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Title: Development of an image processing method for automated, non-invasive and scale-independent monitoring of adherent cell cultures
Author: Jaccard, N.
ISNI:       0000 0004 5364 8687
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Adherent cell culture is a key experimental method for biological investigations in diverse areas such as developmental biology, drug discovery and biotechnology. Light microscopy-based methods, for example phase contrast microscopy (PCM), are routinely used for visual inspection of adherent cells cultured in transparent polymeric vessels. However, the outcome of such inspections is qualitative and highly subjective. Analytical methods that produce quantitative results can be used but often at the expense of culture integrity or viability. In this work, an imaging-based strategy to adherent cell cultures monitoring was investigated. Automated image processing and analysis of PCM images enabled quantitative measurements of key cell culture characteristics. Two types of segmentation algorithms for the detection of cellular objects on PCM images were evaluated. The first one, based on contrast filters and dynamic programming was quick (<1s per 1280×960 image) and performed well for different cell lines, over a wide range of imaging conditions. The second approach, termed ‘trainable segmentation’, was based on machine learning using a variety of image features such as local structures and symmetries. It accommodated complex segmentation tasks while maintaining low processing times (<5s per 1280×960 image). Based on the output from these segmentation algorithms, imaging-based monitoring of a large palette of cell responses was demonstrated, including proliferation, growth arrest, differentiation, and cell death. This approach is non-invasive and applicable to any transparent culture vessel, including microfabricated culture devices where a lack of suitable analytical methods often limits their applicability. This work was a significant contribution towards the establishment of robust, standardised, and affordable monitoring methods for adherent cell cultures. Finally, automated image processing was combined with computer-controlled cultures in small-scale devices. This provided a first demonstration of how adaptive culture protocols could be established; i.e. culture protocols which are based on cellular response instead of arbitrary time points.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available