Lamoureux Erik S, Cheng You, Islamzada Emel, Matthews Kerryn, Duffy Simon P, Ma Hongshen
Department of Mechanical Engineering, University of British Columbia, Canada.
Centre for Blood Research, University of British Columbia, Canada.
Heliyon. 2024 Jul 26;10(15):e35276. doi: 10.1016/j.heliyon.2024.e35276. eCollection 2024 Aug 15.
Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is inaccessible to human expert labellers. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage time points) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluate differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can detect subtle cellular morphology differences resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.
薄膜血涂片的显微镜检查被广泛用于识别红细胞（RBC）病变，包括疟疾寄生虫感染和血红蛋白病，如镰状细胞病和地中海贫血。新兴研究表明，红细胞的非病理性变化也可以在图像中检测到，例如储存损伤导致的变形性和形态变化。在输血医学中，细胞变形性是捐赠红细胞质量的潜在生物标志物。然而，这种生物标志物临床转化的一个主要障碍是进行该测量存在困难。为应对这一挑战，我们开发了一种基于薄膜血涂片细胞图像的红细胞生物物理分析方法。我们假设在血涂片图像中存在明显的细微细胞变化，但人类专家标注人员无法获取这些信息。为验证这一假设，我们开发了一种深度学习策略来分析吉姆萨染色的血涂片，以评估指示红细胞变形性和基于储存的降解的细微形态。具体而言，我们从27个红细胞样本（9名供体在3个储存时间点进行评估）制备了薄膜血涂片，并使用高分辨率显微镜对其成像。利用该数据集，我们训练了一个卷积神经网络来评估与细胞变形性相关的基于图像的形态特征。供体变形性的预测与微流控评分高度相关，可用于将图像高精度地分类到特定的变形性组中。我们还使用该模型评估了冷藏导致的红细胞形态差异。总之，我们的结果表明深度学习模型可以检测到由变形性和冷藏导致的细微细胞形态差异。这一结果表明从薄膜血涂片中评估供体血液质量具有潜力，而薄膜血涂片在临床工作流程中随处可得。
