High-Throughput Methods

The division High-Throughput Methods currently consists of 13 members. One of our goals is the provision of standardized protocols (SOPs) describing how to obtain, process, store and document patient-derived biological sample material to ensure their usability for a broad spectrum of high-throughput methods. For this purpose the patients have to be informed about the goals and studies of the ZPM and give their consent. Therefore an interdisciplinary valid ethics committee vote has to be prepared that will especially meet the concerns related to genetic diagnostics, and fulfill the requirements of the ‘Gendiagnostikgesetz’ (GenDG) (German Gene Diagnostics Law). At the same time the associated clinics must develop recommendations concerning the different types of databases used and storage and access authorization for patient data. Already, the flood of information from high-throughput methods poses an analysis problem for the experts. Especially the representation of the diagnostic findings with state-of-the-art analysis software is not sufficient. At this point we closely cooperate within the ZPM and with the Leibniz-Institute ‘Knowledge Media Research Center’ (KMRC; Prof. F. Hesse) to accomplish better and constructive data visualization.

Another central pillar of the division is the steady enhancement and development of our established high-throughput technologies, which are primarily available in the excellent core facilities of the medical faculty and the ‘Center of Quantitative Biology’ (QBiC). A central question of the ZPM is to what extent these technologies are useful to predict the course of the disease and the therapeutic response. Beyond this they potentially help to forecast side-effects which then can be considered in the therapy plan. Besides the genomic, epigenomic and proteomic analysis, the consequent development of biomarker analysis and the integration of microbiome data will play a pivotal role. Here our department faces major challenges, namely to provide data in no time of the highest quality, to analyze them in a patient- and illness-related manner and to establish a concept of how to transfer these results to key persons to ensure immediate treatment decisions. Finally we want to develop procedures of how to integrate feedback information of the treatment and who is responsible for re-evaluation of the case. This self-learning strategy will guarantee a steady improvement of the personalised medicine approach at the ZPM.