Metabolomics And The Role Of Metabolism In Stem Cell Bioprocessing

Stem cell bioprocesses require reproducibility, robustness and quality control of both the process and the product for wide clinical use1. The role of metabolism is critical in stem cell bioprocesses as it controls cellular processes (proliferation, apoptosis, reprogramming) but also influences gene regulation and cellular physiology by directly affecting epigenomic changes2. Metabolomics analysis of both intracellular (finger-printing) and extracellular (foot-printing) metabolites a) enables evaluation of “cellular state”, b) captures a holistic view (snapshot) of the cell culture physiology, and c) provides dynamic information culture needs that can be used for bioprocess optimisation. Examples of research conducted in our group highlight 1) that metabolic profiling was able to identify differences in human pluripotent cell physiology (hESCs and hiPSCs) after treatment with ROCK inhibitor, which control gene expression and protein expression was not sensitive enough to detect; 2) time-series metabolomics analysis of the osteogenic differentiation process of umbilical cord blood mesenchymal stem cells identified differences in the efficiency of two major osteoinductive agents (dexamethasone and BMP-2) demonstrating that dexamethasone-treated MSCs were metabolically close to human primary osteoblasts; 3) the development of a novel perfusion bioreactor for the culture of pluripotent stem cells (ESCs) that facilitates environmental homeostasis by maintaining sufficient levels of nutrients while preventing the accumulation of metabolic by-products over toxic levels ensuring ESC pluripotency. The above examples emphasise the importance of metabolomics in all stages of stem cell bioprocess by sensitive and effective monitoring, which can be used for robust bioprocess optimisation as well as bioprocess and product quality control – critical aspects of biomanufacturing for clinical applications

Systematic experimental design for bioprocess characterization: elucidating transient effects of multi-cytokine contributions on erythroid differentiation

In vitro differentiation of hematopoietic stem cells (HSCs) is a highly dynamic process whereby contributions of exogenous cytokines vary at each stage of differentiation. In this study, we present erythroid differentiation as three progressive yet independent stages and aim to elucidate transient contributions from stem cell factor (SCF), insulin-growth factor II (IGF-II), and erythropoietin (EPO). This will be accomplished using the Taguchi design and response surface methodology (RSM). We found that cultures with high process variability (noise factors), such as those in primary cell cultures, pose limitations on the effectiveness of RSM and result in inconsistencies in empirical models developed for elucidating transient effects. However, the Taguchi design—which showed greater robustness in accommodating for noise factors—successfully identified significant main and interactive contributions at each differentiation stage, thus highlighting the dynamic roles of each cytokine. The Taguchi analysis suggested high IGF-II dependency during early erythroid differentiation, with an antagonistic effect in the presence of EPO. At mid-stage differentiation, the roles of SCF and EPO dominate those of IGF-II, and the former act independently. Finally, toward erythroid maturation, only EPO plays a significant role. Although process outcomes from the Taguchi analysis were semi-quantitative, this approach provides a path for overcoming cell culture and sample-to-sample variability and can therefore be utilized with many cell culture applications in order to understandcomplex and intricate process relationships

Nanosensors for regenerative medicine

Assessing biodistribution, fate, and function of implanted therapeutic cells in preclinical animal experiments is critical to realize safe, effective and efficient treatments for subsequent implementation within the clinic. Currently, tissue histology, the most prevalent analytical technique to meet this need, is limited by end-point analysis, high cost and long preparation time. Moreover, it is disadvantaged by an inability to monitor in real-time, qualitative interpretation and ethical issues arising from animal sacrifice. While genetic engineering techniques allow cells to express molecules with detectable signals (e.g., fluorescence, luminescence, T1 (spin–lattice)/T2 (spin–spin) contrast in magnetic resonance imaging, radionuclide), concerns arise regarding technical complexity, high-cost of genetic manipulation, as well as mutagenic cell dysfunction. Alternatively, cells can be labeled using nanoparticle-sensors—nanosensors that emit signals to identify cell location, status and function in a simple, cost-effective, and non-genetic manner. This review article provides the definition, classification, evolution, and applications of nanosensor technology and focuses on how they can be utilized in regenerative medicine. Several examples of direct applications include: (1) monitoring post-transplantation cell behavior, (2) revealing host response following foreign biomaterial implantation, and (3) optimization of cell bioprocess operating conditions. Incorporating nanosensors is expected to expedite the development of cell-based regenerative medicine therapeutics

An in silico erythropoiesis model rationalizing synergism between stem cell factor and erythropoietin

Stem cell factor (SCF) and erythropoietin (EPO) are two most recognized growth factors that play in concert to control in vitro erythropoiesis. However, exact mechanisms underlying the interplay of these growth factors in vitro remain unclear. We developed a mathematical model to study co-signaling effects of SCF and EPO utilizing the ERK1/2 and GATA-1 pathways (activated by SCF and EPO) that drive the proliferation and differentiation of erythroid progenitors. The model was simplified and formulated based on three key features: synergistic contribution of SCF and EPO on ERK1/2 activation, positive feedback effects on proliferation and differentiation, and cross-inhibition effects of activated ERK1/2 and GATA-1. The model characteristics were developed to correspond with biological observations made known thus far. Our simulation suggested that activated GATA-1 has a more dominant cross-inhibition effect and stronger positive feedback response on differentiation than the proliferation pathway, while SCF contributed more to the activation of ERK1/2 than EPO. A sensitivity analysis performed to gauge the dynamics of the system was able to identify the most sensitive model parameters and illustrated a contribution of transient activity in EPO ligand to growth factor synergism. Based on theoretical arguments, we have successfully developed a model that can simulate growth factor synergism observed in vitro for erythropoiesis. This hypothesized model can be applied to further computational studies in biological systems where synergistic effects of two ligands are seen