Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39159935", "kind": "science-record", "revision": 2, "content_hash": "sha256:896384c639c309351bbcb74b8a7c2ac3315740d95148f26375cb773a4057ea55", "created_at": "2026-11-01T20:29:17Z", "updated_at": "2026-11-01T21:33:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.", "doi": "10.1021/acs.jproteome.4c00104", "pmid": "39159935", "authors": ["Bradley Ward", "Sébastien Pyr Dit Ruys", "Jean-Luc Balligand", "Leïla Belkhir", "Patrice D Cani", "Jean-François Collet", "Julien De Greef", "Joseph P Dewulf", "Laurent Gatto", "Vincent Haufroid", "Sébastien Jodogne", "Benoît Kabamba", "Maxime Lingurski", "Jean Cyr Yombi", "Didier Vertommen", "Laure Elens"], "journal": "Journal of proteome research", "year": 2024, "abstract": "Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.", "claims": [{"id": "claim-method", "statement": "Two sample proportion tests and t test were used to evaluate the significance of the data when appropriate, such as when comparing the differences in the mean number of identified proteins in fractionated and nonfractionated samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39159935", "locator": "sections[12] \"Statistical Analysis\" paragraph 2", "quote": "Two sample proportion tests and t test were used to evaluate the significance of the data when appropriate, such as when comparing the differences in the mean number of identified proteins in fractionated and nonfractionated samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available via ProteomeXchange with identifier PXD047901.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39159935", "locator": "sections[19] \"notes\" paragraph 1", "quote": "Data are available via ProteomeXchange with identifier PXD047901."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The mean number of proteins detected and quantified in human plasma is found to be significantly higher, with an increase of around 50% for fractionated-pooled samples as compared to unfractionated samples, with an average of 1321 and 894 proteins per patient for fractionated-pooled and unfractionated samples, respectively (right-tailed, paired, t test, p-value = 1.17 × 10–6).", "kind": "quantitative", "target": {"value": 50.0, "unit": "per cent", "tolerance": null, "source": "The mean number of proteins detected and quantified in human plasma is found to be significantly higher, with an increase of around 50% for fractionated-pooled samples as compared to unfractionated samples, with an average of 1321 and 894 proteins per patient for fractionated-pooled and unfractionated samples, respectively (right-tailed, paired, t test, p-value = 1.17 × 10–6)."}, "evidence": [{"asset": "source:pmid:39159935", "locator": "sections[14] \"Enhanced Protein Detection with Fractionation\" paragraph 2", "quote": "The mean number of proteins detected and quantified in human plasma is found to be significantly higher, with an increase of around 50% for fractionated-pooled samples as compared to unfractionated samples, with an average of 1321 and 894 proteins per patient for fractionated-pooled and unfractionated samples, respectively (right-tailed, paired, t test, p-value = 1.17 × 10–6)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The mean number of proteins detected and quantified in human plasma is found to be significantly higher, with an increase of around 50% for fractionated-pooled samples as compared to unfractionated samples, with an average of 1321 and 894 proteins per patient for fractionated-pooled and unfractionated samples, respectively (right-tailed, paired, t test, p-value = 1.17 × 10–6).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39159935"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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