Abstract
Actin stress fibers are abundant in cultured cells, but little is known about them in vivo. In podocytes, much evidence suggests that mechanobiological mechanisms underlie injury, with structural changes to actin stress fibers potentially responsible for pathological changes to cell morphology. However, this hypothesis is difficult to rigorously test in vivo due to challenges with visualization. This thesis presents the first visualization technique capable of resolving the three-dimensional (3D) cytoskeletal network in podocytes in detail while definitively identifying the proteins that comprise this network. Images acquired using membrane-extraction and focused ion beam scanning electron microscopy (FIB-SEM) were assembled and interpreted using machine learning image segmentation. Using isolated mouse glomeruli from healthy animals, we observed actin cables and intermediate filaments linking the interdigitated podocyte foot processes to newly described contractile actin structures located at the periphery of the cell body. Actin cables within foot processes formed a continuous, mesh-like, electron dense sheet that incorporated the slit diaphragms. Our new technique revealed, for the first time, the detailed 3D organization of actin networks in healthy podocytes. Our data are in agreement with the gel compression hypothesis regarding the glomerular filtration barrier and provide insight into how podocytes respond to mechanical cues from their surrounding environment.
Committee Chair
Guy GeninHani SuleimanAmit Pathak
Committee Members
Guy Genin Hani Suleiman Amit Pathak
Degree
Master of Science (MS)
Author's Department
Mechanical Engineering & Materials Science
Document Type
Thesis
Date of Award
Winter 1-5-2022
Language
English (en)
DOI
https://doi.org/10.7936/9rr9-1s66
Author's ORCID
https://orcid.org/
0000-0002-1719-8354
Recommended Citation
Qu, Chengqing, "Three-Dimensional Visualization of the Podocyte Actin Network Using Integrated Membrane Extraction, Election Microscopy, and Machine Learning" (2022). McKelvey School of Engineering Theses & Dissertations. 676.
The definitive version is available at https://doi.org/10.7936/9rr9-1s66