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In vitro model systems used in drug discovery typically do not address the complex architecture of human disease tissues. Only few approaches aim to faithfully recapitulate the complexity, heterogeneity and cellular dynamics e.g. in epithelial tissues and carcinomas. The most important aspects relate to the (tumor-) microenvironment, including cell-cell and cell-matrix interactions, inflammation and the role of stromal components. All of these elements can have a significant, but often underestimated impact on differentiation, normal and abnormal tissue functions, or drug response versus drug resistance.

The basis for performing informative high content screening campaigns with such complex tissue models in vitro is access to fast, automated image analysis. We have developed a software platform (AMIDA, Automated Morphometric Image Data Analysis) that captures a large number of morphometric features in an unsupervised fashion. This approach enables us to capture much of the inherent complexity and dynamics of microtissues, yet still allows high experimental throughput. This screening platform is ideally suited for investigating a broad spectrum of defined, biological questions in drug discovery as well as personalised medicine.

Technology and screening platform are applicable for multiple types of research, such as quantitatively measuring the response of primary cancer cells or cell lines to drugs, siRNAs or other perturbations. Image analysis algorithms can also be adapted towards specific applications in neurodegenerative diseases, stem cell research, and to quantitate the interaction of epithelial cells with immune, adipocytes or mesenchymal stem cells.