Laser Machining Surface Topographies for Stem Cell Control

Lead Research Organisation: University of Southampton
Department Name: Optoelectronics Research Centre

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.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509747/1 30/09/2016 29/09/2021
2115650 Studentship EP/N509747/1 30/06/2018 29/12/2021 Benita MacKay
EP/R513325/1 30/09/2018 29/09/2023
2115650 Studentship EP/R513325/1 30/06/2018 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