Abstract

The replenishable soft agar colony technique in test-tubes, first used in 1976 by Courtenay et al., is based on observations on the growth requirements of cells taken directly from excised tumours. Probably the most important feature of the method is the use of low oxygen concentrations in the gas phase, together with the addition of rat red blood cells (RBC) and the replenishment of the nutrients in the agar by adding liquid medium. Under standard culture conditions in air-CO2 incubators it has been a general observation, both in monolayer and in agar, that primary cultures of tumour cells fail when plated out at low cell densities but may survive when the number of cells is increased. A density of between 2 × 105 and 5 × 105 cells per millilitre (Hamburger and Salmon 1977) has therefore commonly been used. However, for colony assays such high cells numbers may lead to difficulties due to early exhaustion of the medium, limiting the growth of potentially clonogenic cells. The apparent need for high cell densities has been thought to be due to the conditioning of the medium by the release of various normal metabolites from large numbers of cells, thus modifying the biochemical constitution of the medium. But another consequence of cellular metabolic activity is the uptake of O2, and in static culture at high cell density the O2 concentration within the medium is substantially reduced. CO2 produced at the same time is more rapidly lost by diffusion but causes some reduction in pH. The effect of subatmospheric O2 concentrations in promoting the growth of normal mouse embryo cells was first reported in 1972 by Richter et al. Then, in 1975, Courtenay developed an agar colony method for Lewis Lung mouse tumour studies (Shipley et al. 1975), which was based on the use of low oxygen levels.KeywordsColony FormationSoft AgarCell Survival CurveNucleate Blood CellHuman Tumour Cell ColoniThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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