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CochleaNet:基于深度学习的图像分析用于耳蜗连接组学和基因治疗

CochleaNet: deep learning-based image analysis for cochlear connectomics and gene therapy.

AI/ML耳科IF 12.7Q1

文献信息

中文摘要

随着针对耳聋的基因治疗和光遗传学治疗的出现,对耳蜗分子解剖学和生理学的全面分析变得越来越重要。在此,我们介绍CochleaNet,这是一个基于深度学习的框架,用于分析通过光片显微镜对脱钙、透明化和荧光标记的耳蜗所获得的三维成像数据。CochleaNet涵盖了从耳蜗重建到内毛细胞、螺旋神经节神经元及其传入突触分割,再到音调定位和特定荧光表达模式分析的工作流程。我们通过与人工图像分析比较验证了CochleaNet,并证明其优于先前的分割方法。随后,我们使用CochleaNet分析了不同的螺旋神经节神经元类型,并验证了不同的基因治疗产品。CochleaNet在高各向同性分辨率小鼠数据上训练后,也适用于沙鼠的耳蜗——另一种相关动物模型——以及来自市售显微镜的低分辨率小鼠和狨猴数据。我们得出结论,光片显微镜与CochleaNet图像分析的结合为快速可靠地量化耳蜗分子解剖学和临床前基因治疗结果铺平了道路。

英文摘要

With the emergence of gene and optogenetic therapies targeting deafness, the comprehensive analysis of the molecular anatomy and physiology of the cochlea has become ever more important. Here, we introduce CochleaNet, a deep learning-based framework to analyze volumetric imaging data obtained by light-sheet microscopy of decalcified, cleared and fluorescently labeled cochleae. CochleaNet covers the workflow from reconstruction of the cochlea to segmentation of inner hair cells, spiral ganglion neurons and their afferent synapses, to the analysis of tonotopy and specific fluorescent expression patterns. We validated CochleaNet by comparison to manual image analysis, also demonstrating its superiority to prior segmentation methods. We then analyzed different spiral ganglion neuron types and validated different gene therapy products with CochleaNet. Trained on high isotropic resolution mouse data, CochleaNet was also applicable to the cochlea of the gerbil, another relevant animal model, and lower-resolution mouse and marmoset data from a commercially available microscope. We conclude that the combination of light-sheet microscopy and image analysis with CochleaNet paves the way for rapid and reliable quantification of cochlear molecular anatomy and preclinical gene therapy outcomes.