Predicting natural and unnatural red blood cell shape transformations

Red blood cells (RBCs) can undergo many changes as they travel through the body, both natural and unnatural. One of these is a sequence of morphological changes known as the stomatocyte-discocyte-echinocyte transformation, in which the RBC transforms from a cell with a slit-like central area — looking like a coffee bean — to a normal biconcave shape, to a spiky cell shape.

Researcher Sisi Tan created a nonlinear model to predict this unique transformation.

“Nonlinearity is a behavior exhibited by the vast majority of materials in nature, while linearity often serves as a simplification that approximates nonlinearity,” Tan said. “This is true for cells as well.”

Tan’s model accounts for the nonlinear behaviors of cell mechanics, including the stretching and bending deformations, area and volume variations, and changes between the two leaflets of the cell membrane.

“This adaptability not only indicates the robustness of our current model, but also highlights the significance of incorporating nonlinear dynamics in cellular biomechanics,” Tan said.

However, the current model doesn’t take the cell’s nucleus into account, which Tan hopes to include in future models so that it can be applied to other types of cells.

Tan said that the model accurately predicted the RBC transformations without much modification. Since the mechanical properties of cells are closely associated with some diseases, Tan hopes it can be used for disease prediction.

“For instance, in sickle cell anemia, the shape and mechanics of red blood cells are significantly altered,” Tan said. “The model could predict these changes based on the altered mechanical properties, which are known to be affected in such conditions.”

Source: “Nonlinear modeling for predicting red blood cell morphological transformations,” by Sisi Tan, Journal of Applied Physics (2024). The article can be accessed at https://doi.org/10.1063/5.0239806 .