Jia Dongyu, Xu Qiuping, Xie Qian, Mio Washington, Deng Wu-Min
Department of Biological Science, Florida State University, Tallahassee, FL 32306-4370, USA.
Department of Mathematics, Florida State University, Tallahassee, FL 32306-4510, USA.
Sci Rep. 2016 Jan 6;6:18850. doi: 10.1038/srep18850.
The Drosophila egg chamber, whose development is divided into 14 stages, is a well-established model for developmental biology. However, visual stage determination can be a tedious, subjective and time-consuming task prone to errors. Our study presents an objective, reliable and repeatable automated method for quantifying cell features and classifying egg chamber stages based on DAPI images. The proposed approach is composed of two steps: 1) a feature extraction step and 2) a statistical modeling step. The egg chamber features used are egg chamber size, oocyte size, egg chamber ratio and distribution of follicle cells. Methods for determining the on-site of the polytene stage and centripetal migration are also discussed. The statistical model uses linear and ordinal regression to explore the stage-feature relationships and classify egg chamber stages. Combined with machine learning, our method has great potential to enable discovery of hidden developmental mechanisms.
果蝇卵室的发育分为14个阶段，是发育生物学中一个成熟的模型。然而，通过视觉确定阶段可能是一项繁琐、主观且耗时的任务，容易出错。我们的研究提出了一种客观、可靠且可重复的自动化方法，用于基于DAPI图像量化细胞特征并对卵室阶段进行分类。所提出的方法由两个步骤组成：1）特征提取步骤和2）统计建模步骤。所使用的卵室特征包括卵室大小、卵母细胞大小、卵室比率和卵泡细胞分布。还讨论了确定多线期位置和向心迁移的方法。统计模型使用线性回归和有序回归来探索阶段与特征之间的关系并对卵室阶段进行分类。结合机器学习，我们的方法具有发现隐藏发育机制的巨大潜力。
