Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning

• Published in: International journal of molecular sciences 2023-07, Vol.24 (15), p.11885
• Main Authors: Chen, Cindy X, Funkenbusch, George T, Wax, Adam
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Blood; Cells; Datasets; Deep learning; detection; flow cytometry; Health aspects; Hemoglobin; Hemoglobin S; holography; Machine learning; Medical research; Medical screening; Medicine, Experimental; Morphology; Mortality; Neural networks; Polymerization; quantitative phase imaging; Regression analysis; Sickle cell anemia; Sickle cell disease; Sub-Saharan Africa; Taiwan
• ISSN: 1422-0067; 1661-6596; 1422-0067
• 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.