StratRegen generates cutting-edge research. In addition to this, the programme is providing strategies to maximize benefits from the research results by supporting research initiatives in a number of areas where technology development will be of particular importance to stem cell research and regenerative medicine.
While stem cell research and regenerative medicine are of obvious importance to the healthcare sector and thus of considerable societal importance, stem cell research and regenerative medicine also offer several opportunities for the business sector. Companies have been spun out from the research activities at KI, and other companies rely on the expertise at KI in these research areas for specific product developments. This is in line with a globally growing involvement of the biotechnology sector, and it is observed in leading universities that large academic constellations of high-quality research in stem cell research and regenerative medicine foster a vibrant biotechnology sector, for example in developing tools for stem cell research and in the development of stem cell-based screening methodologies.
StratRegen supports research initiatives in a number of areas where technology development will be of particular importance to stem cell research and regenerative medicine, namely:
Whole organisms cell linage analysis
Cell lineage tracking. Understanding cell lineage in developing mammalian embryos, as well as in regenerating adult tissues, is an important but unexplored field. We have developed a high-throughput method for selectively sequencing short polyguanine repeat sequences, which are prone to somatic mutation. As mutations accumulate during embryogenesis, individual cells become marked with distinct patterns of polyguanine repeat polymorphisms. These patterns can be used to infer the ancestral relationships of cells within and between tissues.
Phylogenetic lineage analysis. New mutations arise in the genome of somatic cells with every division, so that the genome of a single cell contains unique differences that distinguish it from other cells. Cells with closely related patterns of mutations share recent common ancestry. Thus, the inheritance pattern of such genomic mutations in cells will mirror their hierarchical relationship. For example, polyguanine repeat sequences – abundant throughout the genome and particularly vulnerable to mitotic mutations altering their length – seem to represent good cell lineage indicators for the time of birth of a given cell relative to others. Preliminary results suggest that the number of rapidly evolving loci that would have to be sequenced in order to trace cell lineage is tractable, in the range of a few hundred per cell, and that unbiased DNA amplification techniques are sufficiently robust to allow the technology to work at the single cell level.
Biomaterials and Tissue Engineering. Biomaterials offer the opportunity to control the physical and chemical stimuli sensed by cells and find applications in the development of in vitro models of developmental and disease processes as well as in regenerative medicine approaches. We are developing biomaterial-based methods to manipulate stem cell microenvironments with temporal and spatial precision. Using electroactive materials, we were able to dynamically control the activity of heparin-bound growth factors and the onset of neural stem cell differentiation. Further, we are investigating the potential roles of Eph and Notch pathways in mechanotransduction, the conversion of physical to biochemical signals, using novel approaches to control the nanoscale spatial distribution and mechanical properties of ligand assemblies.
Bioinformatics / transcriptomics
Transcriptomics. Cell theory was developed over 150 years ago, demonstrating that all life is composed of cells, and that new cells arise exclusively from other cells. This is one of the few fundamental laws of biology. However, surprisingly there is no agreement on the number of cell types present in the mammalian body, or their relationships. One reason is the lack of tools for unbiased discovery and classification of cell types. We propose to produce a map of the abstract cell type landscape by analyzing the transcriptomes of thousands of single cells picked at random from tissues. For this purpose, we have developed methods for large-scale single-cell RNA sequencing, capable of analyzing thousands of single cells.