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- Title
Deep learning for high-throughput quantification of oligodendrocyte ensheathment at single-cell resolution.
- Authors
Xu, Yu Kang T.; Chitsaz, Daryan; Brown, Robert A.; Cui, Qiao Ling; Dabarno, Matthew A.; Antel, Jack P.; Kennedy, Timothy E.
- Abstract
High-throughput quantification of oligodendrocyte myelination is a challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a high-throughput method to assess oligodendrocyte ensheathment in-vitro, combining nanofiber culture devices and automated imaging with a heuristic approach that informed the development of a deep learning analytic algorithm. The heuristic approach was developed by modeling general characteristics of oligodendrocyte ensheathments, while the deep learning neural network employed a UNet architecture and a single-cell training method to associate ensheathed segments with individual oligodendrocytes. Reliable extraction of multiple morphological parameters from individual cells, without heuristic approximations, allowed the UNet to match the accuracy of expert-human measurements. The capacity of this technology to perform multi-parametric analyses at the level of individual cells, while reducing manual labor and eliminating human variability, permits the detection of nuanced cellular differences to accelerate the discovery of new insights into oligodendrocyte physiology. Yu Kang T Xu et al. combined nanofiber culture methods, automated imaging, and analytic algorithms to develop a quantitative high-throughput system to measure oligodendrocyte ensheathment. The analytic methods employed include a heuristic approach to model oligodendrocyte ensheathment characteristics and a deep learning neural network to recognize ensheathment by individual oligodendrocytes.
- Subjects
DEEP learning; OLIGODENDROGLIA; MYELIN; NEURAL circuitry; NANOFIBERS
- Publication
Communications Biology, 2019, Vol 2, Issue 1, pN.PAG
- ISSN
2399-3642
- Publication type
Article
- DOI
10.1038/s42003-019-0356-z