School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK.
Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK.
Sci Rep. 2019 Oct 25;9(1):15299. doi: 10.1038/s41598-019-51897-0.
Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, it is important to ensure that each colony originates from a single founding cell and to avoid subsequent merging of colonies during their growth. Clonal homogeneity can be obtained with low seeding densities; however, this leads to low yield and viability. It is therefore important to quantitatively assess how seeding density affects clonality loss so that experimental protocols can be optimised to meet the required standards. Here we develop a quantitative framework for modelling the growth of hESC colonies from a given seeding density based on stochastic exponential growth. This allows us to identify the timescales for colony merges and over which colony size no longer predicts the number of founding cells. We demonstrate the success of our model by applying it to our own experiments of hESC colony growth; while this is based on a particular experimental set-up, the model can be applied more generally to other cell lines and experimental conditions to predict these important timescales.
人类胚胎干细胞(hESCs)和诱导多能干细胞(iPSCs)具有很有前景的临床应用,这些应用通常依赖于克隆同源的细胞群体。为了实现这一目标,重要的是要确保每个集落都起源于单个创始细胞,并避免在生长过程中集落的后续融合。通过低接种密度可以获得克隆同源性;然而,这会导致产量和活力降低。因此,重要的是定量评估接种密度如何影响克隆性丧失,以便优化实验方案以满足所需的标准。在这里,我们开发了一种基于随机指数增长的定量框架,用于从给定的接种密度来模拟 hESC 集落的生长。这使我们能够确定集落融合的时间尺度,以及在多长时间内集落的大小不再预测创始细胞的数量。我们通过将我们的模型应用于我们自己的 hESC 集落生长实验来证明我们的模型的成功;虽然这是基于特定的实验设置,但该模型可以更普遍地应用于其他细胞系和实验条件,以预测这些重要的时间尺度。