Laboratory of Chemical Physics, National Institute of Diabetes, Digestive and Kidney Diseases , National Institutes of Health , Bethesda , Maryland 20892 , United States.
J Phys Chem B. 2018 Dec 13;122(49):11579-11590. doi: 10.1021/acs.jpcb.8b07638. Epub 2018 Sep 18.
The polymerization of the mutant hemoglobin S upon deoxygenation to form fibers in red blood cells of patients suffering from sickle-cell anemia results in changes in cell shape and rigidity, also known as sickling, which underlie the pathology of the disease. While much has been learned about the fundamental physical chemistry of the polymerization process, transferring these insights to sickling of red cells under in vivo conditions requires being able to monitor, and ultimately predict, the time course of cellular sickling under physiological conditions of deoxygenation. To this end, we have developed an experimental technique for tracking the temporal evolution of the sickling of red blood cells under laboratory deoxygenation conditions, based on the automated analysis of sequences of microscope images and machine-learning analysis to characterize cell morphology. As an aid in the quantitative understanding of these experiments, we have developed a computational framework for simulating the time dependence of sickling in populations of red blood cells which incorporates the current theoretical and empirical understanding of the physical chemistry of the sickling process. In order to apply these techniques to our experiments, we have theoretically determined the time course of deoxygenation by solving the diffusion equation for oxygen in our experimental geometry. With this combined description, we are able to reproduce our experimentally observed kinetics of sickling, suggesting that our theoretical approach should be applicable to physiological deoxygenation scenarios.
患有镰状细胞贫血症的患者的红细胞在脱氧时突变血红蛋白 S 的聚合形成纤维,导致细胞形状和刚性发生变化,也称为镰状化,这是该疾病病理学的基础。尽管已经了解了聚合过程的基本物理化学性质,但将这些见解转移到体内缺氧条件下的红细胞镰状化,需要能够监测并最终预测在生理缺氧条件下细胞镰状化的时间进程。为此,我们开发了一种实验技术,用于在实验室缺氧条件下跟踪红细胞镰状化的时间演变,该技术基于显微镜图像序列的自动分析和机器学习分析来表征细胞形态。作为对这些实验进行定量理解的辅助手段,我们开发了一种计算框架,用于模拟红细胞群体中镰状化的时间依赖性,该框架结合了当前对镰状化过程物理化学的理论和经验理解。为了将这些技术应用于我们的实验,我们通过在我们的实验几何形状中求解氧气的扩散方程,从理论上确定了脱氧的时间过程。通过这种综合描述,我们能够重现我们观察到的实验性镰状化动力学,这表明我们的理论方法应该适用于生理缺氧情况。