Abstract
Deep interrogation of plasma proteins on a large scale is a challenge due to the number and concentration of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochemical properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by specific surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biological samples.
Original language | English (US) |
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Article number | e2106053119 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 119 |
Issue number | 11 |
DOIs | |
State | Published - Mar 15 2022 |
Keywords
- machine learning
- mass spectrometry
- nanoparticle
- nano–bio interaction
- proteomics
ASJC Scopus subject areas
- General