鼻黏膜上皮紧密连接自动化多维量化评估工具的开发及其在变应性鼻炎中的应用

An automated multidimensional quantitative assessment tool for tight junctions in nasal mucosal epithelium: development and application in allergic rhinitis

摘要

目的 开发一种基于深度学习的紧密连接（TJs）多维自动化定量分析插件，以解决上皮屏障形态学分析主观性强、通量低的方法学瓶颈，并利用变应性鼻炎（AR）体外模型进行验证。方法 基于 ImageJ 平台，整合 Cellpose 单细胞分割、感兴趣区域荧光量化及 Ridge Detection 线性结构检测算法，构建自动化多维图像分析流程。以原代人鼻上皮细胞（hNECs）为模型，分别经白细胞介素 13（IL-13）和 IL-17A 刺激后进行免疫荧光及透射电镜检测，对比自动化插件与传统人工分析的一致性，量化评估炎症因子对 TJs 的破坏程度。结果 本插件在荧光强度（ r = 0.995 4， P ＜ 0.000 1）、网状覆盖面积（ r = 0.996 4， P ＜ 0.000 1）、细胞周长（ r = 0.997 9， P ＜ 0.000 1）及细胞间隙宽度（ r = 0.972 7， P ＜ 0.000 1）4 个核心维度上，均与人工分析结果高度相关。IL-13 与 IL-17A 均可显著破坏 hNECs 的 TJs 网络结构，表现为细胞周长增加、细胞间隙异常增宽，本插件能够精确捕捉并量化上述病理性改变。结论 本研究开发的紧密连接自动化多维量化工具，可为呼吸道上皮物理屏障功能的标准化、自动化评估提供可靠的方法学保障。

Abstract

Objective To develop a deep learning-based plug-in for automated, multidimensional quantitative analysis of tight junctions (TJs), addressing methodological bottlenecks in epithelial barrier morphological analysis, such as strong subjectivity and low throughput, and to validate the tool using an in vitro model of allergic rhinitis (AR). Methods Based on the ImageJ platform, an automated multidimensional image-analysis workflow was established by integrating Cellpose-based single-cell segmentation, fluorescence quantification within region of interest, and Ridge Detection algorithms for linear-structure detection. Primary human nasal epithelial cells (hNECs) were used as the model and were stimulated with interleukin-13 (IL-13) and interleukin-17A (IL-17A), respectively, followed by immunofluorescence staining and transmission electron microscopy. The consistency between the automated plug-in and conventional manual analysis was evaluated, and the extent of TJ disruption induced by inflammatory cytokines was quantitatively assessed. Results The plug-in showed strong correlations with manual analysis across four core dimensions, including fluorescence intensity ( r = 0.995 4, P ＜ 0.000 1), network coverage area ( r = 0.996 4, P ＜ 0.000 1), cell perimeter ( r = 0.997 9, P ＜ 0.000 1), and intercellular gap width ( r = 0.972 7, P ＜ 0.000 1). Both IL-13 and IL-17A significantly disrupted the TJ network structure of hNECs, manifested by increased cell perimeter and abnormally widened intercellular gaps, and the plug-in accurately captured and quantified these pathological changes. Conclusion The automated multidimensional TJs quantification tool developed in this study provides a reliable methodological support for standardized and automated assessment of the physical barrier function of the respiratory epithelium.

关键词

深度学习 / 紧密连接 / 白细胞介素 13 / 白细胞介素 17A / 变应性鼻炎

Key words

Deep learning / Tight junctions / Interleukin-13 / Interleukin-17A / Allergic rhinitis

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基金资助

国家自然科学基金青年项目(82401331)

国家自然科学基金面上项目(82571289)