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Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

ABSTRACT: Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle-based circulating plasma proteomics studies have reported varying number of proteins (ca 2000-7000), but it’s unclear whether higher protein number is more informative. Here, we first develop OmniProt – a silica-nanoparticle workflow optimized through systematic evaluation of nanoparticle types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups. Using Astral with 60 sample-per-day throughput, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy in nanoparticle-enriched samples. Through controlled contamination experiments, we identified biomarkers for platelet/erythrocyte/coagulation-related contaminations in nanoparticle-based plasma proteomics. We developed open-access software Baize for contamination assessment. We validated pipeline in 193 patients with CT-indistinct benign nodules or early lung cancers, flagging five contaminated samples. This study reveals contamination alters protein identification/quantification in nanoparticle-based plasma proteomics and presents Baize software to evaluate contamination.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Platelet, Blood Plasma, Blood Serum

SUBMITTER: 欢高

LAB HEAD: Tiannan Guo

PROVIDER: PXD068107 | Pride | 2025-12-12

REPOSITORIES: Pride

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			Action	DRS
	Astral_plasma_build_spectral_library_DIANN181report20241023-lib.tsv	Tabular
	CAF20240205gaohh_EV_24minDIA_30F_F1.raw	Raw
	CAF20240205gaohh_EV_24minDIA_30F_F10.raw	Raw
	CAF20240205gaohh_EV_24minDIA_30F_F11.raw	Raw
	CAF20240205gaohh_EV_24minDIA_30F_F12.raw	Raw

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Project description:In biomarker discovery phases clinical proteomics provides large-scale identification and quantification of proteins in donor samples. For these studies plasma is the most frequently used biofluid, as it reflects both physiological and pathological states of the circulating proteome. However, the vast dynamic range of proteins in plasma remains a significant challenge, where low-abundance biomarkers are suppressed by high-abundance proteins, despite substantial technological improvements. Often the focus of improving outcomes in plasma proteomic workflows are within the biofluid’s sample preparation, although adjusting MS methods dramatically improves detection and sampling of lower abundance proteins. Herein we have benchmarked various liquid chromatography (LC) and data-independent acquisition (DIA) methods across three generations of mass spectrometry instruments: timsTOF Pro (2017), Orbitrap Eclipse (2020), and Orbitrap Astral (2023). This study explored 27 methods across 200 hours of instrument acquisition time, encompassing varying LC, MS, and database searching workflows. While each instrument generation significantly improved performance, each instrument also revealed a unique and different tuneable range to improve performance in plasma samples, highlighting the benefit of investing in plasma-specific method development for any mass spectrometer. We also evaluated the detection and quantification capabilities of each instrument via a unique approach of mixing paired Platelet Poor Plasma (PPP) and Platelet Rich Plasma (PRP), introducing linear contamination markers for quantitative assessment. This approach tested each instrument's sensitivity to detect low-abundance peptides and evaluated their quantitative accuracy. Lastly, we performed testing of the Orbitrap Astral’s parallel ion processing capabilities with the aim of improving low-abundance protein identifications using gas-phase enrichment (GPE). More specifically, we show that optimisation of MS2 AGC targets and injection time enhanced GPE of low-abundance peptides, improving detection in plasma samples.

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Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

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