无需荧光标记即可实时评估微血管网络连通性，助力类器官和肿瘤研究。
Label-free Prediction of Vascular Connectivity in Perfused Microvascular Networks in vitro
- 基于对比学习与不平衡数据处理的VC-Net模型，实现无标记血管连通性分析。
- 在肿瘤微环境下，血管连通性平均下降30.8%，非连通区域增加37.3%。
- 适用于类器官、肿瘤血管化研究，支持长期动态监测。
持续监测和原位评估微血管连通性对培养血管化类器官及优化治疗策略具有重要意义。然而，现有方法多依赖荧光标记，可能引发生物相容性问题或干扰正常细胞生长。为此，本文提出一种无标记血管连通性网络（VC-Net），通过体外培养微血管网络（MVNs）并获取不同培养条件下的显微图像作为训练数据。VC-Net采用血管队列对比学习（VQCL）与类别不平衡算法，解决样本量少、特征模糊及类别分布不均问题。结果表明，该模型评估结果与荧光成像无显著差异；且能有效区分正常与肿瘤相关微血管网络的连通特性：在肿瘤微环境中，平均连通性下降30.8%，非连通区域扩大37.3%。本研究为类器官与肿瘤血管化无标记、连续评估提供了新路径。
原文摘要 · Abstract (English)
Continuous monitoring and in-situ assessment of microvascular connectivity have significant implications for culturing vascularized organoids and optimizing the therapeutic strategies. However, commonly used methods for vascular connectivity assessment heavily rely on fluorescent labels that may either raise biocompatibility concerns or interrupt the normal cell growth process. To address this issue, a Vessel Connectivity Network (VC-Net) was developed for label-free assessment of vascular connectivity. To validate the VC-Net, microvascular networks (MVNs) were cultured in vitro and their microscopic images were acquired at different culturing conditions as a training dataset. The VC-Net employs a Vessel Queue Contrastive Learning (VQCL) method and a class imbalance algorithm to address the issues of limited sample size, indistinctive class features and imbalanced class distribution in the dataset. The VC-Net successfully evaluated the vascular connectivity with no significant deviation from that by fluorescence imaging. In addition, the proposed VC-Net successfully differentiated the connectivity characteristics between normal and tumor-related MVNs. In comparison with those cultured in the regular microenvironment, the averaged connectivity of MVNs cultured in the tumor-related microenvironment decreased by 30.8%, whereas the non-connected area increased by 37.3%. This study provides a new avenue for label-free and continuous assessment of organoid or tumor vascularization in vitro.
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