Abstract
INTRODUCTION: The safe-by-design of engineered nanoparticles (NPs) for any application requires a detailed understanding of how the particles interact with single cells. Most studies are based on two-dimensional, uniformly dense cell cultures, which do not represent the diverse and inhomogeneous cell environments found in situ. In-vitro models that accurately represent tissue complexity, including realistic cell densities, are essential to increase the predictive accuracy of studies on cell-NP interactions. This study uses a bioprinted cell gradient model to examine the relation between cell density and NP uptake in one dish. METHOD: A549 lung epithelial cell density gradients within single inserts were produced with a bioprinter by modulating inter-droplet distances. After two days in culture, cells were exposed to Cy5-labeled silica NPs (SiO(2) NPs, ∼112 nm, 20 μg/mL) for up to 48 h. Confocal fluorescence microscopy and 3D image analysis were used to quantify NP uptake, cell surface area, and cell volume. The relationship between NP uptake and the other parameters was then investigated statistically. RESULTS: Bioprinting enabled the creation of reproducible linear cell density gradients, allowing controlled modeling of density variations while preserving cell viability throughout the experiment. Increasing inter-droplet distances, from 0.1 mm to 0.6 mm, were used to achieve uniformly decreasing cell densities. SiO(2) NP uptake per cell was around 50% higher in low-density regions compared to high-density areas across all time points, i.e., 6, 24, and 48 h post-exposure. This inverse relationship correlated with greater average cell surface area in lower-density regions, while differences in the proliferation rates of the A549 cells at varying densities did not significantly impact uptake, did not significantly impact uptake. CONCLUSION: SiO(2) NP uptake is significantly enhanced at lower cell densities, mainly due to the increased available surface area, revealing potential cell-NP interaction differences in tissues that present cell density variability. Our drop-on-demand bioprinting gradient model successfully supports the implementation of cell density gradients in in-vitro models to increase their relevance as new approach methodologies (NAMs) for next-generation risk assessment strategies.