Slide-based cytometry for cytomics—A minireview



In the postgenomic era, to gain the most detailed quantitative data from biological specimens has become increasingly important in the emerging new fields of high-content and high-throughput single-cell analysis for systems biology and cytomics. Areas of research and diagnosis with the demand to virtually measure “anything” in the cell include immunophenotyping, rare cell detection and characterization in the case of stem cells and residual tumor cells, tissue analysis, and drug discovery. Systemic analysis is also a prerequisite for predictive medicine by genomics, proteomics, and cytomics.

This issue of Cytometry Part A is dedicated to innovative concepts of system wide single cells analysis and manipulation, new technologies, data analysis and display, and, finally, quality assessment. The manuscripts to these chapters are provided by cutting edge experts in the fields. This overview will briefly highlight the most important aspects of this continuously developing field. © 2006 International Society for Analytical Cytology

Measurement is the heart of scientific world. Ever since Galileo the process of scientific discovery has gone hand-in-hand with the development of methods and techniques of measurement and analysis, influencing decisively the opportunities for scientific progress.

In the postgenomic era, to gain the most detailed quantitative data from biological specimens has become increasingly important in the emerging new fields of high-content and high-throughput single-cell analysis for systems biology (1–4) and cytomics (5, 6). 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 (5). Areas of research and diagnosis with the demand to virtually measure “anything” in the cell include immunophenotyping (7–9), rare cell detection (10) and characterization in the case of stem cells (11–14) and residual tumor cells (15–17), tissue analysis (18–21), and drug discovery (22, 23). Systemic analysis is also a prerequisite for predictive medicine by genomics, proteomics, and cytomics.

To get the most detailed information from single cells, the following requirements need to be fulfilled:

  • 1quantitation of as many cellular constituents on the single cell level as possible,
  • 2quantitative structural analysis of cellular constituents and their interrelations,
  • 3quantitation of cell function properties,
  • 4quantitation of the interrelationship of cells, and
  • 5standardization and quantification protocols.

Obviously, light microscopic analysis of cells and tissues in conjunction with imaging and sophisticated image analysis and data display are the prerequisite for automated high-content single-cell-based analysis. This type of analytical approach is termed slide-based cytometry (SBC). As demonstrated in several of the following manuscripts, it can combine both, single-cell genomics and proteomics, with structural analysis.

The relatively low number of cells measured by SBC as compared with that by flow cytometry (FCM) is out-weighted by specific advantages. For example, stained artifacts and debris can be excluded unequivocally from the analysis by SBC (24): every single event can be visualized by relocation in order to verify results either according to forward scatter and fluorescence images or by direct evaluation through the eyepiece of the microscope. In addition, although FCM instrumentation equipped with a multitude of lasers and applying the plethora of presently available fluorescent dyes for labeling is able to simultaneously measure 17 or probably even more colors (8), it is hampered by its inability to measure the identical cell repeatedly. Furthermore, the analysis of each individual cell's morphology is only possible after time-consuming cell sorting or with specialized imaging FCM (25) where the number of colors measurable in a single run is limited. Finally, FCM systems are in contrast to SBC instruments (19–21) by principle unable to analyze cells in their natural environment (e.g., tissues, cell cultures).

This issue of Cytometry is dedicated to innovative concepts of system wide single cells analysis and manipulation, new technologies, data analysis and display, and, finally, quality assessment. The manuscripts to these chapters are provided by cutting edge experts in the fields. I will briefly highlight the most important aspects in the following manuscript in order to motivate for further reading.


Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological 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 (4). Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics research aimed at creating systems for automated, systematic determination of location is reviewed by Chen et al. (26). The methods reported employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, such as hyperchromatic cytometry (27) or toponomics (28–30), the computational methods reviewed enable the new subfield of location proteomics (26, 31). The authors conclude that this subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on upwards. Second, it will provide tools for cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

A prerequisite for location proteomics are assays that can analyze up to hundreds of individual proteins on the single cell level for mapping and deciphering the functional molecular networks of proteins directly in a cell or a tissue. Therefore, technologies for the colocalization of random numbers of different molecular components (e.g., proteins) in one sample in one experiment are required. Schubert (28) developed the Multi-Epitope-Ligand-“Kartographie” (MELK) approach as a microscopic imaging technology running cycles of iterative fluorescence tagging, imaging, and bleaching, to colocalize a large number of proteins in one sample (morphologically intact routinely fixed cells or tissue). In the presented study (30), 18 different cell surface proteins were colocalized by MELK in cells and tissue sections from different compartments of the human immune system. From the resulting sets of multidimensional binary vectors, the most prominent groups of protein–epitope arrangements were extracted and imaged as protein “toponome” maps. These maps provide direct insight in the higher order of the topological organization of immune compartments uncovering new tissue domains. The data sets suggest that protein networks, topologically organized in proteomes in situ, obey a unique protein-colocalization and -anticolocalization code describable by three symbols. The technology presented has the potential to colocalize hundreds of proteins and other molecular components in one sample and may offer many applications in biology and medicine.

Combining diverse data streams across different levels of biological observation, such as molecular, cellular and clinical chemistry responses, support a system-wide diagnostic approach. Recent progress in SBC contributes to the development of Tissomics, a high-throughput and high-content phenotyping methodology providing data-rich profiles of cellular heterogeneity in tissues enabling correlative statistical treatments over multiple scales of biological hierarchies (32–34). Phenotypical data are covariants that can be used as biomarkers to identify relevant candidate genes by associating initiating molecular events with phenotypical changes and adverse outcomes. In their paper Kriete et al. (34) introduce a procedure of combined statistical and analytical tools to identify and visualize such associations for nonpooled entities. The new utility is applied to a time-controlled, low-dose toxicological study including a control and two xenobiotic compounds. The approach demonstrates the value of an integrated systems biology analysis in identifying specific molecular and phenotypical biomarkers, which supports the classification in the absence of any visual indicators from pathology readings. The introduction of controlled perturbations provides a prototypical setting to develop a methodology suitable for a broader range of biomedical applications (35).

Advances in living cellular fluorescence biosensors and computerized microscopy enable a vision of fully automated high-resolution measurements of the detailed intracellular molecular dynamics directly linked to cellular behaviors. Given the heterogeneity of cell populations, a statistically relevant study of molecular–cellular dynamics is a key motivation for improved automation.

Shen et al. (36) explored automating computerized, microscope-based data extraction and analyses that monitors in mouse fibroblasts cell locomotion, rates of mitoses, and spatiotemporal activities of intracellular proteins via ratiometric fluorescent biosensors. Novel image processing methods include: K-means clustering segmentation preprocessing followed by modified discrete, normalized cross-correlational alignment of 2-color images; ratiometric processing for fluorescence resonance energy transfer (FRET) measurements (37); and intracellular spatial distribution measurements of RhoA GTPase activity. These advances 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.


The optimal tool for high-content cell and tissue analysis are microscope-based or SBC instruments. Nowadays, several instruments that are build around the principle of performing cytometry on the slide are commercially available with innovative technological developments in instruments and software improving the analytical capabilities.

The application of SBC technology also in a resource poor environment would greatly facilitate its day-to-day use. Low cost instrument upgrades have been proposed earlier (10, 38). Shapiro and Perlmutter (39) discuss the issue how a cheap SBC instrument could be built for slide-based analysis. Although some manufacturers have optimistically described instruments with prices in the US $40,000 range as “personal cytometers,” analogy with the personal computer suggests that the target price for a true “personal” cytometer should be under $5,000. Since such an apparatus could find a wide range of applications in cytomics in both developing and developed countries, the authors found it desirable to consider its technical and economic feasibility. Using resolution targets and a variety of fluorescent bead standards immobilized on filters and/or slides, high-intensity LEDs as fluorescence excitation sources, relatively inexpensive CCD cameras as detectors, and plastic low-power microscope optics for light collection in a simple, inexpensive low-resolution imaging cytometer were evaluated. The components tested could be combined to produce an instrument capable of detecting fewer than 10,000 molecules of cell-associated fluorescent label, and thus applicable to a broad range of cytometric tasks. The authors conclude that given the requirements for light sources, detectors, optics, mechanics, electronics, and data analysis hardware and software, and the components presently available, it should be easier to reach the desired $5,000 price point with an image cytometer than with a FCM.

Levenson (40) discusses the advantage of spectral imaging for Cytomics by SBC. Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is a tool that simplifies and enriches the extraction of morphological and molecular information. Simple-to-use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution (37). The resulting data can be exploited using just the spectral content, or, with novel tools, the spectral and spatial information can be combined in the analytical process, using machine-learning approaches for optimization. In addition, spectral imaging allows the use of convenient immunohistochemical approaches for moderate degrees of molecular multiplexing in brightfield; in fluorescence, even higher multiplicities are possible. Thus, spectral imaging can be used in support of cytomics by combining morphology and molecular phenotyping in robust, reproducible, and automatable implementations.

The development of fluorescent dyes of organic and inorganic origin in the recent years greatly facilitated the approach of highly multiplexed fluorescence detection. Fluorochromes emitting in the near infrared, such as FRET dyes (PE-Cy7, APC-Cy7) (41) as well as fluorescent nanoparticles or quantum dots (8, 42), offer the opportunity to increase the number of simultaneously measurable fluorochromes. These developments initially led to polychromatic flow cytometry (FCM) (8) or polychromatic laser scanning cytometry (LSC) (7), revealing new cell phenotypes with discrete antigen expression or cytokine production patterns.

Mittag et al. (27) demonstrate that by the combination of different technologies and staining methods, multicolor analysis can be pushed forward to optically dissect and measure a cell by intrinsic and extrinsic staining. They termed this approach hyperchromatic cytometry and present different components suitable for achieving this task. Hyperchromatic cytometry takes advantage of the SBC feature that analyzed samples are fixed on the slide and can be reanalyzed after manipulation.

The authors demonstrate various approaches for hyperchromatic analysis by LCS. The different technological components demonstrated include: (1) polychromatic cytometry (7), (2) iterative restaining (30, 43), (3) differential photobleaching (44), (4) photoactivation, and (5) photodestruction. Based on the ability to relocate cells that are immobilized on a microscope slide with a precision of ∼1 μm, identical cells can be reanalyzed on the single-cell level after manipulation steps. The authors conclude that with the intelligent combination of different techniques hyperchromatic cytometry approach allows to quantify and analyze all components of relevance on the single-cell level.


The precise storage of the location of each measured event on the slide allows reanalysis after modifications of the specimen, e.g., restaining or changes of instrument settings. Both analyses, e.g., before and after restaining, can then be fused to one data file (a feature of the LSC software called “merging”). Examples are: restaining cell nuclei after immunophenotyping (45), combining alive and dead information (46), destaining kinetics (47), and restaining surface antigens with second and third sets of antibodies (43). A specific feature of the LSC that cannot be covered by FCM is the quantitative analysis of tissue sections. Steiner et al. (18, 20, 48) demonstrated that immunophenotyping of leukocytes in tissue sections is possible with confocal microscopy. Gerstner et al. (20) showed three- and four-color immunophenotyping of tonsil sections; Lenz et al. (49) established quantitative two- and three-dimensional analysis of the distribution of nuclei and neurons in brain tissue sections by LSC. Recently, approaches have been proposed toward the concept of 3D-cytometry of tissue sections and in tissue cultures termed Tissomics (32, 33).

From the perspective of the industry, extracting quantitative information from tissue is of eminent value. Van Osta et al. (23) provides an overview of current technologies for the pharmaceutical and biotech industry. Disease processes express themselves in the functional and structural disturbance of cellular systems. As cells and their metabolites constitute the building blocks of tissues studying the spatial and temporal phenotype of disease processes in tissues at the cellular level reveals a multitude of information about the progress and status of a disease. Detailed exploration of tissues by SBC therefore is an important source of information about disease processes. Technological and analytical advances allow to shed new light on tissues and to come to a better understanding of the complexity of disease processes (22). Dealing with complex multidimensional datasets from tissue samples requires an advanced approach to image processing and data management. The increase in computing power and the continuing research into imaging algorithms allow us to improve the exploration of the data content of tissues.

Computational approaches of multicellular assemblies have reached a stage where they may contribute to unveil the processes that underlie the organization of tissues and multicellular aggregates. Galle et al. (50) review and present new results on a number of 3D lattice free individual cell-based mathematical models of epithelial cell populations. The models considered are parameterized by biophysical and cell–biological quantities on the level of individual cells. Eventually, they aim at predicting the dynamics of the biological processes on the tissue level. The review focuses on a number of systems, the growth of cell population in-vitro and the spatial–temporal organization of regenerative tissues. For selected examples, different model approaches are compared showing that qualitative results are robust with respect to many model details. Hence, for the qualitative features and largely for the quantitative features many model details do not matter as long as characteristic biological features and mechanisms are correctly represented. For a quantitative prediction, the control of the biophysical and cell–biological parameters on the molecular scale has to be known. At this point, SBC contributes by permitting to track the fate of cells and other tissue subunits in time and space and to verify the organization processes predicted by the mathematical models.

Presentation of multiple interactions is of vital importance in cytomics. Quantitative analysis of polychromatic-stained cells in tissue will serve as basis for medical diagnosis and prediction of disease in forthcoming years (1, 51, 52). In this context, visualization is a major problem associated with huge interdependent data sets. As an example, a 17 color immunofluorescence protocol requires 136 scattergrams for showing cross-correlation between all markers. Therefore, alternative and easy-to-handle strategies for data visualization as well as data metaevaluation (population analysis, cross-correlation, coexpression analysis) were developed.

Streit et al. (53) present a 3D parallel coordinate system aimed to facilitate human comprehension of complex data, generated by automated microscopy-based multicolor tissue cytometry (MMTC). Frozen sections of human skin were stained using the combination anti-CD45PE, anti-CD14APC, and SytoxGreen as well as the appropriate single and double negative controls. Stained sections were analyzed using automated confocal laser microscopy and semiquantitative MMTC-analysis. Current techniques in data visualization primarily utilize scattergrams, where two parameters are plotted against each other on linear or logarithmic scales. However, data evaluation on cartesian x/y-scattergrams is in general only of limited value in multiparameter analysis. Dot plots suffer from serious problems and in particular do not meet the requirements of polychromatic high-context tissue cytometry of millions of cells. The authors generated 2D and 3D parallel coordinate plots from semiquantitative immunofluorescent data of single cells. These 3D parallel coordinates plot replaces the vast amount of scattergrams, which are usually needed for the cross-correlation analysis enabling the investigator to perform the data metaevaluation by using one single plot. On the basis of two-dimensional parallel coordinate systems, a density isosurface is created for representing the event population in an intuitive way.

The authors show that 3D parallel coordinate plots open new possibilities to represent and explore multidimensional data in the perspective of cytomics and other life sciences, e.g., DNA chip array technology (53). The improved data visualization method allows the observation of such complex patterns in only one 3D plot and could take advantage of the latest developments in 3D imaging.

Detailed image analysis is still a considerable bottleneck for many cellular assays. Baatz et al. (54) present an object-oriented image analysis based approach as a flexible system for detailed and reliable analysis of cellular image data. This approach was specifically designed to meet the need for detailed image analysis in cellular high throughput assays. The system contains a library of predefined, adaptable modules for specific analysis tasks. By selecting appropriate modules and adapting those to the actual image data, the system can be configured freely and easily, with calibration through buttons, sliders, and interactive user input in the image domain.

By representing cells and subcellular structures within a network of interlinked image objects, multiparameter analysis and multiplexing are supported by a large number of parameters that describe shape, intensity, and relevant structural and relational aspects of any chosen class of structures. The authors demonstrate this approach by the creation of a solution that differentiates GFP expression based on its characteristics and relative location within the cell.


System wide single-cell analysis in combination with appropriate image analysis leads to the identification and pinpointing of cell subsets with specific phenotypes and functions (7, 8). In genomic and proteomic profiling, signals from rare cells may be lost in the background (17). This immediately leads to the desire to enrich for these specific subtypes. If cells are in suspension, this demand is relatively easily satisfied using electrostatic or mechanic cell sorting. In SBC, by contrast, mechanical or optical cell isolation techniques are of relatively low speed. In two articles of this issue, optical cell enrichment and manipulation techniques suitable for high-throughput are introduced (15, 16).

Contaminant cancer cells in autologous transplant tissue can cause relapse; their rate is unknown. A method capable of removing all contaminant cells with a high probability detected by cytomic analyses would be useful. Neither 100% cell purging nor techniques for measuring the probability of success have been developed. Shen and Price (16) report a method for removing 100% of malignant cells under ideal staining conditions and quantify the probability of success. The authors combined laser ablation with automated microscopy to purge contaminant cells and evaluate 100% ablation in a coculture model of prestained mouse melanoma cells mixed with mouse NIH-3T3 cells. Melanoma passage efficiency was measured by micropipetting single cells into microtiter wells as well as ablating all but one melanoma cell in cocultures.

With ablation of all but one contaminant cell in cocultures, melanoma dominated in 62% cultures in 21 days. With 100% ablation no melanoma outgrowth was observed, giving a >99.1% probability that all contaminant melanoma cells were purged. The authors successfully demonstrate complete ablation by automated high-content image cytometry. Their results show that the instrumentation is capable of delivering 100% ablation at a defined probability and establishes the basis for further studies with clinical models; there, pretherapeutic cytomic analysis of unique cellular expression and/or morphological characteristics will be the key for contaminant cancer cell identification (55, 56).

SBC now has many of the features (and the power) of multiparameter FCM while keeping its own advantages as an imaging technology. Modern instruments combine capabilities of SBC with the ability to manipulate cells. Szaniszlo et al. (15) introduce a new technology, called LEAP™ (Laser Enabled Analysis and Processing), offering a unique combination of capabilities in cell purification and selective macromolecule delivery (optoinjection). In model experiments, LEAP-mediated cell purification and optoinjection effects were assessed using adherent and suspension cell types and cell mixtures plated and processed at different densities. Optoinjection effects were visualized by delivering fluorescent dextrans into cells. Live cell samples (adherent and suspension) could be purified to 90–100% purity with 50–90% yield, causing minimal cell damage depending on the cell type and plating density. 100% of the targeted cells of all cell types examined could be successfully optoinjected with dextrans of 3–70 kDa, causing no visual damage to the cells. Indirect optoinjection effects were observed on untargeted cells within 5–60 μm to targeted areas. The authors conclude that LEAP provides solutions in cell purification and targeted macromolecule delivery for traditional and challenging applications where other methods fall short.


By cytometric analysis stoichiometric information of cell constituents based on the fluorescence intensity of labeled reporter molecules is collected. Special attention needs to be taken for calibration and standardization. By principle, all (optical) manipulations that affect the fluorescence emission are contradictory to cytometric analysis because the measured fluorescence intensities will be modified and will not strictly represent antigen expression levels. When samples are iteratively restained, those antigens that are stained in later steps may artificially present reduced fluorescence intensity (43). This is either due to loss of antigen availability, photodamaging of the antigen, or other reasons (27). Therefore, it is recommended to use the fluorescence intensity signals before further manipulation in order to quantify expression levels. The signals detected after manipulation serve as phenotypic markers in order to pin-point to which subtype the cells belong.

With some additional effort, however, also these signals may be standardized in order to yield cytometric data. To this end, one can apply control cells of known antigen expression that are treated identically to the unknown specimen (57). Alternatively, all antigen expressions are tested in a permutating order within the flow of the hyperchromatic staining and changes are precisely recorded (i.e., before manipulation vs. after manipulation). All preparation and manipulation steps are strictly standardized and reproducible, e.g., by automating sample preparation and analysis.

Two manuscripts of Zucker (58, 59) highlight different key aspects of quality assessment of SBC systems, concluding earlier work of the author (60). All SBC detections systems basically include: (1) an excitation light source, which may be laser, xenon, or mercury arc; (2) intermediate optics, which may include an acousto optical tunable filter, dichroics, barrier filters, and lenses; and (3) a detection device, which may be a CCD camera, or a PMT (photomultiplier). Although the configuration of the system appears to be unique, the optical principles are very similar for all of the optical equipment that is currently used in slide-based analysis. For example, all of the equipment must be adjusted to produce a proper uniform field illumination with proper spectral registration. Since it is not possible to address all the manufacturers and models of slide-based systems, in both manuscripts a point scanning confocal microscope (CLSM) has been used as an example to illustrate the principles applicable to SBC.

A number of tests have been devised to evaluate equipment performance (tests for measuring dichroic reflectivity, field illumination, lens performance, laser power output, spectral registration, axial resolution, laser stability, PMT reliability, spectrometer reproducibility, and system noise). These tests were either incorporated from those described in the literature or were developed in our laboratory to measure performance. The quality assessment (QA) tests on the CLSM measured laser power, PMTs function, dichroic reflection, spectral registration, axial registration, system noise and sensitivity, lens performance, and laser stability. The values can be used as a comparison to test machine sensitivity from the same or different manufactures. Although resolution is essential for all fields of microscopy, many slide-based systems may sacrifice resolution to optimize other variables in the detection. However, the aim of this equipment is to achieve higher signal-to-noise ratios, which will in turn yield a sharper image. The author strongly suggests tests that are applicable to slide-based systems with minor modifications and should be used as a starting point in developing specific tests for specific equipment.

Standardization and QA are but also minimization of sample volume of particular necessity in clinical diagnosis and predictive medicine by Cytomics and systems biology (51, 52, 55, 61, 62). Earlier work shows that using minimal invasive sampling predictive medicine is feasible for different cancer entities (63–66). Gerstner et al. (67) demonstrate on the example of predicting upper aerodigestive tract cancer by SBC using swabs for a minimal-invasive approach. LSC was used for multiparametric analysis of cells stained for cytokeratin and DNA to determine the DNA-index (DI) of the tumor cells. After subsequent HE-staining, single cells were relocalized for morphology documentation. Conventional cytology was also performed on a subset of the slides. Routine histopathology of parallel biopsies served as gold standard. In 99 analyzed specimens, 1 benign lesion was misclassified as malignant, while 61 of the 75 malignant lesions were correctly identified; i.e., the predictive values were 90.0% and 62.1% for the detection of malignant and benign samples by LSC, respectively. It is demonstrated in this pilot study the validity of LSC screening for the identification of tumor malignancy in the upper aerodigestive tract from swab collected cytological material.


To use several or even all aspects of SBC for Cytomics analysis as standard technology in future new instruments are required and new computational solutions have to be achieved. Instruments have to be developed that enable to automatize staining, analysis, and photomanipulation. Presently, several slide-based instruments are under development that can at least partially fulfill this demand (30, 68). New fluorochromes that have very specific features (e.g., photoconversion at very specific wavelengths) need to be developed to more specifically modify fluorescences. New detection molecules smaller in size such as RNA aptamers (69) are essential to reduce sterical hindrance. Finally, solutions for combining, analyzing, and documenting the tremendous amount of data collected by hyperchromatic analysis have to be developed [examples in Refs.26, 30, 53].

The possibility to analyze genomic and proteomic properties and intracellular distributions of whole cell populations on the single-cell level in their natural environment is important to unravel the complexity of cells and cell systems. It opens the way to better understand complex processes in health and disease and could be an important tool for predictive and preventative medicine. Cytomics and systems biology can show information of the present status and diagnosis, and consequently allow an individualized therapy as a general practice of medicine (1, 61, 62, 70). With the methods introduced in this issue, novel cell types will be recognized not detectable with present methods. In theory, the amount of detectable cell characteristics is only limited by sterical hindrance. Cytomics by SBC systems can thus be conceived as a joint cross-disciplinary effort for cytomics, systems biology, and high-throughput-oriented research for basic, clinical, and industry scientists (1, 5, 33, 62, 61, 71, 72).

Finally, Figure 1 (based on a model proposed earlier (71)) tries to summarize how a future instrument and/or work-flow will look like that will enable to analyze the cellular context on the single-cell level from the whole organism over high-throughput single-cell analysis in tissues (tissomics, toponomics) combined with single cell high-content metabolic investigation (lipidomics (73, 74), metabolomics) (75), intracellular pattern recognition (location proteomics) down to single-cell proteomics and genomics. Combined with data pattern analysis these approaches will lead to the complete characterization of the cytome (52, 61, 62, 72) with the detection of new cell types, cellular functions and interrelationships and imminent implications for individualized risk assessment in the clinical environment (1, 51, 76).

Figure 1.

Idealized slide-based cytometer for cytomics. The scheme summarizes the workflow for the complete high context dissection of biological specimens such as tissues. The analysis starts on the top with low resolution high-content analysis (e.g., by whole animal single cell imaging) and ends in the single-cell proteomic and genomic dissection. The different components of cytomics SBC and how these components can be combined in a work flow. The left column shows the different technologies and instrumentation that come to application at the different steps of the work (flow). These detection modalities may either be combined in a single instrument or are combined in a work-flow scheme. The right column shows the different Omics appearing in this article and in articles of this focus issue and their relation.


The author thanks Dipl.-Ing. Anja Mittag (University Leipzig, Germany), Dr. Andreas O.H. Gerstner (University Bonn, Germany), and Dr. Jozsef Bocsi (University Leipzig, Germany) for their support with this manuscript.