Short-Term Cellular Responses to Sub-100 nm PMMA and Polystyrene-Based Copolymer Nanoplastics in Human Embryonic Lung Fibroblasts: Cytotoxicity and Redox-Associated Markers

Abstract

Airborne plastic particles are a potential route of human inhalation exposure. We compared short-term responses of human embryonic lung fibroblasts to four sub-100 nm nanoplastic formulations: crosslinked PMMA particles (PMMA-70, PMMA-90) and styrene/sodium p-styrenesulfonate/methacrylic acid copolymers (PS-65, PS-80). Cells were exposed for 1, 3, or 24 h, and metabolic activity (MTT), mitochondrial membrane potential, intracellular oxidant-associated fluorescence (DCF), DNA-damage-response markers (8-oxo-dG, γH2AX, BRCA1), and protein-associated signals (NOX4, NRF2, NF-κB, STAT3, PCNA, BAX/BCL2, LC3, Beclin-1) were assessed. In equal-mass concentration–response experiments, PMMA-70 reduced the MTT signal at substantially lower nominal concentrations than the other formulations, whereas PMMA-90 showed the lowest apparent cytotoxicity. Molecular endpoints were measured at 28 µg/mL (PS-65, PS-80, PMMA-90) and, owing to its higher cytotoxicity, at 0.08 µg/mL (PMMA-70); the resulting profiles are formulation-specific and do not constitute dose-matched comparisons. DCF-associated fluorescence did not change significantly, whereas NOX4-, NRF2-, γH2AX-, and BRCA1-associated signals showed formulation- and time-dependent changes. Across the tested conditions, the formulations were generally associated with lower total intracellular NF-κB-associated fluorescence, with the most pronounced decreases observed after 24 h for PS-65 and PMMA-90 (to ~40% and ~20% of the control signal, respectively). In complete culture medium, PMMA-based formulations formed 180–200 nm hydrodynamic dispersions versus 81–90 nm for PS-based formulations, showing that nominal primary particle size does not describe the exposure-relevant particle state. These descriptive profiles warrant follow-up studies with matched doses, functional cellular endpoints, and physiologically relevant exposure models.