Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks

Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with good and bad morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of similar to 144 mu m was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (similar to 15 mu m) and the entire colony (similar to 540 mu m). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.

Automated summary

Human pluripotent stem cells have great potential for research and therapy, but their maintenance in culture requires careful control of their pluripotent and clonal status. In this study, we developed a classifier using a convolutional neural network to analyze images of human embryonic stem cell colonies and predict their morphological phenotypes associated with pluripotency and clonality. The classifier showed high accuracy in predicting phenotype (89%) and the spatial scale of around 144 μm was found to be most informative for classification. Proteomic analysis also revealed molecular differences between cells with different phenotypes. This non-invasive automated analysis approach could be valuable for identifying developmental anomalies in pluripotent stem cells.