Dempsey, Katherine (2017) Monitoring Individual Cells within Cell Cultures using Image Processing and Pattern Recognition Techniques. Doctoral thesis, Keele University.

[thumbnail of DempseyPhD2017.pdf]

Download (4MB) | Preview


Cells are the building blocks of the human body which are normally specialised by type in accordance with their function. Human cells interact with each other to form the tissues that make up the body. Consequently, it is important to study the behaviour and interactions of these cells at the microscale level, so that the causes of cellular irregularities can be identified; and, possible treatments can be devised. This project aimed to create algorithms that were capable of tracking a variety of cells types within both single cultures and mixed cultures, and from this generate data that was relevant to current clinical trials. There have been successes in tracking some cells types, most notably articular chondrocytes and spinal disk cells. In terms of data generated there has been successes in a whole variety of different types of clinical trials. The algorithms used here have been able to identify the point of mitosis. They have created a better method of determining neural growth and from this have shown that neurons co-cultured with MCSs can grow in places with neural inhibitors. Through the use of algorithms that can analyse culture in three dimensional structures it has been shown that neurons are more affected by topographical cues than chemical cues in their direction of growth. It has also been shown that vesicles are more likely to appear on smaller back disk cells. In the study of gels, it has been found that the more transparent gels are better for imaging. Finally, it has been shown that MSCs and chondrocytes behave differently when in single and co-cultures. These discoveries would not have been possible without the use of the algorithms that allowed for the study of individual cells within a larger culture.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Lisa Bailey
Date Deposited: 06 Nov 2017 09:37
Last Modified: 06 Nov 2017 09:37

Actions (login required)

View Item
View Item