Single-Cell Profiling of Red Blood Cell Density and Sickling to Reveal Patient Heterogeneity in Sickle Cell Anemia - PROJECT SUMMARY/ABSTRACT Sickle cell anemia (SCA), first identified by Linus Pauling over 70 years ago as the prototypical “molecular disease,” remains a debilitating disorder affecting over 100,000 individuals in the U.S. SCA is a monogenic disease caused by a point mutation in the gene encoding the hemoglobin β chain, causing polymerization of hemoglobin monomers under hypoxia and characteristic red blood cell (RBC) “sickling.” Despite recent advances in gene therapies, treatment with hydroxyurea remains the standard of care. However, patient responses to hydroxyurea are highly variable, with many individuals continuing to suffer from severe symptoms. While some genetic factors contributing to this heterogeneity have been identified, they do not explain the whole scope of patient variability, and there are no known biomarkers or phenotypic factors that can fully explain the broad range of treatment responses. A key obstacle in this area is the lack of analytical tools that can capture the fundamental underpinnings of patient variability. Current clinical assessments focus on basic RBC properties, which fail to capture the full scope of patient variability, and emerging imaging techniques lack reproducibility and resolution. To address this problem, we propose leveraging a fundamental biophysical phenomenon that occurs when cells sickle: as hemoglobin polymerizes, a decrease in intracellular osmotic pressure forces water out of the cell and leads to an increase in cell density. Using a suspended microchannel resonator, we propose to measure multiple single-cell biophysical metrics—including cell density and sickling state—in an oxygen-controlled environment to gain unprecedented, high-resolution information about hemoglobin polymerization while avoiding the limitations of existing techniques. In Aim 1, we will develop a system to control oxygen tension within our integrated measurement platform, characterize the differential impact of hypoxia on healthy and SCA RBCs, and train a machine learning model to classify images of cells with hemoglobin polymer versus those without. Aim 2 will perform a patient study, measuring the RBCs of 40 SCA patients and correlating features derived from cell density distributions with de- identified clinical features, thereby identifying the biophysical factors that best distinguish patients with mild versus severe outcomes following hydroxyurea treatment. This project will provide critical insight into the mechanisms underlying variable treatment responses in SCA, enabling further study into the mechanisms underlying this heterogeneity and advances in personalized clinical care. This project, conducted in Dr. Scott Manalis’s lab at MIT, leverages world-class engineering and biological expertise, supplemented by collaborations with labs at MIT and Massachusetts General Hospital. It will provide comprehensive training relevant to postgraduate career opportunities in microfluidics, machine learning, performing patient studies, and facilitating multidisciplinary research. Finally, MIT’s programs in responsible research conduct will provide an opportunity to reflect on my ethical responsibilities as a researcher.
