Dataset Information

Benchmarking plasma from clinical and preclinical models

ABSTRACT: The push for new clinical biomarkers has seen rapid innovation in biofluid analysis, particularly for plasma. For mass-spectrometry (MS)-based analysis, achieving depth and quantitative accuracy whilst ensuring throughput continues to shape plasma methods development. Numerous workflows have emerged that mitigate high-abundance suppression and expand dynamic range, especially when paired with next-generation MS instrumentation. Yet systematic evaluations that also consider biological variables (e.g., biofluid type, species) and technical parameters (e.g., MS methods) are limited. Here, we benchmarked eight sample-preparation workflows spanning neat approaches (SP3, STrap), depletion (perchloric acid, PerCA), and corona-enrichment strategies (MagNet HILIC/SAX, Enrich-iST, ProteoNano). We compared their performance across human plasma, human serum, and rat plasma, analysing all samples on an Orbitrap Astral (Thermo) using two plasma-optimised data-independent acquisition (DIA) methods: one discovery-maximised and one throughput-maximised. We identified 2,726 human and 3,767 rat proteins across workflows and methods, including ~1,000 from neat plasma. Increasing throughput incurred a ~20-30% reduction in depth, depending on workflow and species. EV-enrichment produced the deepest proteomes but with distinct compositions relative to neat, depleted, and secreted-protein-enriched samples, revealing a unique sub-proteome niche. Several workflows also performed markedly better in rat plasma, supporting improved sensitivity for preclinical analyses. Enrichment or depletion dramatically reshaped the balance of tissue- and cell-specific proteins detectable in plasma, suggesting that workflow choice should be guided by the organs, immune targets, or inflammatory signals most relevant to the study. In this vein, statistical analysis of differentially abundant proteins showed that >90% of detected proteins were significantly altered between workflows, with the largest numbers arising from the corona-enrichment strategies, underscoring how strongly workflow choice shapes the downstream proteome. Taken together, these findings emphasise a rapidly expanding plasma methodological landscape, where the most effective workflow is the one most precisely tailored to a cohort’s biology.

INSTRUMENT(S):

ORGANISM(S): Rattus Norvegicus (rat) Homo Sapiens (human)

TISSUE(S): Blood Plasma, Blood Serum

SUBMITTER: Samantha Emery-Corbin

LAB HEAD: Ralf Schittenhelm

PROVIDER: PXD072131 | Pride | 2026-07-27

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	AST_SJE_20241213_E24_003_1.raw	Raw
	AST_SJE_20241213_E24_003_10.raw	Raw
	AST_SJE_20241213_E24_003_10_15cm.raw	Raw
	AST_SJE_20241213_E24_003_11.raw	Raw
	AST_SJE_20241213_E24_003_11_15cm_b.raw	Raw

Items per page:

1 - 5 of 155

Similar Datasets

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.

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.

Dataset Information

Benchmarking plasma from clinical and preclinical models

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets