Biomechanics Research Unit, GIGA In Silico Medicine, University of Liege, CHU - BAT 34, Quartier Hopital, Liege, Belgium; Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium.
Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; M3-BIORES, KU Leuven, Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium.
Cytotherapy. 2020 Feb;22(2):82-90. doi: 10.1016/j.jcyt.2019.12.006. Epub 2020 Jan 25.
Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics.
In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics.
Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups.
In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.
人类间充质基质细胞（hMSCs）已成为先进的医学细胞治疗的有吸引力的候选者。通常使用体外扩增步骤来达到所需的临床数量。然而，这受到许多变量的影响，包括供体特征，如年龄和性别，以及培养条件，如细胞接种密度和可用的培养表面积。计算建模，特别是机器学习，可以在破译个体供体特征与其生长动力学之间的关系方面发挥重要作用。
在这项研究中，研究了来自 174 名年龄在 3 至 64 岁之间的男性和女性供体的 hMSCs，其传代数从 2 到 27。我们应用随机森林（RF）技术来通过预测每个传代的群体倍增时间（PDT）来对细胞扩增过程进行建模，同时考虑到个体供体相关特征。
使用 RF 模型，与理论估计或历史数据相比，1 至 4 代 PDT 的模型预测与实验结果之间的平均绝对误差明显更低。此外，统计分析表明，不同年龄组的 PD 和 PDT 存在显著差异，尤其是在最年轻的组（10 岁以下）与其他年龄组相比。
总之，我们引入了一种基于个体供体特征的体外细胞扩增动力学预测计算模型，这种方法可以极大地协助细胞扩增培养过程的自动化。
