Tunable Supramolecular Hydrogels for Directing Stem Cell Differentiation
One of AFSBio’s partners, BioGelX, creates hydrogels/supramolecular hydrogels and bioInk for 3D cell culture work. The following paper shows that by simply adjusting the stiffness of their supramolecular hydrogel, they were able to differentiate MSCs/pericytes into neural cells (soft), muscle/cartilage cells (stiff), and bone cells (rigid) respectively. In this paper, the application was for identifying metabolites uniquely used during differentiation as drug candidates
Link to paper: https://www.sciencedirect.com/science/article/pii/S2451929416300535
Controlling the differentiation of stem cells with drugs, including small molecules, is highly desirable for eliciting targeted regeneration. However, it is hard to follow metabolite changes without adding bias from the media used or from the substrata on which the cells are cultured.
For example:
- Changing media formations to control growth/differentiation could add major artifacts to metabolite analysis and biological bias
- Most gels studied to date include bioactive groups, typically introduced through coating of the substrate with ECM or by chemical functionalization with bioactive groups (e.g. proteins or peptide motifs).
- Substrata used tend to differ in formulation, cross linking, and surface coating to achieve cell adhesion and control of stiffness.
Key Advantages of Supramolecular hydrogels for metabolite drug discovery:
A supramolecular hydrogel provides an idealized physical environment, rather than a conventional biochemical (use of growth factors or defined media) approach to trigger differentiation.
- No need to change media formations, no need for biofunctionalization, chemical cross-linking, or change in chemical composition.
- With supramolecular hydrogels, the stiffness and chemical composition can be varied systematically. This keeps growth cell-growth conditions as chemically identical as possible so as not to bias the metabolomics experiments.
- Retains the ability to target a range of lineages and follow the metabolite profile with time.
This is ideally suited for metabolomics experiments, where metabolites can be of very low abundance and changes can be subtle.