Abstract
For over 10 years BSG has been developing synthetic beads with differential proteome binding properties. Derivative products are well established with numerous citations in leading journals. Following the BSG lead to circumvent immuno-affinity, corona-based separations adapted to magnetic nano-particles has been introduced. However, this necessitates a specialized instrument-based workflow, and the need to perform LC-MS analysis on multiple bead-derived sub-proteomes. Nevertheless, singular BSG products support low abundance serum/plasma, enrichment, and different levels of Albumin removal. These products offer solutions that stands in terms of simplicity and versatility. To further their utility, LC-MS data characterization of the binding biases towards certain families or structurally related features of the sub-proteomes enriched is advantageous. Here we compared the Platelet rich plasma proteome using NRicher™, AlbuVoid™ and AlbuVoid™ PLUS by DIA LC-MS/MS. We characterized and evaluated the derived proteomes, vs. neat (not enriched) standard workflows. The bead-based workflow follows a bind/wash protocol using a standard microfuge to separate the buffer solutions from the beads. The derived sub-proteomes (different for each product) are bound to the beads, and then eluted. Proteins were reduced, alkylated, and digested with Trypsin/LysC and precipitated. Samples were acidified with 2% formic acid and purified by SPE. Peptides were resuspended in H2O, 0.1% FA, and analyzed on a TripleTof (Sciex), using a 45 min. gradient. AlbuVoid™ products provide the most proteome coverage over the 3 products, with total identifications of 1600 vs 900 for the neat (not enriched) sample. AlbuVoid™ and AlbuVoid™ PLUS reduced Albumin specific signal to around 85%. AlbuVoid™ PLUS also reduces Immunoglobulin signal. Importantly, regardless of the different efficiencies of Albumin removal, all threeproducts generate a signal enrichment of around 1.6X of the low abundance sub-proteome; those proteins below the top 100. Notably, the one key characteristic from the NRicher™ data is a very reproducible enrichment of specific sets of sub-proteomes, with low %CV across technical replicates. Using clustering analysis, network analysis and Gene ontology we have extracted those specific sub-proteomes to determine their communalities. In particular, enzymes, proteases and membrane protein families are especially enriched using NRicher™ making it an interesting tool for biomarker discovery targeted towards those specific families. Also, for NRicher™, we noted an increase coverage of Complement proteins, especially C4A, which is usually hard to detect and quantify in plasma biofluids. Overall, from this analysis and our experience with other common workflows, these products provide a cost-effective alternative, comparable or better than commonly used depletion methods. Individual product biases, thus create fit for purpose workflows with applications in both discovery, and targeted LC-MS quantification,