Abstract:
Comparative anatomy is an important tool for investigating evolutionary relationships among species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. We describe a method using synchrotron source micro-x-ray computed tomography (syn-μXCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of ~15 min/mm3 at 600 nm3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species-independent manner thus removing any barrier to imaging any species of interest. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.
摘要:
比较解剖学是研究物种之间进化关系的重要工具,但是缺乏可扩展的成像工具和染色剂来快速绘制相关物种的微观解剖结构,这对使用比较解剖学方法来识别进化适应性构成了主要障碍。我们描述了一种使用同步加速器源微X射线计算机断层扫描(syn-μXCT)结合机器学习算法对鳞翅目进行高通量成像的方法(即,蝴蝶和蛾)的眼睛。我们的管道允许在600nm3分辨率下以〜15min/mm3的速率成像。使用标准电子显微镜标记方法生成图像对比度(例如,四氧化锇)以不依赖物种的方式无偏标记所有细胞膜,从而消除了对任何感兴趣物种进行成像的任何障碍。为了证明该方法的强大功能,我们分析了蝴蝶晶锥的三维形态,与敏锐度和敏感度相关的视觉系统的一部分,在六个蝴蝶个体中发现了显着变化。尽管有这种变化,优化的经典措施,眼间角度与眼间分辨率之比,在很大程度上同意关于跨物种的眼睛几何形状的早期工作。我们证明该方法可以成功地用于确定复眼组织和晶锥形态。我们新颖的管道提供了快速,可应用于任何节肢动物物种的眼睛解剖结构的可扩展可视化和分析,提出了关于复眼和超越的进化适应的新问题。