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. 2006 Nov 15;39(6):495–505. doi: 10.1111/j.1365-2184.2006.00407.x

Cytomics – importance of multimodal analysis of cell function and proliferation in oncology

A Tárnok 1, J Bocsi 1, G Brockhoff 2
PMCID: PMC6496464  PMID: 17109634

Abstract

Abstract.  Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self‐sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native‐state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High‐content and high‐throughput single‐cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

INTRODUCTION

Cytomics is bioinformatic knowledge extraction, from a large amount of structural and functional information by molecular cell phenotype analysis of tissues, organs and organisms, at the single cell level by image or flow cytometry (Valet 2005a, 2005b). Single‐cell analysis by cytometric methods has equally reached high‐throughput capacity in recent years, permitting the assessment and quantification of the molecular morphology of single cells (Tarnok 2004, 2006; Valet et al. 2004). Typical investigations use hypothesis‐driven parameter selection in combination with hypothesis‐free exhaustive knowledge extraction, that is, cytomics (Kitano 2002; Hood et al. 2004; Kriete 2005; Murphy 2005; Valet 2005b). Although a given hypothesis can be proved or disproved by a given experiment, the evaluation of all cell data in a hypothesis‐free fashion (discovery science) enables the exploration of unknown multiparametric data spaces. The secondary results thus generated are subsequently available for new hypothesis development. This approach reflects the reality that cells and their inter‐relationship and not genes or biomolecules represent the elementary functional units of organisms. Diseases are caused by molecular alterations in cells or cellular systems (cytomes) as a consequence of genotype and exposure to external or internal influences.

Cytomics in oncology

It is evident that systemic cell analysis has instant impact on oncology and cancer research (Celis et al. 2005; Khalil & Hill 2005; Abramovitz & Leyland‐Jones 2006; Hornberg et al. 2006; Liu et al. 2006). It helps unravel molecular circuits underlying cell proliferation, cancer development and ultimately, treatment (Alberghina et al. 2004; Spencer et al. 2004; Tabach et al. 2005; van Leeuwen et al. 2006).

Flow cytometry (FCM) is a very powerful tool for metabolic and dynamic analysis of living single cells. It is the method of choice for understanding how a system behaves over time and under various conditions (Sachs et al. 2005). Crucial molecular mechanisms underlying specific kinds of cellular behaviour for example, differentiation, cell death, growth and cell proliferation (Yarden & Sliwkowski 2001), can be identified using FCM. Quantitative analysis of networking molecules within cells might be applied to understand native‐state tissue signalling biology, complex drug actions and dysfunctional signalling in diseased cells such as cancer cells. Awareness is evolving that intra‐cellular signal diversity and specificity, initially triggered by extracellular signals, is based on highly complex networking, inter‐pathway cross‐talk, rather than by individual pathways or model systems in isolation (Yarden & Sliwkowski 2001; Jones et al. 2006). Multivariate analytical and single‐cell–based cytometric approaches are required to understand normal cellular physiology and the potential dysregulation that might lead to malignant disease (Chassagnole et al. 2006). Complex and highly dynamic analysis of intra‐cellular signalling pathways can be performed flow cytometrically by quantifying the activation of pathway components under unaffected and under perturbed conditions using, for example, specific kinase inhibitors disturbing a functional system at different levels. The complementary application of (intra‐cellular) multicolour, high‐content FCM (Edwards et al. 2004) and Bayesian network analysis allows the description of direct and indirect causal interactions of pathway components and therefore the generation of contextual images of networks within the cell, derived from quantitative analysis of thousands of individual cells (Sachs et al. 2005).

Multiparameter FCM is a particularly appropriate source of reliable quantitative data that enables, in combination with computational network analysis, to decipher the importance and regulative impact of pathway components on each other (Valet 2003; Wiley et al. 2003). High‐content, quantitative analysis and network construction can be performed without a priori knowledge of pathway connectivity, which would warrant circumvention of loss of valuable and essential information (Perfetto et al. 2004; Bernas et al. 2006). Appreciation of both biological diversity of cancer and tumour cell heterogeneity is forcing treatment into patterns that reflect the underlying cellular features of the tumour. Principles of therapy may need to be redefined for each distinctive class of cancer, based on comprehensive analysis of molecular systems and vital cells.

Functional proteomics has been applied to comprehensively investigate the capacity of highly related erbB‐receptor tyrosine kinases to interact with intra‐cellular pathway molecules (Uetz & Finley 2005; Abramovitz & Leyland‐Jones 2006; Uetz & Stagljar 2006). erbB receptors are of particular importance in pathological diagnosis of numerous epithelial malignancies (Lottner et al. 2005). Abnormal expression and/or activation of this gene/protein has a severe negative impact on the course of disease in breast, ovarian, head and neck, colon and lung cancers on the one hand, and this is of importance for therapeutic strategy on the other (Richter & Zhang 2005). However, capability of the four erbB‐receptor members (epidermal growth factor receptor, Her2–4) to interact laterally with each other and to trigger intra‐cellular signalling collectively has been incompletely understood and needs a comprehensive investigative approach to be deciphered (Yarden & Sliwkowski 2001). Intra‐cellular phosphotyrosine binding sites have been the subject of unbiased investigation concerning their potential to interact with intra‐cellular signalling molecules and to trigger specific pathways (Schulze et al. 2005; Jones et al. 2006), which result in malignant cell growth and proliferation. Shared and unique features of individual erbB molecules have been identified to be responsible for their specific promiscuity, a crucial feature of their malignant potential. However, the impact of specific and individual co‐expression profile of these receptors on lateral interaction and intra‐cellular signalling can be expediently investigated in living cells (Brockhoff et al. 2001; Diermeier et al. 2005). Fluorescence resonance energy transfer (FRET) is a highly sensitive and quantitative technique to investigate single‐cell–based receptor interaction (Vereb et al. 2004). In combination with dynamic read‐out parameters, for example cell cycle kinetics, the dysregulation of cell proliferation as a crucial marker malignant cell growth, can be accurately and sensitively analysed (Brockhoff et al. 2001; Diermeier et al. 2005). Dynamic multiparameter analysis will contribute significantly to elucidate inter‐relationships of molecular components within molecular systems (for example, the erbB‐receptors) and their regulation, at the cellular level of normal and diseased cells. Multiparametric FCM will contribute significantly to specific patient selection for individualized therapy. A detailed tumour cell analysis, which is available after analytical methods in cytomics, has the potential to increase the ratio of therapy responders to non‐responders and will minimize cases of patient over‐treatment. In addition, a cost‐effective patient treatment with, for examples Trastuzumab™ (Roche Diagnostics, Penzberg, Germany), can be achieved (Elkin et al. 2004). In oncology, the now emerging multiparametric approaches to uncover cells’ protein networks have the potential to determine optimal treatment improving both survival and quality of life for an individual patient (Valet & Hoeffkes 2004). Combining dynamic and functional FCM entails the capacity of establishing predictive medicine in order to identify patients at risk of disease manifestation or of developing complications and side effects to a certain treatment (Tarnok et al. 2006). Utilizing dynamic and functional FCM for novel and highly specific therapeutic targets expected to help identify and optimize patient stratification based on quantitative cell and tissue analysis (Ecker & Steiner 2004; Lottner et al. 2005; Mosch et al. 2006), will follow.

Innovative technologies for systemic cell analysis

Cytomic studies on cascades of information that flow through signalling pathways need complex analysis of individual pathway components. Knowledge of carcinogenesis and tumour progression, and the variety of intra‐cellular physiological processes triggered by anticancer treatments, are continuously extending. Polychromatic and functional FCM as well as slide‐based cytometry (SBC) are key technologies to achieve this goal. Clearly, light microscopic analysis of cells and tissues, in conjunction with imaging – sophisticated image analysis, and data display – are central for automated high‐content single‐cell–based analysis. SBC analysis can combine both single‐cell genomics and proteomics, with structural analysis of cells in their natural environment (for example in whole tissues and/or in cell cultures) (Tarnok & Gerstner 2002; Ecker & Steiner 2004; Gerstner et al. 2004a; Megyeri et al. 2005). Quantitative cytometric analysis of tissue sections can only be performed with the aid of SBC technology. Thus, a multitude of single‐cell features can be captured simultaneously with high‐throughput single‐cell microscopy (Steiner et al. 2000; Schubert 2003, 2006; Bocsi et al. 2004; Ecker et al. 2004; Gerstner et al. 2004a; Kantor et al. 2004; Perlman et al. 2004; Varga et al. 2004; 2005, 2006) and can be reconstituted to single‐cell 3D tissue architecture (called tissomics), to provide data‐rich profiles of cellular heterogeneity in tissues (Ecker & Tarnok 2005; Kriete & Boyce 2005; Kriete et al. 2006; Mosch et al. 2006). Phenotypical data are covariants that can be used as biomarkers to identify relevant candidate genes; molecular events can be linked with phenotypical changes that have initiated them. In this way, adverse outcomes typical of disease classification, even in the absence of usual visual indicators in use for diagnosis via pathology, can be revealed. Controlled perturbations can be introduced to provide prototypical settings to develop methodologies suitable for a broad range of biomedical applications (Kriete & Boyce 2005). High‐throughput FCM (Edwards et al. 2004) or flow and image hybrid systems (George et al. 2004) as well as chip‐based flow systems (Palkova et al. 2004; Wu et al. 2004), cellular genomics (Taylor et al. 2004), cellular proteomics by immunophenotyping (Maynadiéet al. 2002; Casasnovas et al. 2003; Valet et al. 2003; Habib & Finn 2006) or chemical cytometry (Wu et al. 2004; Arkhipov et al. 2005), as well as cellular metabolomics (Dovichi & Hu 2003) constitute further facets of recent extensions in molecular cytometry.

Areas of research and diagnosis, with the demand to measure ‘virtually anything’ in the cell include immunophenotyping (Perfetto et al. 2004; Pugh‐Bernard et al. 2004; Miller 2005; Mittag et al. 2005; Gerstner et al. 2006a), red blood cell analysis (Arndt & Garratty 2004; Greve et al. 2004), rare cell detection (Bocsi et al. 2004), characterization in the case of stem cells (Bou‐Gharios et al. 2004; Camargo et al. 2004; Jovcic et al. 2004; Levering et al. 2004; Lovell & Mathur 2004; Rashid et al. 2004; Rippon & Bishop 2004) and residual tumour cells (2004, 2006; Shen & Price 2006), histopathology and drug discovery (Van Osta et al. 2006; Van Osta 2006). Systemic analysis is also a prerequisite for predictive medicine by genomics, proteomics and cytomics (Valet et al. 2003; Valet & Hoeffkes 2004; Tarnok et al. 2006).

Concepts in systemic tumour cell analysis

System‐wide single‐cell analysis leads to the identification and pinpointing of cell subsets with specific phenotypes and functions (Perfetto et al. 2004; Mittag et al. 2005). In genomic and proteomic profiling, signals from rare cells may be lost in the background (Szaniszlo et al. 2004). Contaminant cancer cells in autologous transplant tissue can cause relapse if their frequency remains unknown. If cells are in suspension, to enrich or deplete specific subtypes is relatively easily satisfied by electrostatic or mechanic cell sorting. Nowadays also, high‐throughput technologies for mechanical or optical cell isolation by SBC are available. LEAP™ technology (laser‐enabled analysis and processing; Cyntellect Inc., San Diego, CA, USA) offers a unique combination of capabilities in cell purification and selective macromolecule delivery (opto‐injection) (Szaniszlo et al. 2006). A method capable of removing all contaminant cells, detected by cytomic analyses with a high probability, is combined laser ablation with automated microscopy (Shen & Price 2006). Thus, complete ablation by automated high‐content image cytometry may become feasible in combination with pre‐therapeutic cytomic analysis of unique cellular expression and/or morphological characteristic patterns (Valet & Hoeffkes 2004; Tarnok et al. 2006).

Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. In the future, however, automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics (Murphy 2005) and toponomics (Schubert 2003) will become available for mapping and for deciphering functional molecular networks of proteins directly in a cell or a tissue. Proteomics, the large‐scale identification and characterization of one or more signalling cascades expressed in a given cell type, has become a major area of cancer research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions (Murphy 2005). Multi‐epitope ligand cartography allows colocalization of a large number of proteins in one sample (morphologically intact routinely fixed cells or tissue) with, at present, analysis of up to 18 different cell proteins in the one specimen (Schubert 2003, 2006) with the potential to colocalize hundreds of molecular components. Topologically organized protein networks, in proteomes in situ, obey unique protein‐colocalization and antico‐localization code. Combining diverse data streams across different levels of biological observation such as molecular, cellular and clinical chemistry responses supports a system‐wide diagnostic approach. Automated, computerized, microscope‐based data extraction and analyses lay the foundation for extracting and correlating measurements, characterizing the functional relationships of spatial localization and protein activation with features of cell migration such as velocity, polarization, protrusion, retraction and mitosis (Debeir et al. 2004; Shen et al. 2006).

Computational approaches to multicellular assemblies have reached a stage where they may contribute to unveiling the processes that underlie organization of tissues and multicellular aggregates (Galle et al. 2006). For quantitative prediction, the control of the biophysical and cell biological parameters on the molecular scale should be known. Here, SBC contributes by permitting tracking the fate of cells and other tissue subunits in time and space, and verifying the organization processes predicted by mathematical models.

Quality control and standardization in systemic analysis

Nowadays, several instruments that have been built around the principle of cytometry are commercially available, with innovative technological developments and software improving the analytical capabilities. Recent expansion in the areas of organic and inorganic fluorescent dyes has greatly facilitated the approach of highly multiplex fluorescence detection for polychromatic (Perfetto et al. 2004; Mittag et al. 2005; Bocsi et al. 2006) and hyperchromatic cytometry (Mittag et al. 2006; Schubert 2006), revealing new cell phenotypes with discrete expression patterns. Spectral imaging simplifies and enriches the extraction of morphological and molecular information (Levenson 2006) with user‐friendly instrumentation (Ecker et al. 2004). The resulting data can be exploited using the spectral content only, or with novel tools, both spectral and spatial information can be combined in the analytical process, using machine‐learning approaches for optimization. The next step is to perform hyperspectral fluorescence imaging for in vivo detection of malignant tissues and cells (2005, 2006; Zavattini et al. 2006) and a combination of these with additional in vivo imaging modalities (Ntziachristos et al. 2005). These technology combinations may further help to unravel processes in tumour development and tumour cell migration in vivo (Wolf et al. 2003; Wolf & Friedl 2006).

Object‐orientated image analysis could provide detailed and reliable analysis of cellular image data (Baatz et al. 2006), and quantitative analysis of polychromatic stained cells in tissue will serve as basis for medical diagnosis and prediction of disease in forthcoming years (Hood et al. 2004; Valet et al. 2004; Lenz et al. 2005). Visualization is a major problem associated with huge inter‐dependent data sets. Therefore, alternative and easy‐to‐handle strategies for data visualization as well as data meta‐evaluation (population analysis, cross‐correlation, co‐expression analysis) are needed (Streit et al. 2006), aimed to facilitate human comprehension of complex data.

To collect the most detailed information from single cells standardization must be enabled for:

  • 1

    quantification of as many individual components of signalling pathways at the single‐cell level;

  • 2

    quantitative spatial analysis of intra‐cellular structures and inter‐relationships of components;

  • 3

    quantification of whole‐cell function properties;

  • 4

    quantification of the inter‐relationships between cells.

In principle, all (optical) manipulation affecting fluorescence emission are contradictory to cytometric analysis as measured fluorescence intensities are modified and do not strictly represent antigen expression levels (Mittag et al. 2006). Although configuration of various instruments appears to be unique, optical principles are very similar, enabling a number of tests for the evaluation and standardization of equipment performance (Gratama et al. 1998; Lerner & Zucker 2004; Zucker 2006). Minimization of sample volume, of necessity in clinical diagnosis and predictive medicine, is facilitated in cytometric techniques (Valet & Hoeffkes 2004; Valet et al. 2004; Lenz et al. 2005; Valet 2005a, 2005b, 2005c) and is applicable for various types of cancer (2003, 2004b, 2005a, 2005b, 2006b; Valet et al. 2003).

CONCLUSION

To apply the aspects of cytometry for cytomic analysis, as standard technology in future new instruments, new reagents are required and new computational solutions must be achieved. Instruments are required that enable automated staining, analysis, and (photo)manipulation. Today, several instruments are under development that can at least partially fulfill this demand (Jager et al. 2003; Schubert 2006). New small detection molecules such as RNA aptamers (Ulrich et al. 2004; Martins et al. 2005) are essential to reduce sterical hindrance in highly multiplex analysis. Finally, there is an urgent need for solutions to combine, analyse and document the tremendous amount of data collected by hyperchromatic analysis (Chen et al. 2006; Schubert 2006; Streit et al. 2006).

The possibility of analysis of genomic and proteomic properties and intra‐cellular distributions of whole‐cell populations, at the single‐cell level in their natural environment, is important if we are to unravel the complexity of cells and cell systems. It opens the way to better understanding of complex processes in health and disease and could be an important tool for predictive and preventative medicine. Cytomics and systems biology can provide information of present tissue status and diagnosis, and consequently promotes the possibility of individualized therapy as a general practice in medicine (Farkas 2003; Hood et al. 2004; Valet 2005a, 2005b). Multiparametric and non‐invasive analysis of living cells by FCM will elucidate crucial regulative mechanisms within molecular systems, an essential prerequisite for diagnostic interpretation of (co)expression profiles and with significant contribution to the identification of specific drug targets. In contrast to lysate based (functional) techniques, multicolour FCM allows more quantitative simultaneous observations of multiple interactions and/or signalling molecules, in many thousands of individual cells. Multiplex analysis of cells in suspension is nowadays limited by hardware (for example, the optical systems of FCM) as a basis for separation and to detect individual discrete (fluorescent) signals; however, it is yet the most powerful tool for analysis of cell function and dynamics. Cytomics by novel systems can thus be conceived as a joint cross‐disciplinary effort for cytomics, systems biology and high‐throughput–orientated research for basic, clinical and industrial scientists (Hood et al. 2004; Tarnok 2004; Valet & Tarnok 2004; Kriete & Boyce 2005; Valet 2005a, 2005b, 2005c).

Future instruments and/or work flow will enable the analysis of cellular contexts at the single‐cell level for the whole organism over high‐throughput single‐cell analysis in tissues (tissomics, toponomics). This can be combined with single‐cell high‐content metabolic investigation for lipidomics (Wenk 2005; Werner et al. 2006) and metabolomics, intra‐cellular location proteomics down to single‐cell proteomics and genomics. Consequently, this will lead to merging of genomics, cytomics and proteomics with systems analysis (Bocharov 2005; Bernas et al. 2006). Combined with data pattern analysis, these approaches will lead to complete characterization of the cytome (Valet & Tarnok 2004; Valet 2005b, 2005c) with the detection of new cell types, cellular functions and inter‐relationships and imminent implications for individualized risk assessment (Valet & Tarnok 2003; Hood et al. 2004; Lenz et al. 2005).

ACKNOWLEDGEMENTS

We thank Dr. Simone Diermeier (Institute of Pathology, University of Regensburg) for her support with this manuscript.

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