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A Fast and Reliable Method Based on QCM-D Instrumentation for the Screening of Nanoparticle/Blood Protein Interactions.

Mariacristina GagliardiLaura ColagiorgioMarco Cecchini
Published in: Biosensors (2023)
The interactions that nanoparticles have with blood proteins are crucial for their fate in vivo. Such interactions result in the formation of the protein corona around the nanoparticles, and studying them aids in nanoparticle optimization. Quartz crystal microbalance with dissipation monitoring (QCM-D) can be used for this study. The present work proposes a QCM-D method to study the interactions on polymeric nanoparticles with three different human blood proteins (albumin, fibrinogen and γ-globulin) by monitoring the frequency shifts of sensors immobilizing the selected proteins. Bare PEGylated and surfactant-coated poly-(D,L-lactide- co -glycolide) nanoparticles are tested. The QCM-D data are validated with DLS and UV-Vis experiments in which changes in the size and optical density of nanoparticle/protein blends are monitored. We find that the bare nanoparticles have a high affinity towards fibrinogen and γ-globulin, with measured frequency shifts around -210 Hz and -50 Hz, respectively. PEGylation greatly reduces these interactions (frequency shifts around -5 Hz and -10 Hz for fibrinogen and γ-globulin, respectively), while the surfactant appears to increase them (around -240 Hz and -100 Hz and -30 Hz for albumin). The QCM-D data are confirmed by the increase in the nanoparticle size over time (up to 3300% in surfactant-coated nanoparticles), measured by DLS in protein-incubated samples, and by the trends of the optical densities, measured by UV-Vis. The results indicate that the proposed approach is valid for studying the interactions between nanoparticles and blood proteins, and the study paves the way for a more comprehensive analysis of the whole protein corona.
Keyphrases
  • protein protein
  • amino acid
  • binding protein
  • walled carbon nanotubes
  • high resolution
  • drug delivery
  • machine learning
  • iron oxide
  • mass spectrometry
  • deep learning
  • data analysis
  • aqueous solution