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Keywords:

  • cytomics;
  • tissue cytometry;
  • multicolor staining;
  • quantitative histology;
  • pattern recognition

Abstract

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED

Cytomics is a novel perspective from which to look at life. As with genomics and proteomics before, this discipline requires novel and innovative techniques and technologies to focus on its substrate of research—the cytome. With cytomics being the discipline that analyzes cellular systems and their interdependencies, advanced microscopy represents a key technology in cytomics research. Yet, conventional microscopy-based investigations, i.e., “look and conclude” analyses, do not meet the major cytomics criteria of 1) relating multiple parameters to each other, 2) within large populations of cells, 3) on a single-cell basis, and 4) in a quantitative and observer-independent manner. However, emerging improvements in the fields of fluorophore technology, sensitive fluorescence detection devices, and sophisticated image analysis procedures, are important and necessary steps into the cytomics era. Tissue represents an important class of cytomes, hence tissue cytometry—on the single cell level—can be expected to become an important cytomics technology. In this report, the techniques and technologies of microscopy-based multicolor tissue cytometry (MMTC) are outlined and applications are discussed, including the phenotypic characterization of tissue infiltrating leukocytes, in situ quantification of proliferation markers and tumor suppressors, and in situ quantification of apoptosis. © 2004 Wiley-Liss, Inc.

With the advent of cytomics, an increasing amount of cellular parameters and molecular markers, as well as their interrelations (1, 2), have to be determined on a single-cell level and in a quantitative manner. This means a substantial challenge to microscopy. However, the vast majority of microscopy-based investigations are currently evaluated visually, in a ‘look and conclude’ manner. Although important gross effects can be observed by a human observer, complex interdependencies among and between different cellular events are often beyond our observation capabilities and remain undiscovered. In cytomics approaches, however, techniques are required that permit quantitative analysis of multiple markers and of an unlimited number of single cells simultaneously, instead of just visual evaluation.

To date, several techniques of slide-based cytometry have been introduced and have been applied to biomedical research (3–11). Moreover, first clinical applications have been addressed and have yielded promising results (12–17). Advanced instruments for slide-based cytometry typically offer: 1) recognition of single objects by utilizing fluorescence of antigenic markers or DNA dyes; 2) fluorimetric determination of the mean relative intensities of all recognized objects in all channels (colors), whereby the localization of analyzed cells is known; 3) computation of the number of positive objects depending on their fluorescence staining pattern, size, and shape; and 4) definition of cellular subpopulations and statistical evaluation. Available instruments comprise laser scanners as well as charge-coupled device (CCD)-based imaging.

Although current techniques can be applied to tissue sections and cell monolayers, automated and quantitative analysis of tissue sections (i.e., tissue cytometry) is an extremely difficult task. The reason for this difficulty relates to a major problem in medical image analysis that is both old and current: pattern recognition (8, 18–20). This is particularly true in cytomics, in which tissue cytometry on a single-cell level is required. Identification of individual cells in tissue context is a challenging task, because tissues are complex structures composed of a variety of changing attributes. On the one hand, there is a high redundancy with many (optically distinguishable) features being common to most or even all types of cells, and, on the other hand, there is a high variability in particular structures characteristic of certain cell types, and there are many exceptions to the rule. Additionally, individual tissue cells stay in close contact, making it difficult, not only some of the time, but most of the time, to separate them optically and to distinguish between neighboring cells. Hence, a first and major challenge in tissue cytometry in terms of a cytomics technique is reliable recognition of individual cells in tissue context.

In order to address this challenge, our group previously introduced a method for slide-based cytometry utilizing a commercial confocal laser scanning microscope LSM 510/META (Zeiss, Jena, Germany) and advanced image analysis (21). The motorized microscope Axiovert 200M (Zeiss) has been fully automated. Only the scanning area and the instrument settings (laser, filters, channel assignment) have to be defined by the user. Once the data acquisition procedure has been started, the entire area of interest is scanned without the need for a user being present. The control software performs an autofocus and systematic data storage for each field of view (i.e., image) scanned. After data acquisition has been completed, the analysis procedure starts, using predefined search strategies (e.g., for leukocytes) and protocols (i.e., parameters for the selected search strategy). Identification strategies continue to be developed for automated recognition of individual cells using multiple antigenic markers and/or DNA dyes and advanced pattern-recognition algorithms. These algorithms can be used in microscopy-based multicolor tissue cytometry (MMTC) for various purposes, e.g., subclassification of tissue infiltrating leukocytes, measurement of tumor marker (co-)expression, and quantification of apoptosis in defined cell types in situ—all at the single-cell level.

In this report, we will present an overview of the current state-of-the-art, outline recent applications, and discuss some future perspectives.

TECHNIQUES

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED

Single-Cell Recognition by Multicolor Immunofluorescence

The core function for immunofluorescence-based single-cell recognition is gray value-, size-, and shape-restricted object reconstruction. The cellular reconstruction algorithm requires a lower and upper gray-value threshold, defining all values between these thresholds as valid. If the gray-value of a given pixel is valid, it is included in a binary object that includes all surrounding pixels that 1) “touch” each other, and 2) also own a valid gray value. The reconstruction process is interrupted either when no further pixel with a valid gray value is available or when the image border is reached. Thus, the reconstruction of cellular objects is self-limiting.

As an example of this procedure, we used anti-CD45 for staining of renal-tissue infiltrating leukocytes, revealing a clustered staining pattern, as indicated in Figure 1a. Identification using a standard identification strategy (SIS) (Fig. 1b) consisting of a segmentation procedure with two gray-value thresholds and no further parameters, could not appropriately identify clustered cells. In contrast, the MMTC-based lymphocyte reconstruction algorithm significantly increased the recognition accuracy (Fig. 1c). Mask objects that were obtained by these steps of image processing were referred to as “events” and served as master channels for fluorimetric analysis.

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Figure 1. Antibody-based single cell identification. Two examples of original gray-value images, depicted in different look-up-tables (i.e., colors), showing CD45+ graft-rejecting leukocytes (a,d). Identified objects are depicted in white (binary mask). Gray value–restricted object segmentation by one-parameter segmentation (b,e) meets its limitation when clustered cells are to be analyzed. The advanced MMTC algorithm significantly increases the recognition rate of single cells (c,f). Note the significantly increasing number of recognized single cells (compare c versus b) in the clustered areas of image a. Besides the recognition rate, the identification accuracy obtained by MMTC is increased compared to a standard segmentation procedure and even faint cellular structures are recognized (compare arrows in f versus e).

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Moreover, not only the recognition rate was improved by the advanced reconstruction strategy compared to SIS, but also the structural accuracy of single-cell recognition. For discrete cells in tissue context, a very good shape reconstruction and morphological correspondence could be obtained (Fig. 1d–f) and objects identified matched the cells depicted in the original gray value images (compare arrows in Fig. 1e and f).

This technique has been successfully applied in studying the ontogeny of Langerhans cells in mice by quantifying the in situ expression of cytokines in these cells (Chang-Rodriguez et al., submitted). This algorithm is appropriate for analysis of tissue-infiltrating leukocytes and leukocyte-like cells, but not for dense cell aggregates in which many cells touch each other. In order to analyze dense aggregates, staining of nuclei is inevitable.

Single-Cell Recognition by DNA Staining

A separate algorithm is used for recognition of nuclei using thresholds for fluorescence intensity, object size, and object shape. These thresholds define which pixel of the original image (Fig. 2a) belong to individual nuclei making up the binary mask. In cases in which the DNA-staining is not homogeneous due to a staining artifact, the circle-approximated contour of binary objects can be extracted. Subsequent filling of contours constitutes an optimization procedure for shape adaptation of single nuclei. The final binary mask contains shape-adapted single nuclei only (Fig. 2b). The DNA-based single-cell identification is appropriate to measure the immunofluorescence reactivity of antigens that are expressed either intranuclearly or perinuclearly. Moreover, in leukocytes with a small ring-shaped cytoplasm, this algorithm is also appropriate to measure protein expression in the cytoplasm and on the surface membrane. To measure the expression of cytoplasmic and/or surface antigens of leukocytes, characterized by a relatively thin cytoplasm-ring, the objects (nuclei) are first dilated so that objects do not touch each other. However, whatever the user defines, the dilation process stops when the border of a neighboring cell is reached, constituting a mechanism of basic quality assurance. The original binary mask-image is subsequently subtracted from this dilated mask, resulting in binary ring objects that represent the cytoplasm and surface membrane areas of single cells, indicated by a white ring around each nucleus (Fig. 2c).

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Figure 2. DNA-staining–based single-cell identification. The original gray-value image (a), depicted in red, is processed by the MMTC algorithm for recognition of nuclei. The resulting binary mask for ethidium bromide–labeled nuclei is depicted in white. The final mask (b) contains 141 single objects. Three experts independently determined the number of nuclei, with counts of 146, 148, and 151, respectively, with a mean of 148.33 nuclei. Not included in both visual and manual counting are nuclei that touch the image border (frame in b). In some cases, neighboring nuclei optically form (or are indeed) doublets, which cannot be discriminated into single objects by the automatic recognition process and are therefore removed due to the shape criteria (arrows in b). When identified objects are first dilated and subsequently eroded, by an algorithm using an octagonal matrix with one iteration of the process, and the eroded objects are subsequently subtracted from the dilated ones, a small ring remains, which defines the perinuclear area, and which, in the case of leukocytes, reproduces the leukocytic cytoplasm in good approximation (c).

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This method is appropriate to establish a tissue-based immune status in situ by measuring the amount of leukocytes and leukocytic subpopulations with respect to their spatial distribution and tissue morphology. Rejected allografts are especially difficult to analyze, as they usually exhibit an extraordinary strong infiltration rate. Nevertheless, clear results could be obtained for renal graft rejecting leukocytes (Fig. 3). In this approach, the DNA staining is only utilized to determine the localization of individual cells and not to quantify the DNA with respect to determining proliferation. For the latter task, it is not sufficient to use slices as are obtained by both sectioning and confocal imaging. Quantification of proliferation based on DNA content requires stoichiometric measurements in volumes (i.e., entire nuclei), which is difficult to achieve in tissue sections, regardless of considerations concerning staining technique, 3D volume rendering, and analysis standardization.

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Figure 3. Cytometrical determination of CD45+ leukocytes infiltrating rejected renal allograft tissue. Rejected renal allograft tissue stained for anti-CD45-FITC and DNA (using propidium iodide) exhibits a strong leukocytic infiltration. MMTC analysis reveals 1,440 CD45+ leukocytes per mm2 of tissue area (a). The specificity of anti-CD45 reactivity is determined by IgG1 isotype-control staining, which shows less than 1% of reactivity beyond the cutoff for CD45 (b). Data on DNA intensity are represented on a linear scale, while data on mean relative intensity of CD45 are represented on a logarithmic scale over four decades.

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Furthermore, this algorithm for quantification of markers expressed intranuclearly and/or perinuclearly has been successfully applied to quantify gelatinolytic activity, using a rabbit atherosclerotic femoral artery injury model, in an approach that investigated which mechanisms of the inhibition of coagulation at an early stage might reduce restenosis through overexpression of tissue factor pathway inhibitor (22). It is also appropriate to demonstrate intranuclear translocation of transcription factors upon external stimuli in functional experiments using cell cultures (R. Ecker and T. Thalhammer, unpublished data).

Moreover, by use of spectral imaging microscopy (23) and combination of several cell type–specific markers, the cellular composition of tissue samples with respect to the presence and amount of certain cells could be addressed in clinical approaches. This kind of “tissue-status” could prove extremely helpful in autoimmune disease-diagnosis, transplantation monitoring using fine needle aspirates, and cancer research.

Accuracy and Reproducibility of MMTC

To test the accuracy of MMTC and to demonstrate recent improvements, we used renal cell carcinoma tissue (RCC, n = 8) and tissues of rejected renal allografts (Tx, n = 8) stained for anti-CD8-FITC, anti-CD3-PE, and anti-CD45-PC5, as well as for anti-CD45-FITC and DNA, with the DNA stained with propidium iodide using a modified protocol according to Hedley et al. (24). The identification rate achieved by standard and advanced image processing procedures was compared with visually counted numbers of leukocytes per mm2 (Fig. 4). From each specimen, 30 images (enclosing a total area of 1.2384 mm2 per specimen), spread over the entire area of the section, were analyzed. All of the Tx specimens contained clustered leukocytes in most of the images recorded, and were therefore of special interest for evaluation of the actual significance of the advanced identification strategy. For each of the 16 samples analyzed, the number of visually-identifiable cells was determined independently by three experts, with differing results. The mean value of all three experts was taken as 100% for a particular specimen. Using a one-parameter segmentation with optimized settings, a mean of 44.3% of CD45+ tissue-infiltrating leukocytes was recognized. Applying the advanced MMTC algorithms to the same images, an increase of more than 50% with respect to single-cell identification could be achieved with the immunofluorescence-based identification strategy. A maximum recognition accuracy was obtained with the DNA-based identification of individual leukocytes, for which the algorithm recognized a mean of 95.2% of visually-identifiable cells. The data shown in Figure 4 exhibit a high variability, which is due to the differences in cell numbers of TX and RCC specimens. However, although TX specimens tend to exhibit stronger infiltration by leukocytes compared to RCC, this difference was not significant for the specimens analyzed. Even within the two groups of TX and RCC, the specimen exhibited substantial heterogeneity. TX and RCC specimens were therefore taken together in order to compare the recognition accuracy of different analysis strategies.

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Figure 4. Accuracy of visual versus automated single cell recognition. The number of CD45+ leukocytes in tissue specimens of renal cell carcinoma (n = 8) and rejected renal allograft (n = 8), which were strongly infiltrated by graft rejecting leukocytes, was determined by human observers and computer-aided analysis, using three different techniques. The error bars indicate different leukocyte infiltration rates measured in different patients, representing the biological variability of tissue.

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When DNA-labeling was used for cell identification, the algorithms ensured that only single nuclei were analyzed and doublets were not taken into account (arrows in Fig. 2b). To avoid measurement errors, objects at the image margin were not analyzed when a single cell identification strategy was used (frame in Fig. 2b).

APPLICATIONS

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED

Phenotypic Characterization of Tissue-Infiltrating Leukocytes

Detailed knowledge of the phenotypic composition of tissue-infiltrating leukocytes is of major importance in many types of disease. In allergy, as well as tumor and transplantation immunology, leukocytes are key players and widely determine a patient's fate. Hence, techniques that allow characterization and quantification of cell types involved in a particular immune reaction are of highest interest in the clinical routine, which has been demonstrated by the success of flow cytometry over the last two decades. Figure 5 shows a typical example of a four-color analysis of tissue infiltrating leukocytes. A combination of anti-CD45 (pan-leukocytes), anti-CD3 (pan-T-cells), anti-CD4 (T-helper cells), and anti-CD8 (cytotoxic T-cells) is a basic marker combination in immunology and was used for system evaluation. Three out of the six possible parameter combinations are shown (Fig. 5a–c). The cutoff values, which are defined by appropriate isotype-matched negative controls (Fig. 5d–f), allow computation of cell populations in the four quadrants.

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Figure 5. Phenotypic characterization of tissue infiltrating leukocytes. Rejected renal allograft tissue was stained with anti-CD45 ECD, anti-CD3-PE, anti-CD8-FITC, and anti-CD4-PC5. Data acquisition was done by spectral imaging microscopy (28) using a Zeiss LSM 510 META. Anti-CD45 reactivity was used as “masterchannel,” i.e., objects were identified in the CD45-channel and fluorescence intensity was measured in all four channels (ac). The cutoff values were determined by corresponding isotype-matched negative controls (df). Data are represented on a logarithmic scale over four decades.

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Using this technique and a comparative approach, prominent differences in the cellular composition of tissue-infiltrating leukocytes could be demonstrated between graft-rejecting and tumor-infiltrating leukocytes in the kidney (21). Moreover, a quantitative correlation of T-cell number and phenotype with the in situ cytokine profile could be obtained on a single-cell level (Ecker et al., unpublished results). This is especially important in transplantation immunology, in which both the phenotypic composition of graft-infiltrating leukocytes and the cytokine profile, which indicates the type of immune response with respect to the T-helper type(s) involved, are important prognostic factors (25–27).

In Situ Quantification of Proliferation Markers and Tumor Suppressors

One of the most obvious applications of MMTC is the search for predictive markers in cancer diagnosis. Anti-Ki-67 (a proliferation marker), anti-p53 (wild-type; a molecule involved in gene repair and cell cycle), and anti-pan-cytokeratin were measured in tumor cells. An example for the reactivity of this triple combination of anti-Ki-67 (FITC, in green), anti-p53 (Cy3, in red), and anti-pan-cytokeratin (Cy5, in blue), when applied to squamous-cell carcinoma, is given in Figure 6a. Epithelial structures were identified by anti-cytokeratin (data not shown). This CeCoLyzer™ algorithm identified all anti-p53+ (Fig. 6b) or anti-Ki-67+ (Fig. 6c) nuclei surrounded by anti-cytokeratin+ areas. The binary masks obtained by these two channels were combined by the Boolean operator OR (Fig. 6d). Figure 6e shows the result of the analysis depicted as a dot plot and the data evaluated for this specimen.

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Figure 6. In situ quantification of Ki-67/p53 (co-)expression. A triple-staining combination (a) of anti-cytokeratin-Cy5 (blue), -p53-Cy3 (red), and –Ki-67-FITC (green) of squamous cell carcinoma is used to determine the number of tumor cells per mm2 of malignantly transformed tissue expressing Ki-67 (b) and/or wild-type p53 (c). The cytokeratin-channel is segmented by two gray-value thresholds and combined with the original image by the Boolean AND operation, leaving only cytokeratin+ areas for analysis. The p53- and Ki-67-channel are segmented separately. Finally these two masks are combined by the Boolean OR operation (d). Using this mask for identification of tumor cells expressing Ki-67 and/or p53 and the cytokeratin mask for measuring the epithelial area, the number of cells per mm2 expressing either or both markers is computed (e). Data in scattergram are represented on a logarithmic scale over four decades.

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Quantification of the intranuclear Ki-67/p53 ratio is of particular interest in breast cancer diagnosis (28). Measuring the expression of Ki-67 was also of interest when tumor distant normal areas of renal tissue and corresponding renal carcinoma (pT3aN0M1/G3-renal cell carcinoma) were analyzed (data not shown). Tumor distant areas showed normal morphology, which was confirmed by hematoxilin/eosin staining of corresponding serial sections. Only 2.1% of nuclei in normal tissue were Ki-67+, with the cutoff set referring to the isotype-matched control, which was not a significant difference. The morphologic characteristics of RCC sections and the frequency of nuclei within the tumor specimens expressing the Ki-67 protein, however, appeared to be significantly different, and a substantial amount of individual nuclei (19.4%) stained positive for Ki-67.

In Situ Quantification of Apoptosis

Quantitative determination of apoptosis within cytomes is an important application in many fields of research, in particular in drug screening approaches of the pharmaceutical industry (29). In order to meet the requirements of Good Laboratory Practice and Good Manufacturing Procedures, reliable quantification of cellular and molecular reactions of large cell populations to drugs on a single-cell level is essential.

MMTC was used to quantify apoptosis of epidermal Langerhans cells (LC) in mouse skin treated with different chemical agents, in search for anti-inflammatory drugs. For this purpose, mice were topically treated either with vehicle or with the compounds hydrocortisone or clobetasol propionate, at their clinically used concentrations. Apoptosis was visualized using terminal deoxynucleotidyl transferase-mediated nick end labeling technique (TUNEL) Pictures were taken with a confocal laser scanning microscope (CLSM), followed by an in situ quantification using MMTC. Figure 7 shows a side-by-side comparison between vehicle- (negative control), and compound-treated skin. Quantitative analysis clearly show drug-induced apoptosis, which appears to be potency dependent.

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Figure 7. Quantification of apoptosis of Langerhans cells in mouse skin. BALB/c mice were topically treated with ethanol (negative control, left column), as well as ethanolic solutions of 1% hydrocortisone (middle column), and 0.05% clobetasol propionate (right column). After 72 h, ear epidermal sheets were subjected to the TUNEL technique (TUNEL Label Mix; Roche Molecular Biochemicals, Indianapolis, IN; in green) and counterstained with a PE-conjugated anti-MHC class II monoclonal antibody (in red). Yellow dots are red and green double positive structures, i.e., apoptotic resident MHC II+ Langerhans cells. The lower line shows scattergrams obtained by MMTC-analysis. The relative fluorescence intensity of the TUNEL staining is shown on the x-axis, the relative fluorescence intensity of anti-MHC II-PE is shown on the y-axis. Both axes are depicted in logarithmic scale over four decades.

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With the ability to relate macroscopic effects to microscopic causes in a quantitative and statistically revisable manner, cytomics approaches are exactly the type of investigation needed in order to determine the influence of drugs on the pathophysiology of cells, tissues, and organs. Hence, cytomics is an essential discipline for drug design and therapy implementation, and MMTC is a means of realizing cytomics investigations in the tissue.

SUMMARY AND PERSPECTIVES

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED

MMTC is capable of objectively evaluating the intrinsic advantages of immunohistology, i.e., phenotypic characteristics in correlation with morphologic features, and to provide a standardized method for improved differential analysis. Variable methods for recognition of individual cells in tissue context have been successfully applied. In general, the use of tissue cytometry in biomedical research and clinical diagnosis, as well as approaches towards predictive medicine (30, 31), could contribute to improved patient care. Using MMTC, the investigator may get 1) very detailed analyses of single immunofluorescent images or 2) evaluation of large image series analyzed for thoroughly defined areas of interest, which will presumably be required more frequently. While immunohistology so far has been primarily based on just visual “look and conclude” evaluations, observer-independent numerical data are now available based on an arbitrary number of single measurement values.

MMTC is prepared to be used to analyze as many channels as necessary. The number of channels that can be analyzed simultaneously is limited only by the detection device used. When equipment for spectral imaging (e.g., Zeiss LSM META) is used, analysis of eight (23) or more channels appears to be feasible. When conventional microscopes with band-pass filters and a CCD-camera are used, the number of channels that can be synchronously analyzed will usually be limited to three or four. However, CCD-based imaging is faster than laser scanning and the respective equipment for CCD-based imaging is significantly cheaper than for laser scanning. Hence, it depends on the application (clinical routine: primarily fast, versus research applications: primarily sophisticated) as to which equipment is most appropriate. Regardless of these requirements, MMTC can be done with both laser scanners and CCD imagers, as well as band-pass and spectral imaging devices. In the context of cytomics, MMTC is especially useful in combination with advanced detection techniques in fluorescence microscopy. A combination of spectral imaging (23, 32, 33) and advanced image analysis currently permits multicolor tissue cytometry on the single-cell level with eight parameters (antigens); in the future this combination may allow the use of even more parameters.

It could be further envisaged that MMTC will be appropriate to routinely analyze tissue specimens for which it has so far been difficult or impossible to determine the phenotypic characteristics in situ. We expect that with the advent of cytomics, tissue cytometry in general, and tissue cytometry on the single-cell level in particular, will become a standard requirement for the determination of tissue status for various types of disease—from malignant and benign neoplasia to autoimmune disorders. The quantitative determination of immunophenotypically-identified cell populations in tissue sections will significantly increase both our knowledge and understanding of cytologic relations and the diagnostic capacity (34). Cells with similar or identical morphology but different phenotype (function) can be discriminated from each other and the cytologic in situ tissue measurements are orders of magnitude more accurate than what has been available before.

Using MMTC, excellent results of tissue cytometry were obtained despite the short time required for evaluation. Due to the automated processing of measurements, the problem of observer-dependant discrepancies, as reported by several authors (35–38), is avoided, and results are highly reproducible (21). These results refer to the protein-level, the single cell and its localization, and the distribution of the protein under investigation within the tissue section. In contrast to PCR-based methods and biochips (for review see39,40), MMTC never leaves unanswered the question of whether a protein is present or not. Of special interest in routine cancer diagnosis are the predictive value of cell-cycle related proteins and tumor suppressor proteins. The samples may be huge specimens of solid tissues, as with tumor nephrectomy, but may also be biopsy material. Therefore, the potential applications of MMTC go beyond the field of oncology; it is, for example, also qualified for monitoring the allograft status after transplantation by determining the extent to which a host versus graft immune response occurs. The differential diagnosis can be very detailed, e.g., the amount of leukocytes of a particular phenotype can be evaluated for intraglomerular, perivascular, and peritubular localization.

The variable identification strategies make MMTC a highly versatile analysis system that can help to investigate various types of cells in various kinds of tissues. MMTC is also applicable to in situ analysis of cultured cells, and phenotypic characterization of smears (data not shown).

MMTC allows the quantification of tissue-, cell-, and function-specific molecular patterns within cytomes on the single-cell level, and may thereby constitute an important technique in cytomics approaches.

Acknowledgements

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED

We thank Adelheid Elbe-Buerger, Wolfram Hoetzenecker, and Christoph Kopp for the fruitful collaboration, and Rainer de Martin for proofreading the manuscript.

LITERATURE CITED

  1. Top of page
  2. Abstract
  3. TECHNIQUES
  4. APPLICATIONS
  5. SUMMARY AND PERSPECTIVES
  6. Acknowledgements
  7. LITERATURE CITED