![]() ![]() Median log 10 intensity of complete features is shown on each boxplot, and the outliers are removed. Protein groups were filtered for complete features. ( D) Dynamic range of identified proteins matched with normalized protein intensities from a plasma protein database (22). ( C) CV of median-normalized peptide intensities filtered for three out of three identifications across assay replicates. When processed together, the median numbers of protein groups were 1,615, 862, 461, and 375 for five-NP, high-pH, depleted, and neat plasma, respectively. For this comparison, samples belonging to the respective workflows were processed as independent Spectronaut runs. ![]() The top dash depicts the number of identified proteins in any of the samples, and the lower dash represents the number of identified proteins in three out of three assay replicates (defined as the complete features). Error bars denote SDs of assay replicates. ( B) Median number of protein groups identified by each workflow. ( A) Step-by-step comparison of five-NP, high-pH fractionation, depleted, and neat plasma workflows. Comparing a five-NP workflow (red) to a 19-concatenated-into-9 high-pH fractionation of depleted plasma strategy (blue), a plasma depletion strategy (green), and neat plasma (purple). Machine learning mass spectrometry nanoparticle nano–bio interaction proteomics.ĭIA workflow comparison. ![]() This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies. Our results suggest that nanoparticle functionalization can be tailored to protein sets. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. ![]()
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