揭示内皮细胞边界异质性:VE-cadherin通过深层卷积神经网络坚持连接分层
Rudmer J Postma1, Susan E Fischer2, Roel Bijkerk1
1Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands.
PloS one
|January 6, 2025
概括
一个新的图像分析管道通过分析VE-Cadherin形态来准确分析内皮细胞 (EC) 屏障功能. 这种工具有助于理解全身性疾病,并分层患者对治疗的反应.
科学领域:
- 细胞生物学 细胞生物学
- 生物图像分析分析
- 计算病理学计算病理学
背景情况:
- 系统性疾病经常导致内皮细胞 (EC) 功能障碍,影响血液间隙空间屏障的完整性.
- VE-卡德林相互作用维持了EC屏障,但它们的形态是动态的,取决于环境.
- 评估EC屏障形态对于了解系统性血管疾病进展至关重要.
研究的目的:
- 开发一个自动化图像分析管道,用于EC形态的高内容选.
- 捕获多种VE-Cadherin表型并保存2D结构信息.
- 创建VE-Cadherin连接的1D张量表示,以避免正常化.
主要方法:
- 一个基于深度学习的图像分析管道,以卷积神经网络为中心.
- 从多个实验中对不同VE-Cadherin形态学进行网络培训.
- 在独立实验和ex vivo患者样本上验证模型的概括性.
主要成果:
- 该管道成功检测并分层了各种VE-Cadherin形态.
- 独立实验的一致结果证实了模型的普遍性.
- 启用肝硬化患者的分层,并将细胞边界变化与炎症因素联系起来.
结论:
- 开发了一种直观而强大的图像分析管道,用于分层VE-Cadherin形态.
- VE-Cadherin的形态概况可以比较刺激,选小分子,并评估疾病的进展.
- 这种方法为血管研究和临床应用提供了强大的工具.
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