Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation

ARIEL WAISMAN; ALEJANDRO LA GRECA; ALAN MIQUEAS MOBBS; MARÍA AGUSTINA SCARAFFÍA; NATALIA LUCIA SANTIN VELAZQUE; GABRIEL NEIMAN; MORO, LUCIA NATALIA; CARLOS LUZZANI; GUSTAVO SEVLEVER; ALEJANDRA GUBERMAN; SANTIAGO MIRIUKA

Stem Cell Reports

Cell Presssubs@cell.com

Año: 2019 vol. 12 p. 845 - 859

Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recog- nition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train aCNNwith transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus.We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.