Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes

Area of Science:

• Nanotechnology
• Materials Science
• Entomology

Background:

• Leafhoppers (Hemiptera: Cicadellidae) create brochosomes, hierarchically structured nanoparticles, for cuticle protection.
• Brochosome structure-function relationships and species-specific variations remain incompletely understood.

Purpose of the Study:

• To investigate the structural and mechanical properties of leafhopper brochosomes.
• To elucidate the nanoscale structure and properties using a multimodal characterization approach.
• To explore correlations in brochosome structure across leafhopper species using machine learning.

Main Methods:

• Atomic force microscopy (AFM) for nanomechanical mapping.
• Electron microscopy (EM) and electron tomography for structural analysis.
• Machine learning (ML)-based quantification of scanning electron microscopy (SEM) data.

Main Results:

• Brochosomes are rigid, hollow spheres with dimensions and morphologies dependent on leafhopper species.
• Nanomechanical mapping revealed a compression modulus of 1-3 GPa, indicative of crystalline proteins.
• ML analysis identified structural correlations across multiple leafhopper species.

Conclusions:

• Advanced microscopy and ML provide a comprehensive understanding of brochosome structure and mechanics.
• Brochosome properties are species-specific and linked to their protective functions.
• The employed ML tools offer broad applicability for analyzing nanostructured biological materials.