Background: Central support for genomic methods and bioinformatic data analysis is an important component of a collaborative research consortium and has to be accompanied by state-of-the-art method development.

Strategy: We provide important expertise and have established methods in genomics and bioinformatics, which will be adapted and tailored to suit the needs of the CRC research projects and thereby enable important insights into checkpoints of central nervous system recovery. In this project, we will provide central support and training infrastructure as well as perform original research in method development as well as with integrative data analysis.


As the understanding of the multicellular response determining recovery after CNS injury, requires all PIs to have access to cutting-edge experimental and computational methods, we have centralized and integrated our expertise within a Genomics and Bioinformatics Platform Z02. Starting from the first funding period this project supports our entire consortium from experimental design to bioinformatics data analysis for genomic methods, which include bulk genomic analysis and single cell methods such as scRNAseq and snRNASeq. These technologies include 10x Genomics and flow-cytometry based isolation methods. In addition we provide training to CRC members on tissue dissociation and single-cell sorting and usage of 10x Genomics equipment to generate high-quality data. By centralizing the sample preparation, sequencing and analysis, our core provides methods for rapid turnaround and high-quality data. Our analyses have provided critical insights into the complex cellular reactions that occur upon CNS injury in the various model. We have, for example, uncovered various reactive glial states that drive pathological processes in injury conditions. In the upcoming funding period we want to further expland our method development and project support and in particular focus on the development of methods and tools to integrate CRC datasets across models and species. These data range from different genomic, epigenomic, transcriptomic or proteomic data, to imaging data to complex high-dimensional spatial transcriptomics or single-cell data. Based on this datasets, data analysis methods and processing pipelines to analyze and interpret these data will be developed in order to provide a central platform that helps to standardize data analysis and facilitate data integration within the CRC. Thus, one focus will be on the integrative and comparative analyses of the responses that occur after various injuries and in the different models. Another important focus for the upcoming funding period will be to analyze human samples. One limitation, however, is that samples can be highly variable and biased by the cause of death and post-mortem time. We will, therefore, leverage information from our model organisms, and perform integrated analyses of mouse and human datasets. A second focus is the development of data acquisition and analysis pipelines for combined spatial transcriptomics and single cell sequencing data from experimental and human (in collaboration with Z01) tissue samples. The overall aim of this integrative data analysis is to help identify the checkpoints of central nervous systems recovery, understand which of these checkpoints are shared across disease models and species and determine their relevance for human CNS pathologies in situ.