The application of convolutional neural network to stem cell biology

Abstract

Induced pluripotent stem cells (iPSC) are one the most prominent innovations of medical research in the last few decades. iPSCs can be easily generated from human somatic cells and have several potential uses in regenerative medicine, disease modeling, drug screening, and precision medicine. However, further innovation is still required to realize their full potential. Machine learning is an algorithm that learns from large datasets for pattern formation and classification. Deep learning, a form of machine learning, uses a multilayered neural network that mimics human neural circuit structure. Deep neural networks can automatically extract features from an image, although classical machine learning methods still require feature extraction by a human expert. Deep learning technology has developed recently; in particular, the accuracy of an image classification task by using a convolutional neural network (CNN) has exceeded that of humans since 2015. CNN is now used to address several tasks including medical issues. We believe that CNN would also have a great impact on the research of stem cell biology. iPSCs are utilized after their differentiation to specific cells, which are characterized by molecular techniques such as immunostaining or lineage tracing. Each cell shows a characteristic morphology; thus, a morphology-based identification system of cell type by CNN would be an alternative technique. The development of CNN enables the automation of identifying cell types from phase contrast microscope images without molecular labeling, which will be applied to several researches and medical science. Image classification is a strong field among deep learning tasks, and several medical tasks will be solved by deep learning-based programs in the future.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

“…Meanwhile, it has been shown that DL techniques can holistically capture complex structural features for classification. This has found broad applications in detecting cell types (12)(13)(14), cell states (15)(16)(17)(18)22), drug response (19), and stem cell lineage (20). By fully leveraging the label-free and high multiplexing nature of our technique, it can potentially generate significant impacts in imaging cytometry by offering unprecedented information content and discovering new compound morphological features necessitating multiplexed fluorescence readout.…”

“…By doing so, multiple subcellular structures and cell states can be revealed simultaneously without physical labeling. While previous work has shown that DL models can disentangle the complex structures captured in the label-free data and make in-silico fluorescence labeling with high accuracy (10)(11)(12) or holistically capture "hidden" structural features that are not easily perceived or described (13)(14)(15)(16)(17)(18)(19)(20)(21)(22), these results are fundamentally limited by the weak structural contrast from the transmission modes that contain only forward scattering information. By exploiting the enhanced resolution and sensitivity in the backscattering data, we demonstrate a dramatic increase in the fluorescence prediction accuracy with up to 3× improvement as compared to the current state-of-the-art.…”

Aortic “Disease-in-a-Dish”: Mechanistic Insights and Drug Development Using iPSC-Based Disease Modeling

“…Innovations in robotics and machine learning could overcome these bottlenecks. For example, machine learning has been developed to identify cells in phase contrast based on morphology alone without the need for molecular labeling (Kusumoto and Yuasa, 2019). This technology, in conjunction with modular automated systems, could be powerful for processing large numbers of iPSC lines, including cells derived from severely affected lines, as it could potentially remove the need for an experienced "eye" when culturing cells.…”

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

“…However, deep learning is capable of tackling this limitation, as it can analyze huge volumes of data efficiently. According to Kusumoto & Yuasa (2019), molecular biology is another significant field where deep learning has potential, as each cell has unique morphological features. However, few research studies have applied CNNs to cell identification.…”