Exploring Honeybee Abdominal Anatomy through Micro-CT and Novel Multi-Staining Approaches.
Jessica Carreira De PaulaKevin DoelloCristina MesasGaryfallia KapravelouAlberto Cornet-GomezFrancisco José OrantesRosario MartínezFátima LinaresJose Carlos PradosJesús María Porres FoulquieAntonio OsunaLuis Miguel de PablosPublished in: Insects (2022)
Continuous improvements in morphological and histochemical analyses of Apis mellifera could improve our understanding of the anatomy and physiology of these insects at both the cellular and tissue level. In this work, two different approaches have been performed to add new data on the abdomen of worker bees: (i) Micro-computed tomography (Micro-CT), which allows the identification of small-scale structures (micrometers) with adequate/optimal resolution and avoids sample damage and, (ii) histochemical multi-staining with Periodic Acid-Schiff-Alcian blue, Lactophenol-Saphranin O and pentachrome staining to precisely characterize the histological structures of the midgut and hindgut. Micro-CT allowed high-resolution imaging of anatomical structures of the honeybee abdomen with particular emphasis on the proventriculus and pyloric valves, as well as the connection of the sting apparatus with the terminal abdominal ganglia. Furthermore, the histochemical analyses have allowed for the first-time description of ventricular telocytes in honeybees, a cell type located underneath the midgut epithelium characterized by thin and long cytoplasmic projections called telopodes. Overall, the analysis of these images could help the detailed anatomical description of the cryptic structures of honeybees and also the characterization of changes due to abiotic or biotic stress conditions.
Keyphrases
- high resolution
- computed tomography
- dual energy
- image quality
- contrast enhanced
- positron emission tomography
- magnetic resonance imaging
- mass spectrometry
- flow cytometry
- oxidative stress
- deep learning
- aedes aegypti
- machine learning
- high speed
- magnetic resonance
- convolutional neural network
- single molecule
- coronary artery disease
- left ventricular
- atrial fibrillation
- heat stress
- photodynamic therapy