Label-Free Digital Holotomography Reveals Ibuprofen-Induced Morphological Changes to Red Blood Cells

Abstract

Understanding the dose-dependent effect of over-the-counter drugs on red blood cells (RBCs) is crucial for hematology and digital pathology. Yet, it is challenging to continuously record the real-time, drug-induced nanoscopic shape changes of RBCs in a label-free manner. Here, we demonstrate digital holotomography (DHTM) enabled real-time, label-free concentration-dependent and time-dependent monitoring of ibuprofen on RBCs from a healthy donor. The RBCs are segmented based on 3D and 4D refractive index tomograms and their morphological and chemical parameters are retrieved with their shapes classified using machine learning. We directly observed the formation and motion of spicules on the RBC membranes when aqueous solutions of ibuprofen were drop cast on wet blood, creating rough-membraned echinocyte forms. At low concentrations of 0.25-0.50 mM, the ibuprofen-induced morphological change was transient but at high concentrations (1.5-3 mM) the spiculated RBC remained over a period of up to 1.5 hours. Molecular simulations confirmed that aggregates of ibuprofen molecules at high concentrations significantly disrupted the RBC membrane structural integrity and lipid order, but produced negligible effect at low ibuprofen concentrations. Control experiments on the effect of urea, hydrogen peroxide and aqueous solutions on RBCs showed zero spicule formation. Our work elucidates the dose-dependent chemical effects on RBCs using label-free microscopes that can be deployed for the rapid detection of overdosage of over-the-counter and prescribed drugs. Significance The interaction between drugs and blood cells is an important field of study in order to understand the risk for drug-induced haematological adverse effects. Using digital holo-tomographic microscopy (DHTM), we can resolve the real-time effect of medications on the morphological and chemical properties of red blood cells with high spatial and temporal resolution and in a label-free manner. We show that our approach can be used as a haematology platform for the diagnosis of blood disorders and for monitoring the dose-dependent effect of prescribed and over-the-counter medications in a cost-effective manner, with significant implications for its applicability in resource-limited settings and in the field of personalized medicine.