Laser Machining Surface Topographies for Stem Cell Control
Lead Research Organisation:
University of Southampton
Department Name: Optoelectronics Research Ctr (closed)
Abstract
Recent work has shown that stem-cell differentiation, specifically the transformation of stem cells into bone cells, can be controlled by growing stem cells on specific surface topographies. The use of such topographies holds the potential for using a patient's own stem cells for targeted generation of bone within the body, leading to faster recovery from bone fractures, reduced failure rates for hip implants and potential therapies for Osteoporosis and other bone diseases.
Preliminary results have shown success with surface topographies on the size scale of 100 nanometers to microns, patterned over millimeter sized areas via a lithographic fabrication process. However, this process is expensive and time-consuming and hence, to scale-up to medically useful dimensions, new fabrication processes must be developed.
Femtosecond pulse laser machining offers the potential for high speed and precise fabrication of features that are on the micron-scale and smaller. However, due to the acute sensitivity of the process, even small levels of experimental noise can result in inferior fabrication quality, preventing the ability to scale this process up to the required dimensions.
The proposed project has two objectives. Firstly, the development of an accurate and real-time monitoring system for the laser machining process, taking advantage of recent developments in machine learning, specifically neural networks, in order to enable creation of large-area, high precision fabrication techniques to machine suitable topographic substrates. Secondly, advancing the processes for optimal growth and monitoring of stem cells on these fabricated substrates in order to optimize the size, features, and dimensions of the surface topography for accurate control of stem cell differentiation and proliferation.
Preliminary results have shown success with surface topographies on the size scale of 100 nanometers to microns, patterned over millimeter sized areas via a lithographic fabrication process. However, this process is expensive and time-consuming and hence, to scale-up to medically useful dimensions, new fabrication processes must be developed.
Femtosecond pulse laser machining offers the potential for high speed and precise fabrication of features that are on the micron-scale and smaller. However, due to the acute sensitivity of the process, even small levels of experimental noise can result in inferior fabrication quality, preventing the ability to scale this process up to the required dimensions.
The proposed project has two objectives. Firstly, the development of an accurate and real-time monitoring system for the laser machining process, taking advantage of recent developments in machine learning, specifically neural networks, in order to enable creation of large-area, high precision fabrication techniques to machine suitable topographic substrates. Secondly, advancing the processes for optimal growth and monitoring of stem cells on these fabricated substrates in order to optimize the size, features, and dimensions of the surface topography for accurate control of stem cell differentiation and proliferation.
Organisations
Publications
Mackay BS
(2020)
Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.
in Tissue & cell
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509747/1 | 01/10/2016 | 30/09/2021 | |||
| 2115650 | Studentship | EP/N509747/1 | 01/07/2018 | 29/12/2021 | Benita MacKay |
| EP/R513325/1 | 01/10/2018 | 30/09/2023 | |||
| 2115650 | Studentship | EP/R513325/1 | 01/07/2018 | 29/12/2021 | Benita MacKay |
| Description | Laser-machined surfaces alter stem cell behaviour, and artificial Intelligence (AI) can be used to model how adult stem cells respond to physical microscale cues. AI can predict both stem cell response to unseen laser-machined topographical patterns, and also predict stem cell positioning in future time points. |
| Exploitation Route | New topographical patterns can be discovered without lengthy laboratory-based experimentation dependent on donated tissue samples. Future work should focus on nanotopographical cues, which have been shown to promote differentiation in stem cells. |
| Sectors | Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| URL | https://www.sciencedirect.com/science/article/pii/S0040816620303761 |
| Description | Traditionally laboratory-based experimentation can be transferred to AI-modelling, providing experimentation channels to those without the funding or the ability to access expensive biomedical research facilities. During the pandemic, when access to the research facilities was stopped due to lockdown, research on stem cell response to topographies was able to continue and be published from a home office, and the relationship between stem cells and parallel lines discovered without leaving the house. |
| First Year Of Impact | 2020 |
| Sector | Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Economic,Policy & public services |