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Shioka, I.

Publications and source records attributed to Shioka, I..

2 recordsLinked to original sources

Ex vivo SIM-AFM measurements reveal the spatial correlation of stiffness and molecular distributions in 3D living tissue

Living tissues each exhibit a distinct stiffness, which provides cells with key environmental cues that regulate their behaviors. Despite this significance, our understanding of the spatiotemporal dynamics and the biological roles of stiffness in three-dimensional tissues is currently limited due to a lack of appropriate measurement techniques. To address this issue, we propose a new method combining upright structured illumination microscopy (USIM) and atomic force microscopy (AFM) to obtain precisely coordinated stiffness maps and biomolecular fluorescence images of thick living tissue slices. Using mouse embryonic skin as a representative tissue with mechanically heterogeneous structures inside, we validate the measurement principle of USIM-AFM. Live measurement of tissue stiffness distributions revealed the highly heterogeneous mechanical nature of embryonic skin as well as the role of collagens in maintaining its integrity. Furthermore, quantitative comparisons of stiffness distributions of preserved tissue samples unveiled the distinct impacts of conventional tissue preservation techniques on the tissue stiffness pattern. This series of experiments highlights the importance of live mechanical testing of tissue-scale samples. Our USIM-AFM technique provides a new methodology to reveal the dynamic nature of tissue stiffness and its correlation with biomolecular distributions in live tissues and thus could serve as a technical basis for exploring tissue-scale mechanobiology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/595975v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@8a32f5org.highwire.dtl.DTLVardef@117a350org.highwire.dtl.DTLVardef@e2fb99org.highwire.dtl.DTLVardef@1f41d24_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

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

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

developmental biology↗