Joint project of the Brandenburg Health Campus "Consequences of age-associated cell and organ functions"

Analysis of Human Cells in the Aging Process with a Focus on DNA Double Strand Breaking and Applied Statistical Bioinformatics

Affiliated with topic 1: Age dependence of tissue and organ function,

subproject 1.4: Multiomics - multimodal predictive analysis in the blood organ Head: Dr. Stefan Rödiger

research status

The scope and complexity of health-related data is growing rapidly due to digitalization and is undergoing various transformation processes. In particular, the volume of digital images, Next Generation Sequencing data and multiparametric data sets from point-of-care and high-throughput platforms is growing daily with the advent of new technical solutions. In our working group we use various commercial systems and specially developed multiparameter platforms. Examples include sequencers, microarrays and our VideoScan platform. The technologies were used in previous studies to link biomarkers (proteins, genes) and cellular specifics (patterns) with phenotypic parameters. In modern medicine, therefore, it is likely that new approaches to answer important questions about autoimmunity and tumor formation.

The range of methods in the laboratory generates a variety of data categories (nominal, discrete, continuous), which can usually help to answer a sub-question, but bringing together large multivariate datasets has strong limitations. One of the basic problems of multiparametric high-rate systems is the production of data volumes that can no longer be handled with conventional methods. However, scientists are also in an elegant position, as solutions can be found to answer questions not previously considered by others. The overall objective of the work is to derive a strategic framework for the early detection and risk assessment of autoimmunity and tumor formation through digital image processing and bioinformatics approaches. Big Data will play a role here in the future. It can be assumed that the aetiology and mechanisms of diseases can be defined using this approach and that the acquired knowledge can be transferred to clinics as digital biomarkers or biological biomarkers.

Planned results We know from experience that high volumes of qualitative and quantitative data will be generated. In data analysis, it will therefore be crucial to build effective analysis algorithms that allow us to automatically extract features from experimental datasets in the context of γ-H2AX Foci (cooperation with Prof. Markus Deckert, MHB). The histone H2AX is phosphorylated on serine 139 in response to double-strand breaks. This form is called γ-H2AX and can be visualized by immunofluorescence staining as a focus in the fluorescence microscope and is a putative prognostic marker. Therefore, γ-H2AX foci have established robust and sensitive detection of DNA double strand breaks (DSB) and play an important role in the treatment of tumors. DSBs can induce apotosis or cause genomic instability. They are closely related to carcinogenesis and autoimmune diseases. The image data are structured by project partners on several biological samples and analyzed in detail with methods of digital image processing and statistical bioinformatics. It will be evaluated which statistical methods can be found that allow an association of primary data and secondary data from other data sources. The results will be used by studies with the project partners to develop a better understanding of the mechanisms of diseases (autoimmune diseases, tumor biology). Later, the results should also be available for experimental medicine and personalized medicine.