Physical Biology/Physikalische Biologie (IZN, FB 15). Buchmann Institute for Molecular Life Sciences (BMLS). Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe-Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15, D-60438, Frankfurt am Main, Germany.
Justus-Liebig-Universität Gießen. Department of Insect Biotechnology in Plant Protection, Winchesterstraße 2, D-35394, Gießen, Germany.
Sci Data. 2022 Jun 15;9(1):340. doi: 10.1038/s41597-022-01443-x.
The Mediterranean fruit fly (medfly), Ceratitis capitata, is an important model organism in biology and agricultural research with high economic relevance. However, information about its embryonic development is still sparse. We share nine long-term live imaging datasets acquired with light sheet fluorescence microscopy (484.5 h total recording time, 373 995 images, 256 Gb) with the scientific community. Six datasets show the embryonic development in toto for about 60 hours at 30 minutes intervals along four directions in three spatial dimensions, covering approximately 97% of the entire embryonic development period. Three datasets focus on germ cell formation and head involution. All imaged embryos hatched morphologically intact. Based on these data, we suggest a two-level staging system that functions as a morphogenetic framework for upcoming studies on medfly. Our data supports research on wild-type or aberrant morphogenesis, quantitative analyses, comparative approaches to insect development as well as studies related to pest control. Further, they can be used to test advanced image processing approaches or to train machine learning algorithms and/or neuronal networks.
地中海实蝇（medfly），又称桔小实蝇，是生物学和农业研究中的一种重要模式生物，具有重要的经济意义。然而，关于其胚胎发育的信息仍然很少。我们向科学界共享了九个使用光片荧光显微镜获得的长期活体成像数据集（总记录时间为 484.5 小时，共 373995 张图像，256GB）。其中六个数据集以 30 分钟的间隔，沿四个方向在三个空间维度上展示了大约 60 小时的胚胎发育全过程，覆盖了整个胚胎发育周期的大约 97%。另外三个数据集则聚焦于生殖细胞的形成和头部的内卷。所有成像的胚胎均以形态完整的方式孵化。基于这些数据，我们提出了一个两级分期系统，该系统可作为即将进行的 medfly 研究的形态发生框架。我们的数据支持对野生型或异常形态发生、定量分析、昆虫发育的比较方法以及与害虫防治相关的研究。此外，这些数据还可用于测试先进的图像处理方法，或用于训练机器学习算法和/或神经元网络。
