Browsing Publications of the research group Chemical Biology (CBIO) by Title
Now showing items 101-103 of 103
What Makes Umbilical Cord Tissue-Derived Mesenchymal Stromal Cells Superior Immunomodulators When Compared to Bone Marrow Derived Mesenchymal Stromal Cells?MSCs derived from the umbilical cord tissue, termed UCX, were investigated for their immunomodulatory properties and compared to bone marrow-derived MSCs (BM-MSCs), the gold-standard in immunotherapy. Immunogenicity and immunosuppression were assessed by mixed lymphocyte reactions, suppression of lymphocyte proliferation and induction of regulatory T cells. Results showed that UCX were less immunogenic and showed higher immunosuppression activity than BM-MSCs. Further, UCX did not need prior activation or priming to exert their immunomodulatory effects. This was further corroborated in vivo in a model of acute inflammation. To elucidate the potency differences observed between UCX and BM-MSCs, gene expression related to immune modulation was analysed in both cell types. Several gene expression profile differences were found between UCX and BM-MSCs, namely decreased expression of HLA-DRA, HO-1, IGFBP1, 4 and 6, ILR1, IL6R and PTGES and increased expression of CD200, CD273, CD274, IL1B, IL-8, LIF and TGFB2. The latter were confirmed at the protein expression level. Overall, these results show that UCX seem to be naturally more potent immunosuppressors and less immunogenic than BM-MSCs. We propose that these differences may be due to increased levels of immunomodulatory surface proteins such as CD200, CD273, CD274 and cytokines such as IL1β, IL-8, LIF and TGFβ2.
What you see is what you get: Activity-based probes in single-cell analysis of enzymatic activitiesMolecular imaging methods can provide spatio-temporal information about the distribution of biomolecules or biological processes, such as certain enzymatic activities, in single cells. Within a cell, it is possible to define the subcellular location of a target, its trafficking through the cell, colocalization with other biomolecules of interest and involvement in certain cell biological processes. On the other hand, single-cell imaging promises to distinguish cells that are phenotypically different from each other. The corresponding cellular diversity comprises the presence of functionally distinct cells in a population (‘phenotypic heterogeneity’), as well as dynamic cellular responses to external stimuli (‘phenotypic plasticity’), which is highly relevant, e.g. during cell differentiation, activation (of immune cells), or cell death. This review focuses on applications of a certain class of chemical probes, the so-called activity-based probes (ABPs), for visualization of enzymatic activities in the single-cell context. It discusses the structure of ABPs and other chemical probes, exemplary applications of ABPs in single-cell studies in human, mouse and bacterial systems and considerations to be made with regard to data interpretation
xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action DiscoveryMode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.