Postma Rudmer J, Fischer Susan E, Bijkerk Roel, van Zonneveld Anton Jan
Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands.
Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands.
PLoS One. 2025 Jan 6;20(1):e0317110. doi: 10.1371/journal.pone.0317110. eCollection 2025.
Systemic diseases are often associated with endothelial cell (EC) dysfunction. A key function of ECs is to maintain the barrier between the blood and the interstitial space. The integrity of the endothelial cell barrier is maintained by VE-Cadherin homophilic interactions between adjacent cells. The morphology of these borders is highly dynamic and can be actively remodeled by numerous drivers in a (patho)physiologic context specific fashion.
High-content screening of the impact of circulatory factors on the morphology of VE-Cadherin borders in endothelial monolayers in vitro will enable the assessment of the progression of systemic vascular disease. We therefore aimed to create an image analysis pipeline, capable of automatically analyzing images from large scale screenings, both capturing all VE-cadherin phenotypes present in a sample while preserving the higher-level 2D structure. Our pipeline is aimed at creating 1D tensor representations of the VE-cadherin adherence junction structure and negate the need for normalization.
An image analysis pipeline, with at the center a convolution neural network was developed. The deep neural network was trained using examples of distinct VE-Cadherin morphologies from many experiments. The generalizability of the model was extensively tested in independent experiments, before further validation using ECs exposed ex vivo to plasma from patients with liver cirrhosis and proven vascular complications.
Our workflow was able to detect and stratify many of the different VE-Cadherin morphologies present within the datasets and produced similar results within independent experiments, proving the generality of the model. Finally, by EC-cell border morphology profiling, our pipeline enabled the stratification of liver cirrhosis patients and associated patient-specific morphological cell border changes to responses elicited by known inflammatory factors.
We developed an image analysis pipeline, capable of intuitively and robustly stratifying all VE-Cadherin morphologies within a sample. Subsequent VE-Cadherin morphological profiles can be used to compare between stimuli, small molecule screenings, or assess disease progression.
全身性疾病常与内皮细胞（EC）功能障碍相关。内皮细胞的一项关键功能是维持血液与间质空间之间的屏障。内皮细胞屏障的完整性通过相邻细胞间的血管内皮钙黏蛋白（VE-Cadherin）同型相互作用得以维持。这些边界的形态具有高度动态性，并且在（病理）生理背景下可被多种驱动因素以特定方式积极重塑。
对循环因子对体外内皮单层中VE-Cadherin边界形态的影响进行高内涵筛选，将有助于评估全身性血管疾病的进展。因此，我们旨在创建一个图像分析流程，能够自动分析大规模筛选的图像，既能捕捉样本中存在的所有VE-钙黏蛋白表型，又能保留更高层次的二维结构。我们的流程旨在创建VE-钙黏蛋白黏附连接结构的一维张量表示，并消除归一化的必要性。
开发了一个以卷积神经网络为核心的图像分析流程。使用来自多个实验的不同VE-Cadherin形态的示例对深度神经网络进行训练。在使用离体暴露于肝硬化患者血浆且已证实有血管并发症的内皮细胞进行进一步验证之前，在独立实验中广泛测试了该模型的通用性。
我们的工作流程能够检测并分层数据集中存在的许多不同VE-Cadherin形态，并在独立实验中产生相似结果，证明了该模型的通用性。最后，通过内皮细胞边界形态分析，我们的流程能够对肝硬化患者进行分层，并将特定患者的形态学细胞边界变化与已知炎症因子引发的反应相关联。
我们开发了一个图像分析流程，能够直观且稳健地对样本中的所有VE-Cadherin形态进行分层。随后的VE-Cadherin形态学概况可用于比较不同刺激、小分子筛选，或评估疾病进展。
