Division of Proteomics of Stem Cells and Cancer
Prof. Dr. Jeroen Krijgsveld
Functionality of proteins can be inferred from proteomic data characterizing their expression over time (left), their expression pattern across conditions and compared to other proteins (middle), and from their interaction partners (right).
© dkfz.de
Proteomes are highly complex, consisting of thousands of proteins that operate in intricate networks in a cell and condition-specific manner. The interest in our division is to understand proteome complexity, and to develop tools to investigate how the proteome is dynamically regulated in time and space. Using state-of-the-art mass spectrometric technologies we aim to understand processes that are fundamental to cancer biology and to pluripotency in stem cells, both using cell lines as well as in vivo model systems. For instance, this has enabled us to identify novel proteins controlling the identity of mouse hematopoietic stem cells, and proteins that are key in the gain of pluripotency during reprogramming of fibroblasts to induced pluripotent stem cells (iPSCs). Benefiting from novel methodologies for sample preparation developed in our lab, we are now capable of handling very small sample sizes, integrated in a robotic system for automated and standardized sample handling. This technology will be highly beneficial for studying quantity-limited (clinical) samples. In addition, we have a specific interest in secreted proteins for their role in cellular crosstalk, in RNA-binding proteins involved in translational control, and in chromatin-binding proteins for their key function in transcriptional regulation and cell fate decision. We have developed dedicated biochemical methodologies to enrich for each of these protein classes, based on click-chemistry to study secretory proteins, and combinations of cross-linking and various affinity-enrichment approaches to capture and identify proteins interacting with RNA and DNA. FUTURE OUTLOOK We will now exploit the power of these innovative biochemical methods in combination with our mass spectrometric platforms to gain mechanistic insight in the regulation of cancer and stem cell identity. In particular, we will use our optimized workflows for global proteome profiling of clinical tumor samples. Secretome analysis will shed light on inter-cellular communication in a cancer context, and on principal mechanisms of secretory pathways. We will extend our chromatin interaction studies to identify and functionally characterize the composition and dynamics of protein networks around transcription factors and chromatin-modifying enzymes. We increasingly combine this information with ChIP-seq and RNA-seq, to gain a more complete understanding of how gene expression is regulated, and how this is derailed in cancer.
