Abstract
Abstract1. Comparative anatomy is an important tool for investigating evolutionary relationships amongst 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.2. 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/mm3at 600 nm3resolution. 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.3. 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.4. 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.
Publisher
Cold Spring Harbor Laboratory