Presentation + Paper
6 April 2016 Monitoring stem cells in phase contrast imaging
K. P. Lam, K. P. Dempsey, D. J. Collins, J. B. Richardson
Author Affiliations +
Abstract
Understanding the mechanisms behind the proliferation of Mesenchymal Stem cells (MSCs) can offer a greater insight into the behaviour of these cells throughout their life cycles. Traditional methods of determining the rate of MSC differentiation rely on population based studies over an extended time period. However, such methods can be inadequate as they are unable to track cells as they interact; for example, in autologous cell therapies for osteoarthritis, the development of biological assays that could predict in vivo functional activity and biological action are particularly challenging. Here further research is required to determine non-histochemical biomarkers which provide correlations between cell survival and predictive functional outcome. This paper proposes using a (previously developed) advanced texture-based analysis algorithm to facilitate in vitro cells tracking using time-lapsed microscopy. The technique was adopted to monitor stem cells in the context of unlabelled, phase contrast imaging, with the goal of examining the cell to cell interactions in both monoculture and co-culture systems. The results obtained are analysed using established exploratory procedures developed for time series data and compared with the typical fluorescent-based approach of cell labelling. A review of the progress and the lessons learned are also presented.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. P. Lam, K. P. Dempsey, D. J. Collins, and J. B. Richardson "Monitoring stem cells in phase contrast imaging", Proc. SPIE 9711, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX, 97110E (6 April 2016); https://doi.org/10.1117/12.2211017
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Cited by 1 scholarly publication.
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KEYWORDS
Actinium

Image segmentation

Stem cells

Algorithm development

Biological research

Detection and tracking algorithms

Image analysis

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