著者

Ayako Imanishi Tomokazu Murata Masaya Sato Kazuhiro Hotta Itaru Imayoshi Michiyuki Matsuda Kenta Terai

出版者

Japan Society for Cell Biology

雑誌

Cell Structure and Function (ISSN:03867196)

巻号頁・発行日

vol.43, no.2, pp.129-140, 2018 (Released:2018-08-10)

参考文献数

49

被引用文献数

3 10

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For more than a century, hematoxylin and eosin (H&E) staining has been the de facto standard for histological studies. Consequently, the legacy of histological knowledge is largely based on H&E staining. Due to the recent advent of multi-photon excitation microscopy, the observation of live tissue is increasingly being used in many research fields. Adoption of this technique has been further accelerated by the development of genetically encoded biosensors for ions and signaling molecules. However, H&E-based histology has not yet begun to fully utilize in vivo imaging due to the lack of proper morphological markers. Here, we report a genetically encoded fluorescent marker, NuCyM (Nucleus, Cytosol, and Membrane), which is designed to recapitulate H&E staining patterns in vivo. We generated a transgenic mouse line ubiquitously expressing NuCyM by using a ROSA26 bacterial artificial chromosome (BAC) clone. NuCyM evenly marked the plasma membrane, cytoplasm and nucleus in most tissues, yielding H&E staining-like images. In the NuCyM-expressing cells, cell division of a single cell was clearly observed as five basic phases during M phase by three-dimensional imaging. We next crossed NuCyM mice with transgenic mice expressing an ERK biosensor based on the principle of Förster resonance energy transfer (FRET). Using NuCyM, ERK activity in each cell could be extracted from the FRET images. To further accelerate the image analysis, we employed machine learning-based segmentation methods, and thereby automatically quantitated ERK activity in each cell. In conclusion, NuCyM is a versatile cell morphological marker that enables us to grasp histological information as with H&E staining.Key words: in vivo imaging, histology, machine learning, molecular activity