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基于光组织透明化和机器学习的耳蜗细胞图谱绘制。

Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning.

机构信息

Department of Cellular Neurobiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Department of Otolaryngology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

出版信息

Elife. 2019 Jan 18;8:e40946. doi: 10.7554/eLife.40946.

Abstract

The highly organized spatial arrangement of sensory hair cells in the organ of Corti is essential for inner ear function. Here, we report a new analytical pipeline, based on optical clearing of tissue, for the construction of a single-cell resolution map of the organ of Corti. A sorbitol-based optical clearing method enabled imaging of the entire cochlea at subcellular resolution. High-fidelity detection and analysis of all hair cell positions along the entire longitudinal axis of the organ of Corti were performed automatically by machine learning-based pattern recognition. Application of this method to samples from young, adult, and noise-exposed mice extracted essential information regarding cellular pathology, including longitudinal and radial spatial characteristics of cell loss, implying that multiple mechanisms underlie clustered cell loss. Our method of cellular mapping is effective for system-level phenotyping of the organ of Corti under both physiological and pathological conditions.

摘要

毛细胞在耳蜗中的高度有序排列对于内耳功能至关重要。在这里,我们报告了一种新的分析流程,基于组织光学透明化,构建耳蜗的单细胞分辨率图谱。基于山梨醇的光学透明化方法可实现亚细胞分辨率的整个耳蜗成像。通过基于机器学习的模式识别,自动进行高保真检测和分析整个耳蜗纵轴上所有毛细胞的位置。该方法应用于来自年轻、成年和噪声暴露的小鼠的样本,提取了有关细胞病理学的基本信息,包括细胞丢失的纵向和径向空间特征,这意味着多种机制导致了簇状细胞丢失。我们的细胞映射方法对于生理和病理条件下耳蜗的系统水平表型分析是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e6a/6338463/7a18c654bc63/elife-40946-fig1.jpg

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