中性粒细胞是循环中数量最丰富的白细胞之一，它们的一个关键功能是通过释放细胞外DNA，即中性粒细胞外陷阱（NETs）来消除致病威胁。在许多疾病中，包括癌症、2型糖尿病和传染病，NET的释放是失调的。目前，常规的NET形成（NETosis）定量方法依赖于荧光抗体标记或ELISA检测循环NET相关蛋白，这些方法昂贵、费时且费力。本研究采用了一种新颖的“虚拟染色”方法，利用深度卷积神经网络（CNNs）实现了无标记定量微柱阵列中被NETs困住的NETs在微流控装置中的。虚拟染色用相位对比图像中的形态特征与Sytox绿色（DNA染料）图像中的荧光特征建立关联。我们首先调查了不同学习速率对模型训练的影响，并优化了学习速率，以实现最佳模型，该模型可以根据各种重建衡量标准（如结构相似度（SSIM）和像素误差（MAE、MSE））提供接近Sytox绿色染色的输出。研究了不同NET浓度的虚拟染色，结果显示与荧光染色呈线性相关。作为临床测试的概念验证，该模型被用于表征经NETosis诱导剂（包括脂多糖（LPS）、佛波酯12-肉豆蔻酸13-乙酯（PMA）和钙离子载体（CaI））处理的纯化中性粒细胞，并成功检测到不同处理的不同NET配置文件。总的来说，这些结果证明了使用深度学习进行增强无标记NETs图像分析在临床研究、药物研发和疾病的现场检测方面的潜力。

Neutrophils are the most abundant circulating white blood cells and one of their critical functions to eliminate pathogenic threats includes the release of extracellular DNA, also known as neutrophil extracellular traps (NETs), which is dysregulated in many diseases including cancer, type 2 diabetes mellitus and infectious diseases. Currently, conventional methods to quantify the NET formation (NETosis) rely on fluorescence antibody-based NET labelling or circulating NET-associated protein detection by ELISA, which are expensive, laborious, and time-consuming. In this work, we employed a novel "virtual staining" using deep convolutional neural networks (CNNs) to facilitate label-free quantification of NETs trapped in a micropillar array in a microfluidic device. Virtual staining is constructed to establish relations between morphological features in phase contrast images and fluorescence features in Sytox-green (DNA dye) images. We first investigated the effect of different learning rates on model training and optimized the learning rate to achieve the best model which can provide outputs close to Sytox green staining based on various reconstruction metrics (e.g., structural similarity (SSIM) and pixel-wise error (MAE, MSE)). The virtual staining of different NET concentrations was investigated which showed a linear correlation with fluorescent staining. As a proof of concept for clinical testing, the model was used to characterize purified neutrophils treated with NETosis inducers, including lipopolysaccharide (LPS), phorbol 12-myristate 13-acetate (PMA), and calcium ionophore (CaI), and successfully detected different NET profiles for different treatments. Collectively, these results demonstrated the potential of using deep learning for enhanced label-free image analysis of NETs for clinical research, drug discovery and point-of-care testing of diseases.