients, which may directly benefit of more efficient targeted therapeutics.

For solid tumors, standard diagnostic methods rely on qualitative tissue immunohistochemistry, but histological analysis provides only limited insight into the molecular classification of tumors and fails in multiparametric approaches. In this respect, high-through-put flow cytometry allows multiple measurements of protein in cells but requires high sample/reagent consumption, significantly limiting its diagnostic use. Therefore, molecular diagnosis of solid tumors necessitates a miniaturized platform featuring specimen economy and sensitive multiparametric measurement capabilities [118].

The complex cell mixtures of solid tumors (including non-cancerous fibroblasts, endothelial cells, lymphocytes, mac-rophages and so on) can mask the signal from cancer cells and thus complicate the intertumor and intratumor comparisons, which are the basis of molecular classification of the neoplastic microenvironment.

The genomic and related metabolomic characterization of single cancer cells may reveal the origins, the stemness, the properties of cancer cells, their progression and susceptibility to drugs and the response of the immune system to the cancer cells and therapies. In this context, recently, a genome-copy-number analysis on breast cancer single cells by using flow-sorted nuclei has been reported, whole genome amplification and next generation sequencing, revealing subpopulations with different disease progression [119,120].

Through different cancer models and novel biomolecular tech-nological approaches, all these studies unequivocally demonstrate the ‘cellular heterogeneity of tumors’, composed of significantly

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different cells (differentiated cancer cells, cancer stem cells, non-cancer stem cells) hierarchically organized, with diverse biomo-lecular behaviors and morphological aspects [16,17].

In spite of the improvement in cell and molecular biology methods, conventional biological approaches allowed our initial comprehension of cellular physiopathologic functions, through assays often based on lysis or disaggregation of cell mixtures. However, cancer cell heterogeneity may limit (or at least mask) the detection of biomolecules actually identified only from the averages of a large population of cells, missing (or at least neglect-ing) molecules produced only by ‘rare’ cells.

The intratumor cell heterogeneity is a hot topic of recent debates among the cancer research community, highlighting that pre-sent technologies cannot completely solve the identification and characterization of single cells from heterogeneous cancer cell populations in identical tissue, not because of detection sensitivity but because of the bioinformatics aspects. Alternatively, the laser capture microdissection (LCM) has been employed to recover the typical cell groups (not single cells) for omics studies [121]. LCM is a versatile, nonmolecular, minimally disruptive method to obtain cytologically and/or phenotypically defined groups of cells from heterogeneous tissues. Although this approach has obvious techni-cal limitations (e.g. selection of many cells at once not discerning the cell subtypes, and the inability to prepare homogenous isolates of specific subpopulations of single cells from clusters), it gener-ated much data with biological significance and data otherwise not expected, mainly due to protein and molecular profiles at an unprecedented tissue or cell cluster resolution [122].

To overcome the well known problems and limitations connected to LCM, the combination of LCM with immuno cytochemical methods that allow the more efficient morpho functional analysis of phenotypically ‘similar’ cells from anatomically complex organs has been proposed [123]. The increased sensitivity of this method (as well as those obtained with the combination of LCM with RT-PCR) allow us to isolate a homogenous population of cells, but not to resolve the problem of cell heterogeneity within cluster cells (e.g., cancer stem cells from noncancer stem cells [16]).

In this respect, a recent study (using a capillary based vacuum-assisted microdissection device able to limit/avoid potentially harmful treatment of cells) has opened new frontiers for the study at the cellular resolution of all cell omics, demonstrating high accu-racy and quality and suggesting it as alternative tool for LCM [36].

However, LCM and SCA methods actually may be considered as state-of-the-art technologies to provide reliable approaches to study omics at cluster and single-cell resolution, even if both techniques show important caveats and drawbacks.

Also both the transcriptome and proteome in single cancer cells have been investigated, and appear to be more effective for predicting prognosis than the pathological grade [124].

Omics sciences applied to individual cancer cells play a relevant role for the development of new pharmacologic treatment indis-pensible for personalized therapy. This has just been partially obtained with the introduction of Herceptin treatment in breast cancer, a target therapy focused for breast cancer patient showing ERBB2 heterogeneity [125].

Of considerable importance, for clinical and diagnostic research in the field of cancer, is the possibility of recovery, identification and characterization of circulating tumor cells (CTCs), the meta-static cells derived from a primary epithelial cancer, which spread to other organs by shedding cells into the bloodstream and/or lymphatic channels, and whose identification has been shown to correlate with clinical outcome in patients with metastatic breast, prostate, colorectal and lung cancer [18,126].

Due to the small number of CTCs and their heterogeneity, to better characterize them, an immunomagnetic separation tech-nology, the MagSweeper has recently been developed, that gently extracts live CTCs with high purity from unfixed, unfractionated blood, facilitating robust analyses at the single-cell resolution and without the problem of leucocytes contamination, provid-ing a transcriptional profile of CTCs subgroups in patients with primary and metastatic breast cancer [18,127,128].

The newer devices are reporting greater efficiencies of capture and larger absolute numbers of CTCs, obtaining specific cancer stem cells in up to 99% of patients with breast, lung, pancreatic and colon cancers, with 50% purity, and with cell numbers from five to over 1200 [126]. The information that the single cancer cell contains is likely to be the key to therapeutically targeting every cell in a tumor. On the other hand, the inherent heterogeneity in cancer, coupled with the environmental diversity due to unpredictable tis-sue structures, suggests that we have to neglect the nonuniformity of cancer when it is considered as a complex biological system [126].

Stem-cell researchDespite substantial progress in the knowledge of stem-cell biol-ogy at the genomic and transcriptomic levels (triggered by gene expression and proteomic studies on large cell populations using microarrays, antibody chips, peptide arrays, gel electrophoresis and liquid chromatography combined with MS), much remains to be discover about changes in the metabolome and proteome in order to apply stem cells to replacement therapy and individual-ized medicine. Little is known about the molecular properties of stem cells, their molecular pathways and intracellular signaling that drives proliferation and specialization of these cells into fully functional organocommitted cells. In spite of this, recent technol-ogy development for sample collection, signal acquisition and data processing in a high-throughput manner could improve single stem-cell investigations.

A novel method has been developed combining microfluidic-based SCA to demonstrate that multiple subpopulations exist in murine long-term hematopoietic stem cells [129]. Because of pro-cesses such as embryogenesis, apoptosis, regeneration, cell cycle and differentiation, in which the involved stem cells are highly dynamic and often associated with dramatic changes in the metabolome and peptidome of the cell of interest [84] to improve studies on stem-cell heterogeneity; continuous long-term single-cell observation through noninvasive methods are needed. Only with live-cell imaging is quantifying the dynamics and kinetics of molecular behavior in a single cell possible [130,131]. Even if in vivo single stem-cell obser-vation is limited by technical difficulties related to the necessity to immobilize the organism, in vitro, this approach has just been

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applied to monitor the hematopoietic cytokines action on lineage choice [132,133], the fate of daughter cells before division of neural progenitor cells [134] and of skeletal muscle satellite cells [135].

Expert commentary & five-year viewHeterogeneity between individual clonal cells is a common fea-ture influencing the intracellular signaling network, extracellular interactions and regulatory processes, indispensable for differ-ent cells to respond to the continuous microenvironmental and intracellular stimuli, in order to take advantage of the inherent stochastic variability in gene expression and increase their survival at the expense of the rest of the clonal cell population.

Due to its relation to numerous stochastic transcriptional events, cellular signals originate by a genetically clonal, but pheno-typically heterogeneous, population that generates different and individual responses, which cannot be revealed by conventional techniques investigating biological samples.

The latter, in fact, can provide only averaged measurement on a heterogeneous cell population, and single cell responses/features are hidden within an unspecific cellular background of cells, which have different behavior.

Although there is still a great need for further improvements in several aspect of SCA (like the miniaturization, integration, detection sensitivity, bioinformatic softwares), the combination of new multidisciplinary technologies might enable the develop-ment of new affordable high-throughput methods for omics-based single-cell evaluations.

Moreover, to avoid cell destruction, to preserve biomolecular information and to monitor cell long-term behavior outside the specific microenvironment, improvements of live cell images and other in situ techniques are absolutely needed.

Therefore, theoretical, analytical and technological improve-ments, investigating system biology with single-cell resolution, become essential to recover, monitor and characterize population heterogeneity and, finally, to provide data for a molecular signature. Recent advances in SCA biotechniques and methods give rise to the development of sophisticated, miniaturized and more sensitive devices to analyze real-time cellular changes at individual resolution.

In the same way, crucial improvements in DNA and RNA sequencing, proteinases, proteins and metabolite functional char-acterization extended cell biology to the omics era. Combining omics sciences, generally used to provide large-scale information

on cells, to single-cell technology, we can decipher the single-cell omics, obtaining a dynamic model of all cellular functions ranging from the genome to the complete metabolome of a living cell [136].

Single-cell omics represent a powerful tool in clinical and diag-nostic research allowing the comprehension of cellular variabili-ties during cell proliferation, the investigation of cycle-dependent effects in single cells, the detection of cellular subpopulations and differentiation states and providing an insight into the various and inhomogeneous cellular responses to extracellular stimuli.

To approach tissue heterogeneity at a single-cell level, first of all, the cells should be well classified, and then the omics data from single cells should be integrated, so that we can finally interpret the tissues. Indeed, even if the omics data are piled up, it may not be that we can understand the signal transduction and the regulation of gene expression in the microenvironment of tumor tissues.

Thus, SCA plays a relevant role in characterizing cells in physiopathological conditions, mainly in biological samples in which the cells amount is strongly reduced and the detection of heterogeneous cellular types have a diagnostic, clinic and prog-nostic value.

Application of SCA in developmental biology, stem cell and cancer research and in the study of heterogeneous cell profile in immune system during autoimmune diseases represent a relevant and useful advancement, in order to identify and quantify several molecular markers in single cells at different times, recognize coexpression of markers, and last, but not least, allow noninva-sive diagnosis, monitoring of the disease (e.g., minimal residual disease) and improved individualized therapy. This evolution will likely be possible only with the combined advancement of new apparatus able to bring the omics technologies at single-cell level with technologies capable of isolating viable single cells. Based on the increase in speed, sensitivity and throughput observed in the last few years, it is envisaged that single-cell omics will become a diffuse investigational strategy in the near future.

Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Traditional biological analyses probe cells averaging the relevant individual responses, neglecting differences in differentiation, proliferation, responses to stimuli and disease onset.

• Single-cell analysis is a powerful approach for understanding changes in gene expression and protein profile within an isogenic cell population.

• Single-cell analyses have been achieved by miniaturization of microfluidic and microreactor devices for chemical and biological approaches.

• The applications of single-cell analyses may cover almost all aspects of system biology (genomics, transcriptomics, metabolomics, fluxomics and degradomics) with spatiotemporal resolution.

• Very few strategies have been developed to exploit the potential of single cell analyses.

• An integration of microfluidic/microreactor devices and technologies with analytical instruments to enhance sensitivity is urgently needed.

• The technologies for single-cell analyses are still in their infancy but with an increasing potential to improve the accuracy of diagnosis and novel therapeutic approaches based on knowledge of intracellular and intercellular networks.

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References1 Turner W. The Cell Theory, Past and

Present. J. Anat. Physiol. 24(Pt 2), 253–287 (1890).

2 Kalisky T, Quake SR. Single-cell genomics. Nat. Methods 8(4), 311–314 (2011).

3 Templer RH, Ces O. New frontiers in single-cell analysis. J. R. Soc. Interface 5(Suppl. 2), S111–S112 (2008).

4 Schubert C. Single-cell analysis: The deepest differences. Nature 480(7375), 133–137 (2011).

5 Walling MA, Shepard JR. Cellular heterogeneity and live cell arrays. Chem. Soc. Rev. 40(7), 4049–4076 (2011).

6 Rosenfeld S. Patterns of stochastic behavior in dynamically unstable high-dimensional biochemical networks. Gene Regul. Syst. Bio. 3, 1–10 (2009).

7 Bar-Even A, Paulsson J, Maheshri N et al. Noise in protein expression scales with natural protein abundance. Nat. Genet. 38(6), 636–643 (2006).

8 Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science 297(5584), 1183–1186 (2002).

9 Levsky JM, Shenoy SM, Pezo RC, Singer RH. Single-cell gene expression profiling. Science 297(5582), 836–840 (2002).

10 Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A. Regulation of noise in the expression of a single gene. Nat. Genet. 31(1), 69–73 (2002).

11 Guo CX, Zheng XT, Lu ZS, Lou XW, Li CM. Biointerface by cell growth on layered graphene-artificial peroxidase-protein nanostructure for in situ quantitative molecular detection. Adv. Mater. Weinheim 22(45), 5164–5167 (2010).

12 Pui TS, Agarwal A, Ye F, Balasubramanian N, Chen P. CMOS-Compatible nanowire sensor arrays for detection of cellular bioelectricity. Small 5(2), 208–212 (2009).

13 Tian B, Cohen-Karni T, Qing Q, Duan X, Xie P, Lieber CM. Three-dimensional, flexible nanoscale field-effect transistors as localized bioprobes. Science 329(5993), 830–834 (2010).

14 Kovarik ML, Allbritton NL. Measuring enzyme activity in single cells. Trends Biotechnol. 29(5), 222–230 (2011).

15 Lidstrom ME, Konopka MC. The role of physiological heterogeneity in microbial population behavior. Nat. Chem. Biol. 6(10), 705–712 (2010).

16 Gilbertson RJ, Graham TA. Cancer: Resolving the stem-cell debate. Nature 488(7412), 462–463 (2012).

17 Mannello F, Ligi D. Resolving the cell heterogeneity of tumours and searching reliable protein cancer biomarkers in breast microenvironment. BMC Cancer (2012). (In Press)

18 Powell AA, Talasaz AH, Zhang H et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS One 7(5), e33788 (2012).

19 van Baarsen LG, Wijbrandts CA, Timmer TC, van der Pouw Kraan TC, Tak PP, Verweij CL. Synovial tissue heterogene-ity in rheumatoid arthritis in relation to disease activity and biomarkers in peripheral blood. Arthritis Rheum. 62(6), 1602–1607 (2010).

20 Pelfrey CM, Rudick RA, Cotleur AC, Lee JC, Tary-Lehmann M, Lehmann PV. Quantification of self-recognition in multiple sclerosis by single-cell analysis of cytokine production. J. Immunol. 165(3), 1641–1651 (2000).

21 Lindström S, Andersson-Svahn H. Overview of single-cell analyses: microde-vices and applications. Lab Chip 10(24), 3363–3372 (2010).

22 Mettetal JT, Muzzey D, Pedraza JM, Ozbudak EM, van Oudenaarden A. Predicting stochastic gene expression dynamics in single cells. Proc. Natl Acad. Sci. USA 103(19), 7304–7309 (2006).

23 Yu J, Xiao J, Ren X, Lao K, Xie XS. Probing gene expression in live cells, one protein molecule at a time. Science 311(5767), 1600–1603 (2006).

24 Lindström S. Flow cytometry and microscopy as means of studying single cells: a short introductional overview. Methods Mol. Biol. 853, 13–15 (2012).

25 Konokhova AI, Yurkin MA, Moskalensky AE et al. Light-scattering flow cytometry for identification and characterization of blood microparticles. J. Biomed. Opt. 17(5), 057006 (2012).

26 Wang D, Bodovitz S. Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol. 28(6), 281–290 (2010).

27 Fritzsch FS, Dusny C, Frick O, Schmid A. Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu. Rev. Chem. Biomol. Eng. 3, 129–155 (2012).

28 Lindström S, Andersson-Svahn H. Miniaturization of biological assays – overview on microwell devices for single-cell analyses. Biochim. Biophys. Acta 1810(3), 308–316 (2011).

29 Lin Y, Trouillon R, Safina G, Ewing AG. Chemical analysis of single cells. Anal. Chem. 83(12), 4369–4392 (2011).

30 Breadmore MC. Capillary and microchip electrophoresis: challenging the common conceptions. J. Chromatogr. A 1221, 42–55 (2012).

31 Barbas C, Moraes EP, Villaseñor A. Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples. J. Pharm. Biomed. Anal. 55(4), 823–831 (2011).

32 Hosokawa M, Arakaki A, Takahashi M, Mori T, Takeyama H, Matsunaga T. High-density microcavity array for cell detection: single-cell analysis of hematopoi-etic stem cells in peripheral blood mononuclear cells. Anal. Chem. 81(13), 5308–5313 (2009).

33 Andersson H, van den Berg A. Microtechnologies and nanotechnologies for single-cell analysis. Curr. Opin. Biotechnol. 15(1), 44–49 (2004).

34 Toriello NM, Douglas ES, Thaitrong N et al. Integrated microfluidic bioprocessor for single-cell gene expression analysis. Proc. Natl Acad. Sci. USA 105(51), 20173–20178 (2008).

35 Ros A. Microfluidics in cell analysis. Anal. Bioanal. Chem. 390(3), 799–800 (2008).

36 Kudo LC, Vi N, Ma Z et al. Novel Cell and Tissue Acquisition System (CTAS): microdissection of live and frozen brain tissues. PLoS One 7(7), e41564 (2012).

37 Lecault V, White AK, Singhal A, Hansen CL. Microfluidic single cell analysis: from promise to practice. Curr. Opin. Chem. Biol. 16(3–4), 381–390 (2012).

38 Zhong JF, Feng Y, Taylor CR. Microfluidic devices for investigating stem cell gene regulation via single-cell analysis. Curr. Med. Chem. 15(28), 2897–2900 (2008).

39 Hellmich W, Pelargus C, Leffhalm K, Ros A, Anselmetti D. Single cell manipulation, analytics, and label-free protein detection in microfluidic devices for systems nanobiology. Electrophoresis 26(19), 3689–3696 (2005).

40 Cavanaugh EJ, Crew JR, DiMario JX. Direct electrical stimulation of myogenic cultures for analysis of muscle fiber type control. Methods Mol. Biol. 798, 77–84 (2012).

41 Gou HL, Zhang XB, Bao N, Xu JJ, Xia XH, Chen HY. Label-free electrical discrimination of cells at normal, apoptotic and necrotic status with a microfluidic device. J. Chromatogr. A 1218(33), 5725–5729 (2011).

Single-cell analysis in the omic era

Author Pro

of

Expert Rev. Proteomics 9(6), (2012)12

Review

42 Kelbauskas L, Ashili SP, Houkal J et al. Method for physiologic phenotype characterization at the single-cell level in non-interacting and interacting cells. J. Biomed. Opt. 17(3), 037008 (2012).

43 Rosen DB, Minden MD, Kornblau SM et al. Functional characterization of FLT3 receptor signaling deregulation in acute myeloid leukemia by single cell network profiling (SCNP). PLoS One 5(10), e13543 (2010).

44 Tu LC, Musser SM. Single molecule studies of nucleocytoplasmic transport. Biochim. Biophys. Acta 1813(9), 1607–1618 (2011).

45 Borah S, Nichols LA, Hassman LM, Kedes DH, Steitz JA. Tracking expression and subcellular localization of RNA and protein species using high-throughput single cell imaging flow cytometry. RNA 18(8), 1573–1579 (2012).

46 Gaudet S, Spencer SL, Chen WW, Sorger PK. Exploring the contextual sensitivity of factors that determine cell-to-cell variability in receptor-mediated apoptosis. PLoS Comput. Biol. 8(4), e1002482 (2012).

47 Spencer SL, Sorger PK. Measuring and modeling apoptosis in single cells. Cell 144(6), 926–939 (2011).

48 Marcy Y, Ouverney C, Bik EM et al. Dissecting biological ‘dark matter’ with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl Acad. Sci. USA 104(29), 11889–11894 (2007).

49 Steele PR, Pires JC. Biodiversity assessment: state-of-the-art techniques in phylogenomics and species identification. Am. J. Bot. 98(3), 415–425 (2011).

50 Fiegler H, Geigl JB, Langer S et al. High resolution array-CGH analysis of single cells. Nucleic Acids Res. 35(3), e15 (2007).

51 Cheung VG, Nelson SF. Whole genome amplification using a degenerate oligonu-cleotide primer allows hundreds of genotypes to be performed on less than one nanogram of genomic DNA. Proc. Natl Acad. Sci. USA 93(25), 14676–14679 (1996).

52 Hubert R, Weber JL, Schmitt K, Zhang L, Arnheim N. A new source of polymorphic DNA markers for sperm typing: analysis of microsatellite repeats in single cells. Am. J. Hum. Genet. 51(5), 985–991 (1992).

53 Klein CA, Schmidt-Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmüller G. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis

of single cells. Proc. Natl Acad. Sci. USA 96(8), 4494–4499 (1999).

54 Panelli S, Damiani G, Espen L, Micheli G, Sgaramella V. Towards the analysis of the genomes of single cells: further characteri-sation of the multiple displacement amplification. Gene 372, 1–7 (2006).

55 Dean FB, Hosono S, Fang L et al. Comprehensive human genome amplifica-tion using multiple displacement amplifica-tion. Proc. Natl Acad. Sci. USA 99(8), 5261–5266 (2002).

56 Nadeau JG, Pitner JB, Linn CP, Schram JL, Dean CH, Nycz CM. Real-time, sequence-specific detection of nucleic acids during strand displacement amplification. Anal. Biochem. 276(2), 177–187 (1999).

57 Kalisky T, Blainey P, Quake SR. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45, 431–445 (2011).

58 Lasken RS, Egholm M. Whole genome amplification: abundant supplies of DNA from precious samples or clinical speci-mens. Trends Biotechnol. 21(12), 531–535 (2003).

59 Lasken RS. Genomic DNA amplification by the multiple displacement amplification (MDA) method. Biochem. Soc. Trans. 37(Pt 2), 450–453 (2009).

60 Binga EK, Lasken RS, Neufeld JD. Something from (almost) nothing: the impact of multiple displacement amplifica-tion on microbial ecology. ISME J. 2(3), 233–241 (2008).

61 Marcy Y, Ishoey T, Lasken RS et al. Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet. 3(9), 1702–1708 (2007).

62 Fan HC, Wang J, Potanina A, Quake SR. Whole-genome molecular haplotyping of single cells. Nat. Biotechnol. 29(1), 51–57 (2011).

63 Sakaizawa K, Goto Y, Kiniwa Y et al. Mutation analysis of BRAF and KIT in circulating melanoma cells at the single cell level. Br. J. Cancer 106(5), 939–946 (2012).

64 Kroneis T, Geigl JB, El-Heliebi A et al. Combined molecular genetic and cytogenetic analysis from single cells after isothermal whole-genome amplification. Clin. Chem. 57(7), 1032–1041 (2011).

65 Tang F, Barbacioru C, Nordman E et al. RNA-Seq analysis to capture the transcrip-tome landscape of a single cell. Nat. Protoc. 5(3), 516–535 (2010).

66 Chao TC, Ros A. Microfluidic single-cell analysis of intracellular compounds. J. R.

Soc. Interface 5(Suppl. 2), S139–S150 (2008).

67 Ozsolak F, Milos PM. Single-molecule direct RNA sequencing without cDNA synthesis. Wiley Interdiscip. Rev. RNA 2(4), 565–570 (2011).

68 Ozsolak F, Milos PM. Transcriptome profiling using single-molecule direct RNA sequencing. Methods Mol. Biol. 733, 51–61 (2011).

69 Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12(2), 87–98 (2011).

70 Goetz JJ, Trimarchi JM. Transcriptome sequencing of single cells with Smart-Seq. Nat. Biotechnol. 30(8), 763–765 (2012).

71 Tang F, Lao K, Surani MA. Development and applications of single-cell transcrip-tome analysis. Nat. Methods 8(Suppl. 4), S6–11 (2011).

72 Tang F, Barbacioru C, Wang Y et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6(5), 377–382 (2009).

73 Eberwine J, Bartfai T. Single cell transcriptomics of hypothalamic warm sensitive neurons that control core body temperature and fever response Signaling asymmetry and an extension of chemical neuroanatomy. Pharmacol. Ther. 129(3), 241–259 (2011).

74 Ramsköld D, Luo S, Wang YC et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30(8), 777–782 (2012).

75 Bartfai T, Buckley PT, Eberwine J. Drug targets: single-cell transcriptomics hastens unbiased discovery. Trends Pharmacol. Sci. 33(1), 9–16 (2012).

76 Zhong JF, Chen Y, Marcus JS et al. A microfluidic processor for gene expression profiling of single human embryonic stem cells. Lab Chip 8(1), 68–74 (2008).

77 Marcus JS, Anderson WF, Quake SR. Microfluidic single-cell mRNA isolation and analysis. Anal. Chem. 78(9), 3084–3089 (2006).

78 Furutani S, Nagai H, Takamura Y, Aoyama Y, Kubo I. Detection of expressed gene in isolated single cells in microchambers by a novel hot cell-direct RT-PCR method. Analyst 137(13), 2951–2957 (2012).

79 Le TT, Emonet T, Harlepp S, Guet CC, Cluzel P. Dynamical determinants of drug-inducible gene expression in a single bacterium. Biophys. J. 90(9), 3315–3321 (2006).

Mannello, Ligi & Magnani

Author Pro

of

13www.expert-reviews.com

Review

80 Terazono H, Kim H, Hayashi M et al. A non-destructive culturing and cell sorting method for cardiomyocytes and neurons using a double alginate layer. PLoS One 7(8), e42485 (2012).

81 Warren L, Bryder D, Weissman IL, Quake SR. Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc. Natl Acad. Sci. USA 103(47), 17807–17812 (2006).

82 Wu M, Singh AK. Single-cell protein analysis. Curr. Opin. Biotechnol. 23(1), 83–88 (2012).

83 Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13(4), 227–232 (2012).

84 Rubakhin SS, Romanova EV, Nemes P, Sweedler JV. Profiling metabolites and peptides in single cells. Nat. Methods 8(Suppl. 4), S20–S29 (2011).

85 Lanni EJ, Rubakhin SS, Sweedler JV. Mass spectrometry imaging and profiling of single cells. J. Proteomics 75(16), 5036–5051 (2012).

86 Nelson AR, Allbritton NL, Sims CE. Rapid sampling for single-cell analysis by capillary electrophoresis. Methods Cell Biol. 82, 709–722 (2007).

87 Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523–529 (2005).

88 Irish JM, Hovland R, Krutzik PO et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118(2), 217–228 (2004).

89 Ornatsky O, Bandura D, Baranov V, Nitz M, Winnik MA, Tanner S. Highly multiparametric analysis by mass cytometry. J. Immunol. Methods 361(1–2), 1–20 (2010).

90 Bandura DR, Baranov VI, Ornatsky OI et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81(16), 6813–6822 (2009).

91 Behbehani GK, Bendall SC, Clutter MR, Fantl WJ, Nolan GP. Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry. A 81(7), 552–566 (2012).

92 Ouyang M, Huang H, Shaner NC et al. Simultaneous visualization of protumori-genic Src and MT1-MMP activities with fluorescence resonance energy transfer. Cancer Res. 70(6), 2204–2212 (2010).

93 Ma C, Fan R, Ahmad H et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17(6), 738–743 (2011).

94 Gutstein HB, Morris JS, Annangudi SP, Sweedler JV. Microproteomics: analysis of protein diversity in small samples. Mass Spectrom. Rev. 27(4), 316–330 (2008).

95 Brown RB, Hewel JA, Emili A, Audet J. Single amino acid resolution of proteolytic fragments generated in individual cells. Cytometry. A 77(4), 347–355 (2010).

96 Osenkowski P, Ye W, Wang R, Wolfe MS, Selkoe DJ. Direct and potent regulation of gamma-secretase by its lipid microenviron-ment. J. Biol. Chem. 283(33), 22529–22540 (2008).

97 Jiang D, Sims CE, Allbritton NL. Single-cell analysis of phosphoinositide 3-kinase and phosphatase and tensin homolog activation. Faraday Discuss. 149, 187–200; discussion 227 (2011).

98 Sims CE, Allbritton NL. Single-cell kinase assays: opening a window onto cell behavior. Curr. Opin. Biotechnol. 14(1), 23–28 (2003).

99 Ranjan AK, Joglekar MV, Atre AN, Patole M, Bhonde RR, Hardikar AA. Cellular detection of multiple antigens at single cell resolution using antibodies generated from the same species. J. Immunol. Methods 379(1-2), 42–47 (2012).

100 Li C, Sasaroli D, Chen X et al. Tumor vascular biomarkers: new opportunities for cancer diagnostics. Cancer Biomark. 8(4–5), 253–271 (2010).

101 Colwill K, Gräslund S; Renewable Protein Binder Working Group. A roadmap to generate renewable protein binders to the human proteome. Nat. Methods 8(7), 551–558 (2011).

102 Björling E, Uhlén M. Antibodypedia, a portal for sharing antibody and antigen validation data. Mol. Cell Proteomics 7(10), 2028–2037 (2008).

103 Shi Q, Qin L, Wei W et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109(2), 419–424 (2012).

104 Amantonico A, Urban PL, Zenobi R. Analytical techniques for single-cell metabolomics: state of the art and trends. Anal. Bioanal. Chem. 398(6), 2493–2504 (2010).

105 Zhao J, Zhang L, Chen C, Jiang J, Yu R. A novel sensing platform using aptamer and RNA polymerase-based amplification for

detection of cancer cells. Anal. Chim. Acta 745C, 106–111 (2012).

106 Fessl T, Adamec F, Polívka T, Foldynová-Trantírková S, Vácha F, Trantírek L. Towards characterization of DNA structure under physiological conditions in vivo at the single-molecule level using single-pair FRET. Nucleic Acids Res. 40(16), e121 (2012).

107 Tsuyama N, Mizuno H, Masujima T. Molecular and functional analysis of cellular phenomena using single-cell mass spectrometry. Biol. Pharm. Bull. 35(9), 1425–1431 (2012).

108 Essaka DC, Prendergast J, Keithley RB et al. Single cell ganglioside catabolism in primary cerebellar neurons and glia. Neurochem. Res. 37(6), 1308–1314 (2012).

109 Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J. Intern. Med. 271(2), 111–121 (2012).

110 Bakstad D, Adamson A, Spiller DG, White MR. Quantitative measurement of single cell dynamics. Curr. Opin. Biotechnol. 23(1), 103–109 (2012).

111 Beckman RA, Schemmann GS, Yeang CH. Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer. Proc. Natl Acad. Sci. USA 109(36), 14586–14591 (2012).

112 Shirai M, Taniguchi T, Kambara H. Emerging Applications of Single-Cell Diagnostics. Top. Curr. Chem. doi: 10.1007/128_2012_327 (2012)(Epub ahead of print).

113 Yalçin A, Yamanaka YJ, Love JC. Analytical technologies for integrated single-cell analysis of human immune responses. Methods Mol. Biol. 853, 211–235 (2012).

114 Longo DM, Louie B, Putta S et al. Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation. J. Immunol. 188(4), 1717–1725 (2012).

115 Huang X, Guo Y, Bao C, Shen N. Multidimensional single cell based STAT phosphorylation profiling identifies a novel biosignature for evaluation of systemic lupus erythematosus activity. PLoS One. 6(7), e21671 (2011).

116 Kim SM, Bhonsle L, Besgen P et al. Analysis of the paired TCR α- and

Single-cell analysis in the omic era

Author Pro

of

Expert Rev. Proteomics 9(6), (2012)14

Review

β-chains of single human T cells. PLoS One. 7(5), e37338 (2012).

117 Longo D, Hasty J. Dynamics of single-cell gene expression. Mol. Syst. Biol. 2, 64 (2006).

118 Sun J, Masterman-Smith MD, Graham NA et al. A microfluidic platform for systems pathology: multiparameter single-cell signaling measurements of clinical brain tumor specimens. Cancer Res. 70(15), 6128–6138 (2010).

119 Navin N, Hicks J. Future medical applications of single-cell sequencing in cancer. Genome Med. 3(5), 31 (2011).

120 Navin N, Kendall J, Troge J et al. Tumour evolution inferred by single-cell sequenc-ing. Nature 472(7341), 90–94 (2011).

121 Cheng L, Zhang S, Maclennan GT et al. Laser-assisted microdissection in transla-tional research: theory, technical considerations, and future applications. Appl. Immunohistochem. Mol. Morphol. doi: 10.1097 (2012)(Epub ahead of print).

122 Xu BJ. Combining laser capture microdis-section and proteomics: methodologies and clinical applications. Proteomics. Clin. Appl. 4(2), 116–123 (2010).

123 Baskin DG, Bastian LS. Immuno-laser capture microdissection of rat brain neurons for real time quantitative PCR. Methods Mol. Biol. 588, 219–230 (2010).

124 Dalerba P, Kalisky T, Sahoo D et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29(12), 1120–1127 (2011).

125 Romond EH, Perez EA, Bryant J et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353(16), 1673–1684 (2005).

126 Nagrath S, Sequist LV, Maheswaran S et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450(7173), 1235–1239 (2007).

127 Kling J. Beyond counting tumor cells. Nat. Biotechnol. 30(7), 578–580 (2012).

128 Talasaz AH, Powell AA, Huber DE et al. Isolating highly enriched populations of circulating epithelial cells and other rare cells from blood using a magnetic sweeper device. Proc. Natl Acad. Sci. USA 106(10), 3970–3975 (2009).

129 Glotzbach JP, Januszyk M, Vial IN et al. An information theoretic, microfluidic-based single cell analysis permits identification of subpopulations among putatively homogeneous stem cells. PLoS One 6(6), e21211 (2011).

130 Kokkaliaris KD, Loeffler D, Schroeder T. Advances in tracking hematopoiesis at the single-cell level. Curr. Opin. Hematol. 19(4), 243–249 (2012).

131 Schroeder T. Long-term single-cell imaging of mammalian stem cells. Nat. Methods 8(Suppl. 4), S30–S35 (2011).

132 Rieger MA, Schroeder T. Instruction of lineage choice by hematopoietic cytokines. Cell Cycle 8(24), 4019–4020 (2009).

133 Rieger MA, Hoppe PS, Smejkal BM, Eitelhuber AC, Schroeder T. Hematopoietic cytokines can instruct lineage choice. Science 325(5937), 217–218 (2009).

134 Cohen AR, Gomes FL, Roysam B, Cayouette M. Computational prediction of neural progenitor cell fates. Nat. Methods 7(3), 213–218 (2010).

135 Kuang S, Kuroda K, Le Grand F, Rudnicki MA. Asymmetric self-renewal and commitment of satellite stem cells in muscle. Cell 129(5), 999–1010 (2007).

136 Huang WE, Zhou J. When single cell technology meets omics, the new toolbox of analytical biotechnology is emerging. Curr. Opin. Biotechnol. 23(1), 1 (2012).

Websites

201 The Human Protein Atlas www.proteinatlas.org

202 Nature Antibodypedia www.antibodypedia.com

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