BIOS Lab, Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Int J Mol Sci. 2023 Jul 25;24(15):11885. doi: 10.3390/ijms241511885.
Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs), causing blood vessel occlusion and leading to severe morbidity and early mortality rates if untreated. While sickle solubility tests are available to sub-Saharan African population as a means for detecting sickle hemoglobin (HbS), the test falls short in assessing the severity of the disease and visualizing the degree of cellular deformation. Here, we propose use of holographic cytometry (HC), a high throughput, label-free imaging modality, for comprehensive morphological profiling of RBCs as a means to detect SCD. For this study, more than 2.5 million single-cell holographic images from normal and SCD patient samples were collected using the HC system. We have developed an approach for specially defining training data to improve machine learning classification. Here, we demonstrate the deep learning classifier developed using this approach can produce highly accurate classification, even on unknown patient samples.
镰状细胞病（SCD）是一种遗传性血液病，与高死亡率相关，特别是在撒哈拉以南非洲地区。SCD 是由于镰状血红蛋白的聚合导致的，这降低了红细胞（RBC）的柔韧性，导致血管阻塞，如果不治疗，会导致严重的发病率和早期死亡率。虽然镰状溶解度测试可用于撒哈拉以南非洲人群，作为检测镰状血红蛋白（HbS）的一种手段，但该测试在评估疾病严重程度和可视化细胞变形程度方面存在不足。在这里，我们提出使用全息细胞术（HC）作为一种检测 SCD 的方法，该方法是一种高通量、无标记的成像方式，可全面分析 RBC 的形态特征。在这项研究中，使用 HC 系统收集了来自正常和 SCD 患者样本的超过 250 万个单细胞全息图像。我们已经开发了一种专门定义训练数据的方法来改进机器学习分类。在这里，我们证明了使用该方法开发的深度学习分类器可以产生非常高的分类准确性，即使是对未知的患者样本也是如此。
