Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Mayr, C; Beyreis, M; Dobias, H; Gaisberger, M; Pichler, M; Ritter, M; Jakab, M; Neureiter, D; Kiesslich, T.
Miniaturization of the Clonogenic Assay Using Confluence Measurement.
Int J Mol Sci. 2018; 19(3):
Doi: 10.3390/ijms19030724
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- Co-Autor*innen der Med Uni Graz
- Pichler Martin
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- Abstract:
- The clonogenic assay is a widely used method to study the ability of cells to 'infinitely' produce progeny and is, therefore, used as a tool in tumor biology to measure tumor-initiating capacity and stem cell status. However, the standard protocol of using 6-well plates has several disadvantages. By miniaturizing the assay to a 96-well microplate format, as well as by utilizing the confluence detection function of a multimode reader, we here describe a new and modified protocol that allows comprehensive experimental setups and a non-endpoint, label-free semi-automatic analysis. Comparison of bright field images with confluence images demonstrated robust and reproducible detection of clones by the confluence detection function. Moreover, time-resolved non-endpoint confluence measurement of the same well showed that semi-automatic analysis was suitable for determining the mean size and colony number. By treating cells with an inhibitor of clonogenic growth (PTC-209), we show that our modified protocol is suitable for comprehensive (broad concentration range, addition of technical replicates) concentration- and time-resolved analysis of the effect of substances or treatments on clonogenic growth. In summary, this protocol represents a time- and cost-effective alternative to the commonly used 6-well protocol (with endpoint staining) and also provides additional information about the kinetics of clonogenic growth.
- Find related publications in this database (Keywords)
- clonogenic assay
- clonogenic growth
- 96-well microplate format
- microplate reader
- confluence detection