Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development

Abstract

Organoids, which can reproduce the complex tissue structures found in embryos, are revolutionizing basic research and regenerative medicine. In order to use organoids for research and medicine, it is necessary to assess the composition and arrangement of cell types within the organoid, i.e., spatial gene expression. However, current methods are invasive and require gene editing and immunostaining. In this study, we developed a non-invasive estimation method of spatial gene expression patterns using machine learning. A deep learning model was trained with an encoder-decoder architecture on a dataset of retinal organoids derived from mouse embryonic stem cells. This method successfully estimated spatially plausible fluorescent patterns with appropriate intensities, enabling the non-invasive, quantitative estimation of spatial gene expression patterns within each tissue. Thus, this method could lead to new avenues for evaluating spatial gene expression patterns across a wide range of biology and medicine fields. HighlightsO_LIA non-invasive estimation method of spatial gene expression pattern is proposed C_LIO_LIA CNN architecture is employed to convert a phase-contrast to fluorescence image C_LIO_LIThe method was trained on a dataset of mouse ESC-derived retinal organoids C_LIO_LISpatially plausible patterns of Rx gene expressions were successfully estimated C_LI