Blevins, N. H. | Search bioRxiv

bioRxiv2022

Single-cell transcriptomic atlas reveals increased regeneration in diseased human inner ears

Mammalian inner ear hair cell loss leads to permanent hearing and balance dysfunction. In contrast to the cochlea, vestibular hair cells of the murine utricle have some regenerative capacity. Whether human utricular hair cells regenerate remains unknown. Here we procured live, mature utricles from organ donors and vestibular schwannoma patients, and present a validated single-cell transcriptomic atlas at unprecedented resolution. We describe previously unknown markers of 25 sensory and non-sensory cell types, with genes of hair cell and supporting cell subtypes displaying striking divergence between mice and humans. We further uncovered transcriptomes unique to hair cell precursors, which we validated to be 14-fold more robust in vestibular schwannoma utricles, representing ongoing regeneration in humans. Lastly, trajectory analysis of the supporting cell-hair cell axis revealed 5 distinct patterns of dynamic gene expression and associated pathways, including mTOR signaling and synaptogenesis. Our dataset constitutes a foundational resource, accessible via a web-based interface, serving to advance knowledge of the normal and diseased human inner ears and tools to stimulate human inner ear regeneration.

cell biology↗

bioRxiv2019

Decoding Speech and Music Stimuli from the Frequency Following Response

The ability to differentiate complex sounds is essential for communication. Here, we propose using a machine-learning approach, called classification, to objectively evaluate auditory perception. In this study, we recorded frequency following responses (FFRs) from 13 normal-hearing adult participants to six short music and speech stimuli sharing similar fundamental frequencies but varying in overall spectral and temporal characteristics. Each participant completed a perceptual identification test using the same stimuli. We used linear discriminant analysis to classify FFRs. Results showed statistically significant FFR classification accuracies using both the full response epoch in the time domain (72.3% accuracy, p < 0.001) as well as real and imaginary Fourier coefficients up to 1 kHz (74.6%, p < 0.001). We classified decomposed versions of the responses in order to examine which response features contributed to successful decoding. Classifier accuracies using Fourier magnitude and phase alone in the same frequency range were lower but still significant (58.2% and 41.3% respectively, p < 0.001). Classification of overlapping 20-msec subsets of the FFR in the time domain similarly produced reduced but significant accuracies (42.3%-62.8%, p < 0.001). Participants mean perceptual responses were most accurate (90.6%, p < 0.001). Confusion matrices from FFR classifications and perceptual responses were converted to distance matrices and visualized as dendrograms. FFR classifications and perceptual responses demonstrate similar patterns of confusion across the stimuli. Our results demonstrate that classification can differentiate auditory stimuli from FFR responses with high accuracy. Moreover, the reduced accuracies obtained when the FFR is decomposed in the time and frequency domains suggest that different response features contribute complementary information, similar to how the human auditory system is thought to rely on both timing and frequency information to accurately process sound. Taken together, these results suggest that FFR classification is a promising approach for objective assessment of auditory perception.

neuroscience↗