A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34861815", "kind": "science-record", "revision": 5, "content_hash": "sha256:a8d6fafbe7750cf2a1631f8ef77b91c6929de7fcca5e1cb2ca7fef66629b29f2", "created_at": "2026-11-01T17:36:33Z", "updated_at": "2026-11-01T22:47:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy.", "doi": "10.1186/s12014-021-09328-8", "pmid": "34861815", "authors": ["Sarah R Weber", "Yuanjun Zhao", "Jingqun Ma", "Christopher Gates", "Felipe da Veiga Leprevost", "Venkatesha Basrur", "Alexey I Nesvizhskii", "Thomas W Gardner", "Jeffrey M Sundstrom"], "journal": "Clinical proteomics", "year": 2021, "abstract": "BACKGROUND: Vitreous is an accessible, information-rich biofluid that has recently been studied as a source of retinal disease-related proteins and pathways. However, the number of samples required to confidently identify perturbed pathways remains unknown. In order to confidently identify these pathways, power analysis must be performed to determine the number of samples required, and sample preparation and analysis must be rigorously defined.\nMETHODS: Control (n = 27) and proliferative diabetic retinopathy (n = 23) vitreous samples were treated as biologically distinct individuals or pooled together and aliquoted into technical replicates. Quantitative mass spectrometry with tandem mass tag labeling was used to identify proteins in individual or pooled control samples to determine technical and biological variability. To determine effect size and perform power analysis, control and proliferative diabetic retinopathy samples were analyzed across four 10-plexes. Pooled samples were used to normalize the data across plexes and generate a single data matrix for downstream analysis.\nRESULTS: The total number of unique proteins identified was 1152 in experiment 1, 989 of which were measured in all samples. In experiment 2, 1191 proteins were identified, 727 of which were measured across all samples in all plexes. Data are available via ProteomeXchange with identifier PXD025986. Spearman correlations of protein abundance estimations revealed minimal technical (0.99-1.00) and biological (0.94-0.98) variability. Each plex contained two unique pooled samples: one for normalizing across each 10-plex, and one to internally validate the normalization algorithm. Spearman correlation of the validation pool following normalization was 0.86-0.90. Principal component analysis revealed stratification of samples by disease and not by plex. Subsequent differential expression and pathway analyses demonstrated significant activation of metabolic pathways and inhibition of neuroprotective pathways in proliferative diabetic retinopathy samples relative to controls.\nCONCLUSIONS: This study demonstrates a feasible, rigorous, and scalable method that can be applied to future proteomic studies of vitreous and identifies previously unrecognized metabolic pathways that advance understanding of diabetic retinopathy.", "claims": [{"id": "claim-method", "statement": "Differential analysis was performed using moderated t-statistics from the empirical Bayes procedure linear model for microarray analysis (LIMMA) [30] as extended to accommodate TMT proteomic data [31].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34861815", "locator": "sections[11] \"Differential expression analysis\" paragraph 2", "quote": "Differential analysis was performed using moderated t-statistics from the empirical Bayes procedure linear model for microarray analysis (LIMMA) [30] as extended to accommodate TMT proteomic data [31]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025986. All other Additional files generated during the current study are available at https://github.com/umich-brcf-bioinf-projects/Oculomics_tomwgard_CU3-power_analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34861815", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025986. All other Additional files generated during the current study are available at https://github.com/umich-brcf-bioinf-projects/Oculomics_tomwgard_CU3-power_analysis."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Spearman correlations of protein abundance estimations revealed minimal technical (0.99–1.00) and biological (0.94–0.98) variability.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34861815", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Spearman correlations of protein abundance estimations revealed minimal technical (0.99–1.00) and biological (0.94–0.98) variability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Spearman correlations of protein abundance estimations revealed minimal technical (0.99–1.00) and biological (0.94–0.98) variability.", "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:34861815"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD025986"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD025986"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34861815", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12014-021-09328-8", "sha256": "sha256:9a7a683e0559f1a12b379068b69b911435516271492545c8de2db02b4f6ff865", "bytes": 109743, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12014-021-09328-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8903510", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:PXD025986", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=PXD025986", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD025986", "version": null, "note": "Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025986. 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