1. Real-time prediction of cell division timing in developing zebrafish embryo
- Author
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Takashi Akanuma, Thomas N. Sato, Kazushi Ikeda, Yasuomi D. Sato, Tetsuo Sato, and Satoshi Kozawa
- Subjects
0301 basic medicine ,Multidisciplinary ,Microscopy, Confocal ,Cell division ,Embryonic Development ,Statistical model ,Bayes Theorem ,Computational biology ,Biology ,Bayesian inference ,Bioinformatics ,Neural stem cell ,Article ,Cell Line ,03 medical and health sciences ,Bayes' theorem ,030104 developmental biology ,Neural Stem Cells ,In vivo ,Zebrafish embryo ,Animals ,Time point ,Cell Division ,Zebrafish - Abstract
Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation–thus, presenting a new opportunity for in vivo experimental systems.
- Published
- 2016
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