Abstract
Sensorineural hearing loss (SNHL) is caused by damage to the mechanosensory
hair cells and auditory neurons of the cochlea. The development of imaging tools
that can directly visualize or provide functional information about a patient’s
cochlear cells is critical to identify the pathobiological defect and determine the
cells’ receptiveness to emerging SNHL treatments. However, the cochlea’s small
size, embedded location within dense bone, and sensitivity to perturbation have
historically precluded high-resolution clinical imaging. Previously, we developed
micro-optical coherence tomography (μOCT) as a platform for otologic imaging
in animal models and human cochleae. Here we report on advancing μOCT
technology to obtain simultaneously acquired and co-localized images of cell
viability/metabolic activity through dynamic μOCT (DμOCT) imaging of intracellular
motion. DμOCT obtains cross-sectional images of ATP-dependent movement of
intracellular organelles and cytoskeletal polymerization by acquiring sequential
μOCT images and computing intensity fluctuation frequency metrics on a pixelwise
basis. Using a customized benchtop DμOCT system, we demonstrate the
detailed resolution of anatomical and metabolic features of cells within the organ
of Corti, via an apical cochleostomy, in freshly-excised adult mouse cochleae.
Further, we show that DμOCT is capable of capturing rapid changes in cochlear cell
metabolism following an ototoxic insult to induce cell death and actin stabilization.
Notably, as few as 6 frames can be used to reconstruct cochlear DμOCT images
with sufficient detail to discern individual cells and their metabolic state. Taken
together, these results motivate future development of a DμOCT imaging probe
for cellular and metabolic diagnosis of SNHL in humans.
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@article{schulz17dynamicb, title = {Dynamic micro-optical coherence tomography enables structural and metabolic imaging of the mammalian cochlea}, author = {Hinnerk Schulz-Hildebrandt and Svetolik Spasic and Fang Hou and Kuan-Chung Ting and Shelley Batts and Guillermo Tearney and Konstantina M Stankovic}, doi = {doi: 10.3389/fnmol.2024.1436837}, year = {2024}, date = {2024-10-09}, journal = {Frontiers in Molecular Neuroscience}, volume = {17}, pages = {1436837}, publisher = {Frontiers}, abstract = {Sensorineural hearing loss (SNHL) is caused by damage to the mechanosensory hair cells and auditory neurons of the cochlea. The development of imaging tools that can directly visualize or provide functional information about a patient’s cochlear cells is critical to identify the pathobiological defect and determine the cells’ receptiveness to emerging SNHL treatments. However, the cochlea’s small size, embedded location within dense bone, and sensitivity to perturbation have historically precluded high-resolution clinical imaging. Previously, we developed micro-optical coherence tomography (μOCT) as a platform for otologic imaging in animal models and human cochleae. Here we report on advancing μOCT technology to obtain simultaneously acquired and co-localized images of cell viability/metabolic activity through dynamic μOCT (DμOCT) imaging of intracellular motion. DμOCT obtains cross-sectional images of ATP-dependent movement of intracellular organelles and cytoskeletal polymerization by acquiring sequential μOCT images and computing intensity fluctuation frequency metrics on a pixelwise basis. Using a customized benchtop DμOCT system, we demonstrate the detailed resolution of anatomical and metabolic features of cells within the organ of Corti, via an apical cochleostomy, in freshly-excised adult mouse cochleae. Further, we show that DμOCT is capable of capturing rapid changes in cochlear cell metabolism following an ototoxic insult to induce cell death and actin stabilization. Notably, as few as 6 frames can be used to reconstruct cochlear DμOCT images with sufficient detail to discern individual cells and their metabolic state. Taken together, these results motivate future development of a DμOCT imaging probe for cellular and metabolic diagnosis of SNHL in humans.}, keywords = {}, pubstate = {published}, tppubtype = {article} }