Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images.

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images. We also propose a U-net structure to extract extraembryonic regions on a micropattern, and an algorithm to compares intensities of the fluorescence images staining respective germ-layer cells and extract their regions. The proposed method thus can quantify the pluripotency of a hiPSC line with spatial patterning including cell numbers, areas and distributions of germ-layer and extraembryonic cells on a micropattern, and reveal the formation process of hiPSCs and germ layers in the early differentiation stage by segmenting live-cell bright-field images. In our assay, the cell-number accuracy achieved 86% and 85%, and the cell region accuracy 89% and 81% for segmenting Hoechst and bright-field micropattern images, respectively. Applications to micropattern images of multiple hiPSC lines, micropattern sizes, groups of markers, living and fixed cells show the proposed method can be expected to be a useful protocol and tool to quantify pluripotency of a new hiPSC line before providing it to the scientific community.

Large-scale cultivation of human iPS cells in bioreactor with reciprocal mixing.

A substantial number of human iPS cells (hiPSCs) is needed for cell therapy to be successful against various diseases. We previously reported on a bioreactor with reciprocal mixing that produces specific physical properties that differ from those of conventional bioreactors with rotary paddle stirring. Moreover, such reactors not only provide a homogeneous environment but also allow the control of spheroid size by changing the mixing speed. In this study, we applied this bioreactor to the large-scale cultivation of hiPSCs. Approximately 10 billion hiPSCs were obtained from 2.0 L of culture, and the high expression of pluripotency markers was maintained. Our findings indicate that a bioreactor with reciprocal mixing can be used for large-scale hiPSC cultivation.

Modeling of Retina and Optic Nerve Ischemia-Reperfusion Injury through Hypoxia-Reoxygenation in Human Induced Pluripotent Stem Cell-Derived Retinal Ganglion Cells.

Retinal ganglion cells (RGCs) are specialized projection neurons that constitute part of the retina, and the death of RGCs causes various eye diseases, but the mechanism of RGC death is still unclear. Here, we induced cell death in human induced pluripotent stem cell (hiPSC)-derived RGC-rich retinal tissues using hypoxia-reoxygenation in vitro. Flow cytometry, immunochemistry, and Western blotting showed the apoptosis and necrosis of RGCs under hypoxia-reoxygenation, and they were rescued by an apoptosis inhibitor but not by a necrosis inhibitor. This revealed that the cell death induced in our model was mainly due to apoptosis. To our knowledge, this is the first model to reproduce ischemia-reperfusion in hiPSC-derived RGCs. Thus, the efficacy of apoptosis inhibitors and neuroprotective agents can be evaluated using this model, bringing us closer to clinical applications.