SLAS 2019 AI in Process Automation Symposium

Automated manufacturing of induced pluripotent stem cells (iPSCs) overcomes critical restrictions on scalability and technical variability that otherwise preclude the use of iPSCs in large-scale population studies. Following reprogramming from adult cells, a critical step in ensuring cell line viability is the isolation and expansion of a single stem cell, a step referred to as monoclonalization. Monoclonality is generally verified via manual analysis of microscopic images, a highly time- consuming process which relies upon human judgement, incurring a key barrier to standardization and scalability. Here we report the design of a deep learning workflow which reliably identifies monoclonality and ascribes colony viability. The workflow, termed Monoqlo, integrates multiple convolutional neural networks and, critically, leverages the time seriality of the cell culturing process. This work highlights an example of the way in which human-directed design can often overcome obstacles in machine learning engineering in cases where a paucity of training data precludes the use of “more data, better results” strategies.