Gradient biomimetic platforms for neurogenesis studies
There is a need for the development of new cellular therapies for the treatment of many diseases, with the central nervous system (CNS) currently an area of specific focus. Due to the complexity and delicacy of its biology, there is currently a limited understanding of neurogenesis and consequently a lack of reliable test platforms, resulting in several CNS based diseases having no cure. The ability to differentiate pluripotent stem cells into specific neuronal sub-types may enable scalable manufacture for clinical therapies, with a focus also on the purity and quality of the cell population. This focus is targeted towards an urgent need for the diseases that currently have no cure, e.g. Parkinson’s disease. Differentiation studies carried out using traditional 2D cell culture techniques are designed using biological signals and morphogens known to be important for neurogenesis in vivo. However, such studies are limited by their simplistic nature, including a general poor efficiency and reproducibility, high reagent costs and an inability to scale-up the process to a manufacture-wide design for clinical use. Biomimetic approaches to recapitulate a more in vivo-like environment are progressing rapidly within this field, with application of bio(chemical) gradients presented both as 2D surfaces and within a 3D volume. This review focusses on the development and application of these advanced extracellular environments particularly for the neural niche. We emphasise the progress that has been made specifically in the area of stem cell derived neuronal differentiation. Increasing developments in biomaterial approaches to manufacture stem cells will enable the improvement of differentiation protocols, enhancing the efficiency and repeatability of the process with a move towards up-scaling. Progress in this area brings these techniques closer to enabling the development of therapies for the clinic.
Funding
EPSRC and MRC Centre for Doctoral Training in Regenerative Medicine
Engineering and Physical Sciences Research Council
Find out more...History
School
- Science
Department
- Chemistry
Published in
Journal of Neural EngineeringVolume
19Issue
1Publisher
IOP PublishingVersion
- VoR (Version of Record)
Rights holder
© The Author(s)Publisher statement
This is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Acceptance date
2021-12-23Publication date
2022-01-24Copyright date
2022ISSN
1741-2560eISSN
1741-2552Publisher version
Language
- en