Dataset Information

Comparison of deep plasma proteome enrichment methods

ABSTRACT: Plasma is a complex biological fluid containing extracellular vesicles (EVs), residual platelets, and soluble proteins, all of which can serve as biomarkers for pathological conditions. While conventional plasma proteomics typically identifies hundreds of proteins, recent enrichment strategies have expanded coverage to thousands. It is not clear yet whether these methods enrich preferentially different classes of protein and whether they allow for reliable quantification Here, we compared three common advanced proteomic workflows—Seer Proteograph (nanoparticle-based), MagNET (SCX magnetic beads), and ENRICHplus (PreOmics)—as well as EV enrichment obtained by centrifugation. We aimed to explore the content in soluble proteins, EV cargo, and platelet-derived proteins in platelet-poor plasma (PPP) from healthy donors after the enrichments. Samples were analyzed using timsTOF platforms and quantified with DIA-NN in library-free mode. Functional insights were obtained using Over-Representation Analysis (ORA), Human Protein Atlas annotations, and EV-specific markers. Quantification was evaluated comparing each method to neat plasma using protein coefficient of variation and point-biserial correlation. We quantified an average of ~4500 proteins with EV centrifugation, ~4000 with Seer, ~2800 with ENRICHplus, ~2300 with MagNET, and ~900 with neat plasma. Each method enriched distinct protein signatures: EV markers such as CD81 were predominantly detected in EV preparations; lipoproteins were enriched in ENRICHplus; cytokine and hormone signaling pathways were most evident in Seer. Platelet protein intensity was directly correlated with total protein identifications but did not compromise quantification of low-abundance proteins. Across 50 healthy individuals, Proteograph consistently demonstrated reproducible enrichment and depletion patterns, with notable exceptions for proteins such as FCN3, F11, and FCGBP.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Chiara guerrera

LAB HEAD: Ida Chiara Guerrera

PROVIDER: PXD063673 | Pride | 2025-10-17

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	2024-06-07_p029_JVN_s00458_30SPD_Mag-Net_20ul_PPP5_S4-D3_1_10320.d.7z	Other
	2024-06-07_p029_JVN_s00458_30SPD_Mag-Net_20ul_PPP5_S4-E3_1_10321.d.7z	Other
	2024-06-07_p029_JVN_s00458_30SPD_Mag-Net_20ul_PPP5_S4-F3_1_10322.d.7z	Other
	220722_RD_EV_nE_DIA_Plasma_Sains_1_5335.d.7z	Other
	220722_RD_EV_nE_DIA_Plasma_Sains_2_5336.d.7z	Other

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Publications

Mining the plasma proteome: Evaluation of enrichment methods for depth and reproducibility.

Roger K K Metatla I I Ceccacci S S Wahbi K K Motté L L Chhuon C C Guerrera I C IC

Journal of proteomics 20250818

Plasma is a complex biological fluid containing extracellular vesicles (EVs), residual platelets, and soluble proteins. While conventional plasma proteomics typically identifies hundreds of proteins, recent enrichment strategies have expanded coverage to thousands. It is still unclear whether these methods enrich preferentially different classes of protein and whether they allow for reliable quantification. Here, we compared three common advanced proteomic workflows-Proteograph (Seer), Mag-Net ( ...[more]

PMID: 40829696

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Project description:Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.