Influence of Functional Group Modification on the Toxicity of Nanoplastics

Nanoplastics (NPs) are ubiquitous in harvested organisms at various trophic levels, and more concerns on their diverse responses and wide species-dependent sensitivity are continuously increasing. However, systematic study on the toxic effects of NPs with different functional group modifications is still limited. In this review, we gathered and analyzed the toxic effects of NPs with different functional groups on microorganisms, plants, animals, and mammalian/human cells in vitro. The corresponding toxic mechanisms were also described. In general, most up-to-date relevant studies focus on amino (−NH2) or carboxyl (−COOH)-modified polystyrene (PS) NPs, while research on other materials and functional groups is lacking. Positively charged PS-NH2 NPs induced stronger toxicity than negatively charged PS-COOH. Plausible toxicity mechanisms mainly include membrane interaction and disruption, reactive oxygen species generation, and protein corona and eco-corona formations, and they were influenced by surface charges of NPs. The effects of NPs in the long-term exposure and in the real environment world also warrant further study.

Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

We have developed a scalable system that leverages protein nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle protein interface, driven by the relationship between protein-NP affinity and protein abundance. Here we demonstrate the importance of tuning the protein to NP surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 to 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.