High-Sensitivity Magnetic Levitation Reveals Intrinsic Protein Corona Heterogeneity on Identical Nanoparticles

The protein corona (PC), a layer of biomolecules that adsorbs onto nanoparticles (NPs) surfaces upon exposure to biological fluids, plays a key role in defining the biological identity and performance of nanomaterials. However, most current analytical approaches rely on pooled measurements of PC-coated NPs and therefore lack the resolution needed to detect subtle heterogeneity in PC composition, potentially masking important differences in NPs biological identity. Here, we used a high-sensitivity magnetic levitation (MagLev) platform capable of resolving extremely small density differences among nominally identical PC-coated NPs, enabling fractionation of particles based on subtle variations in PC composition. Compared with conventional standard MagLev systems (density resolution ∼10 ^-3 g/cm ³ ), the high-sensitivity MagLev improves density sensitivity by up to three orders of magnitude, allowing discrimination of density differences as small as 10 ^-5 g/cm ³ . Using this approach, PC-coated NPs were separated along the MagLev column into multiple fractions corresponding to distinct density populations. Subsequent proteomic analysis across the extracted fractions identified more than 500 proteins and revealed a structured but continuous redistribution of protein composition across the column, including fraction-dependent differences in protein abundance, overlap, and biological identity. In particular, the fraction series captured hidden heterogeneity among nominally identical PC-coated NPs, with upper fractions retaining relatively stronger extracellular/plasma-associated signatures and lower fractions showing increasing representation of structural, membrane-associated, cytoskeletal, and metabolic proteins. These findings demonstrate that PC formation is intrinsically heterogeneous even on identical NPs and this heterogeneity is largely missed by conventional pooled analysis. High-sensitivity MagLev provides a simple, label-free framework for resolving PC heterogeneity and offers a new analytical approach for studying NP–biomolecule interactions, with important implications for nanomedicine design, biomarker discovery, and the clinical translation of NP-based systems.