Morphological principles of neuronal mitochondria. Issue 6 (1st November 2021)

• Record Type: Journal Article
• Authors: Mendelsohn, Rachel; Garcia, Guadalupe C.; Bartol, Thomas M.; Lee, Christopher T.; Khandelwal, Priya; Liu, Emily; Spencer, Donald J.; Husar, Adam; Bushong, Eric A.; Phan, Sebastien; Perkins, Guy; Ellisman, Mark H.; Skupin, Alexander; Sejnowski, Terrence J.; Rangamani, Padmini
• Is Part Of: Journal of comparative neurology. Volume 530:Issue 6(2022)
• Page Start: 886
• Page End: 902
• Publication Date: 2021-11-01
• Subjects: EM tomography -- energetics -- mitochondria -- morphology -- neuronal; Comparative neurobiology -- Periodicals; Neurology -- Periodicals; 616
• DOI: 10.1002/cne.25254
• Languages: English
• ISSNs: 0021-9967

Abstract

Abstract: In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like adenosine triphosphate and heat often represent mitochondria as idealized geometries, and therefore, can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial transmission electron microscopy (TEM) tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.64 nanometer isotropic voxels. The resulting in‐silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs present across this population of mitochondria, which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons. Abstract : High quality 3D reconstructions of mitochondria were generated using serial TEM tomography images of axonal and presynaptic mitochondria, producing minimal distortion from the original images. The resulting in‐silico representations were subsequently quantified by differential geometry methods in terms of curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. These methods enable structural motifs to be identified across the sampled population, presenting useful information for future physiological modeling of neuronal mitochondria.