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

Overview

Journal Stem Cell Reports

Publisher Cell Press

Specialty Cell Biology

Date 2019 Mar 19

PMID 30880077

Citations 41

Authors

Ariel Waisman

Alejandro La Greca

Alan M Mobbs

Maria Agustina Scarafia

Natalia L Santin Velazque

Gabriel Neiman

Lucia N Moro

Carlos Luzzani

Gustavo E Sevlever

Alejandra S Guberman

Santiago G Miriuka

Affiliations

Soon will be listed here.

Abstract

Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with 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.

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