A high-throughput method for quantifying Drosophila fecundity.
A high-throughput method for quantifying Drosophila fecundity.
复制标题
量化果蝇繁殖力的高通量方法。
DOI：
10.1101/2024.03.27.587093
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Nystul,ToddG
中科院分区：
文献类型：
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作者：
Gomez,Andreana;Gonzalez,Sergio;Oke,Ashwini;Luo,Jiayu;Duong,JohnnyB;Esquerra,RaymondM;Zimmerman,Thomas;Capponi,Sara;Fung,JenniferC;Nystul,ToddG
果蝇（Drosophila melanogaster）是一种实验上易于处理的模型系统，最近作为一种强大的化学安全测试“新方法方法论”（NAM）而出现。由于卵子发生在分子和细胞水平上都是保守的，所以对果蝇繁殖力的测量可以用于识别影响物种生殖健康的化学物质。然而，标准的果蝇繁殖分析很难以高通量的方式进行，因为必须仔细控制实验因素，如果蝇的生理状态和环境线索，以获得一致的结果。此外，将苍蝇暴露在大量不同的实验条件下（如饮食中的化学添加剂）并手动计算产卵数量以确定对繁殖力的影响是费时的。我们已经克服了这些挑战，将新的多孔苍蝇培养策略与新型3d打印苍蝇转移设备相结合，可以快速准确地将苍蝇从一个盘子转移到另一个盘子，RoboCam是一种低成本的定制机器人相机，可以自动捕获井的图像，以及图像分割管道，可以自动识别和量化卵。我们表明，该方法在整个分析期间与稳健和一致的产卵相兼容，并证明用于量化繁殖力的自动化管道非常准确（自动产卵数与地面真相之间的相关性r2= 0.98）。此外，我们表明，该方法可以有效地检测饮食暴露于化学物质对繁殖力的影响。综上所述，这一策略大大提高了需要将苍蝇暴露于多种不同培养基条件下的高通量产卵试验的效率和可重复性。
The fruit fly,Drosophila melanogaster, is an experimentally tractable model system that has recently emerged as a powerful “new approach methodology” (NAM) for chemical safety testing. As oogenesis is well conserved at the molecular and cellular level, measurements ofDrosophilafecundity can be useful for identifying chemicals that affect reproductive health across species. However, standardDrosophilafecundity assays have been difficult to perform in a high-throughput manner because experimental factors such as the physiological state of the flies and environmental cues must be carefully controlled to achieve consistent results. In addition, exposing flies to a large number of different experimental conditions (such as chemical additives in the diet) and manually counting the number of eggs laid to determine the impact on fecundity is time-consuming. We have overcome these challenges by combining a new multiwell fly culture strategy with a novel 3D-printed fly transfer device to rapidly and accurately transfer flies from one plate to another, the RoboCam, a low-cost, custom-built robotic camera to capture images of the wells automatically, and an image segmentation pipeline to automatically identify and quantify eggs. We show that this method is compatible with robust and consistent egg laying throughout the assay period and demonstrate that the automated pipeline for quantifying fecundity is very accurate (r2= 0.98 for the correlation between the automated egg counts and the ground truth). In addition, we show that this method can be used to efficiently detect the effects on fecundity induced by dietary exposure to chemicals. Taken together, this strategy substantially increases the efficiency and reproducibility of high-throughput egg-laying assays that require exposing flies to multiple different media conditions.