Rupert C. Ecker

Cytokine expression pattern in benign prostatic hyperplasia infiltrating T cells and impact of lymphocytic infiltration on cytokine mRNA profile in prostatic tissue.

The aim of the study is to characterize the type of immune response in benign prostatic hyperplasia (BPH) tissue. BPH tissue–derived T cells (n = 10) were isolated, activated (PMA + ionomycin), and analyzed for intracellular reactivity with anti–IFN-γ and IL-2, -4, -5, -6, -10, and -13, as well as TNF-α and -β by four-color flow cytometry. Lymphokine release was tested using Th1/Th2 cytokine bead arrays. The amount of IFN-γ and IL-2, -4, -13, and TGF-β mRNA expressed in normal prostate (n = 5) was compared with that in BPH tissue separated into segments with normal histology (n = 5), BPH histology with (n = 10) and without (n = 10) lymphocytic infiltration, and BPH nodules (n = 10). Expression of lymphokine receptors was analyzed by immunohistology, flow cytometry, and RT-PCR. We found that 28 ± 18% of BPH T helper cells were IFN-γ+/IL-4− Th1 cells, 10 ± 2% were IFN-γ−/IL-4+ Th2, and 12 ± 6% were IFN-γ+/IL-4+ Th0 cells. In relation, cytotoxic and double-negative BPH T lymphocytes showed a slight decrease in Th1 and Th0 in favor of Th2. In double-positive BPH T lymphocytes, the trend toward Th2 (35 ± 15%) was significant (Th1: 12 ± 7%; Th0: 5 ± 4%). Lymphokine release upon stimulation was found in the case of IL-2, IL-5, IFN-γ, and TNF-α > 4 μg; of IL-4 > 2 μg; and of IL-10 > 1 μg/ml. Expression of lymphokine mRNA in tissue was increased (2- to 10-fold) in infiltrated BPH specimens with and without BPH histology. The infiltrated BPH specimens with normal histology differed from those with BPH histology, most evident by the significant decrease in IFN-γ and the increase in TGF-β mRNA expression. Infiltrated BPH specimens with BPH histology expressed significantly more IFN-γ (5-fold), IL-2 (10-fold), and IL-13 (2.8-fold) when compared with noninfiltrated BPH specimens. BPH nodules, however, showed the highest level of expression of IL-4 and IL-13, with only intermediate levels of IFN-γ and very low levels of IL-2 mRNA. Immune response in histologically less transformed BPH specimens is primarily of type 1, whereas in chronically infiltrated nodular BPH and especially within BPH nodules, it is predominantly of type 0 or type 2.

Application of spectral imaging microscopy in cytomics and fluorescence resonance energy transfer (FRET) analysis.

Specific signal detection has been a fundamental issue in fluorescence microscopy. In the context of tissue samples, this problem has been even more pronounced, with respect to spectral overlap and autofluorescence.

Microscopy‐based multicolor tissue cytometry at the single‐cell level

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.

Cytomics goes 3D: Toward tissomics

CYTOMICS AND MICROSCOPY Conducting a Human Cytome Project will require stateof-the-art techniques and technologies in different fields. Methods arising from molecular biology (genome level) and protein chemistry (proteome level) will be necessary but not sufficient for this project. Although the cytome contains the genome and the proteome, the cytome level comprises additional and essential information. The art of combining molecular, morphologic, and phenotypic information at the single-cell level and at the biological locus where cellular dysfunction appears and formation of disorders takes place—an art known as microscopy—represents the key framework of methods for scrutinizing the cytome (1,2). Although two high-throughput and high-content methods are currently at hand for multiparametric and multicolor cell analysis, i.e., flow and image cytometry, spatial relations and the molecular morphology of cells and tissues are accessible only to microscopic analysis (3–5). In a cytomics context, multiparameter fluorescence methods and hyperchromatic staining are of particular interest because they permit the sequential analysis of DNA status, protein expression and protein interaction, cellular distribution, and morphologic characteristics (6,7).

An improved method for discrimination of cell populations in tissue sections using microscopy-based multicolor tissue cytometry.

In tissue context, researchers and pathologists lack a generally applicable standard for quantitative determination of cytological parameters. Increasing knowledge of disease‐specific markers calls for an appropriate in situ tissue cytometry.

3D parallel coordinate systems—A new data visualization method in the context of microscopy-based multicolor tissue cytometry

Presentation of multiple interactions is of vital importance in the new field of cytomics. Quantitative analysis of multi‐ and polychromatic stained cells in tissue will serve as a basis for medical diagnosis and prediction of disease in forthcoming years. A major problem associated with huge interdependent data sets is visualization. Therefore, alternative and easy‐to‐handle strategies for data visualization as well as data meta‐evaluation (population analysis, cross‐correlation, co‐expression analysis) were developed.

An Emerging Approach for Parallel Quantification of Intracellular Protozoan Parasites and Host Cell Characterization Using TissueFAXS Cytometry.

Characterization of host-pathogen interactions is a fundamental approach in microbiological and immunological oriented disciplines. It is commonly accepted that host cells start to change their phenotype after engulfing pathogens. Techniques such as real time PCR or ELISA were used to characterize the genes encoding proteins that are associated either with pathogen elimination or immune escape mechanisms. Most of such studies were performed in vitro using primary host cells or cell lines. Consequently, the data generated with such approaches reflect the global RNA expression or protein amount recovered from all cells in culture. This is justified when all host cells harbor an equal amount of pathogens under experimental conditions. However, the uptake of pathogens by phagocytic cells is not synchronized. Consequently, there are host cells incorporating different amounts of pathogens that might result in distinct pathogen-induced protein biosynthesis. Therefore, we established a technique able to detect and quantify the number of pathogens in the corresponding host cells using immunofluorescence-based high throughput analysis. Paired with multicolor staining of molecules of interest it is now possible to analyze the infection profile of host cell populations and the corresponding phenotype of the host cells as a result of parasite load.

Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion

Breast cancer is the most common cancer type in women worldwide. Histological evaluation of the breast biopsies is a challenging task even for experienced pathologists. In this paper, we propose a fully automatic method to classify breast cancer histological images to four classes, namely normal, benign, in situ carcinoma and invasive carcinoma. The proposed method takes normalized hematoxylin and eosin stained images as input and gives the final prediction by fusing the output of two residual neural networks (ResNet) of different depth. These ResNets were first pre-trained on ImageNet images, and then fine-tuned on breast histological images. We found that our approach outperformed a previous published method by a large margin when applied on the BioImaging 2015 challenge dataset yielding an accuracy of 97.22%. Moreover, the same approach provided an excellent classification performance with an accuracy of 88.50% when applied on the ICIAR 2018 grand challenge dataset using 5-fold cross validation.

Abstract 1662: Towards automated analysis of prostate inflammatory state: Assessing density and distance of infiltrating T-cells (CD3+) to glandular structures in prostate biopsies by a new software

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA In the prostate, normal glands, glandular hyperplasia, and adenocarcinoma are often accompanied by inflammation. Infiltrating inflammatory cells may affect glandular cells and either promote their proliferation or, alternatively act in tumor suppression. To better understand the function of the inflammatory cells in tumor development, type and number of inflammatory cells and their proximity to glandular structures have to be analyzed in situ and correlated with disease state. To replace the time-consuming, error-prone human evaluation of stained tissue sections, we aimed at developing an automated method capable of detecting inflammatory and prostate glandular cells and of measuring the distance between these structures. Methods: Formaldehyde-fixed, paraffin-embedded prostate cancer biopsies (n=53) were stained with DAB-conjugated anti-CD3-antibody and hematoxylin (H). Large tissue areas were digitized with TissueFAXS 4.0 using a 20x objective and the virtual stitched images were used to develop a StrataQuest 5.0 analysis profile. Manual annotations were used for validation. Results: Color unmixing generated the virtual channels for H and DAB optical densities. Cells were then identified using nuclear segmentation on the H channel. For CD3+ cell counts, each cell was quantified for its DAB signal in a peri-nuclear mask, for which cutoff was set interactively. The epithelial cell (EC) nuclei were selected by gating for nuclear compactness and area. EC density was computed using Parzen windows and thresholded for obtaining the epithelial mask (EM). Whole tissue area excluding lumen was computed based on color. Stroma was then extracted as remaining area. In-silico Distance Transformation (DT) from EM was calculated as a monochrome channel aligned with the original virtual slide. Thus, by using the average of the DT inside each nuclear mask, the distance of each cell to the closest EM was computed. Three peri-glandular proximity areas (0-25um, 25-50um and 50-75um) were used for CD3+ population distribution assessment. Validation of the method revealed an F-score of 0.87 for EM detection and a p value of <0.001 for automated versus manual CD3+counting. The percentage of EM versus total area ranged from 18% to 50%. Between 54% to 89% of all CD3+ cells were found in the closest proximity of EM (in the 0-25um mask). A positive correlation between EM area and T-lymphocyte density in the closest vicinity mask was observed (p < 0.001). Conclusion: The described method enables reliable automated detection of prostate tissue compartments (EC, stroma), CD3+ T-cells counting as well as measurement of their distance from the glandular epithelial cells. Such automated measurements can lead to reliable and rapid measurements of cell location distribution in normal versus tumor prostate tissues, opening new routes in diagnosis and prognosis. Citation Format: Radu Rogojanu, Bogdan Boghiu, Georg Schaefer, Theresia Thalhammer, Georg Steiner, Rupert Ecker, Bettina Schlick, Thomas Szekeres, Isabella Ellinger. Towards automated analysis of prostate inflammatory state: Assessing density and distance of infiltrating T-cells (CD3+) to glandular structures in prostate biopsies by a new software. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1662. doi:10.1158/1538-7445.AM2014-1662

Towards the Automated Detection and Characterization of Osteoclasts in Microscopic Images

Microscopes have been used for a long time to observe biological samples. However, measurements of tissue- and cell-related parameters were conducted by human observers and were consequently ad hoc, not reproducible and restricted to small sample numbers. Since computers have become vastly more powerful, life sciences now routinely take advantage of new opportunities to couple microscopy and in silico methods. Automated image segmentation and analysis of large numbers of digital images allow algorithmic recognition of cell and tissue structures and subsequent numeric measurements of cellular parameters. Nevertheless, these new methods also come with technical challenges concerning computational resources like processing capacity, memory and disk space, biological sensor limitations, as well as algorithm development.