← All .science files
pmid:40855359
High throughput tear proteomics with data independent acquisition enables biomarker discovery in allergic conditions.
América Vera-Montecinos · Claudio Carril Pardo · Mauricio Hernández · Pablo Saldivia · Guillermo Nourdin · Roberto Elizondo-Vega · Evelyn Sánchez · Sofía Amulef · Elard Koch · Cristian Vargas · Karina Oyarce
Scientific reports · 2025 doi:10.1038/s41598-025-17105-y
Paper
complete
Experiment
complete
Data
partial
Analysis
partial
Execution
absent
Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
paper object · 10 fields
authors array · 11 items
claims array · 3 items
[0] object · 6 fields
[1] object · 6 fields
[2] object · 6 fields
evidence array · 2 items
[0] object · 3 fields
gaps array · 0 items
experiment object · 8 fields
steps array · 3 items
[0] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 0 items
data object · 3 fields
assets array · 2 items
[0] object · 15 fields
structure object · 4 fields
[1] object · 15 fields
structure object · 15 fields
sample_accessions array · 0 items
gaps array · 2 items
analysis object · 4 fields
methods array · 5 items
[0] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[1] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[2] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 1 field
data_shapes array · 1 item
[3] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[4] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
targets array · 0 items
gaps array · 1 item
capability object · 3 fields
requirements array · 1 item
[0] object · 6 fields
assay_families array · 9 items
gaps array · 0 items
readiness object · 8 fields
axes object · 3 fields
method object · 5 fields
required_operations array · 3 items
missing_operations array · 0 items
capability object · 4 fields
data object · 6 fields
accessions array · 1 item
resolved_accessions array · 1 item
statements array · 1 item
can_run object · 3 fields
dsl_operations array · 3 items
assets array · 2 items
steps array · 1 item
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
gaps array · 0 items
review object · 5 fields
machine_critique object · 5 fields
reader object · 5 fields
evidence_check object · 2 fields
usage object · 3 fields
stages array · 1 item
[0] object · 4 fields
gaps array · 3 items
provenance object · 7 fields
entities array · 3 items
activities array · 3 items
[0] object · 4 fields
used array · 3 items
[0] object · 3 fields
[1] object · 3 fields
[2] object · 3 fields
[1] object · 4 fields
agents array · 2 items
lineage object · 4 fields
gaps array · 7 items
assays array · 1 item
[0] object · 11 fields
required_operations array · 3 items
source_hashes object · 1 field
evidence array · 1 item
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40855359", "kind": "science-record", "revision": 4, "content_hash": "sha256:5683e29182d9ea89282407431584599a8a40c413992a42df9a907fcdddcef0e6", "created_at": "2026-11-01T20:30:58Z", "updated_at": "2026-11-01T22:57:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "High throughput tear proteomics with data independent acquisition enables biomarker discovery in allergic conditions.", "doi": "10.1038/s41598-025-17105-y", "pmid": "40855359", "authors": ["América Vera-Montecinos", "Claudio Carril Pardo", "Mauricio Hernández", "Pablo Saldivia", "Guillermo Nourdin", "Roberto Elizondo-Vega", "Evelyn Sánchez", "Sofía Amulef", "Elard Koch", "Cristian Vargas", "Karina Oyarce"], "journal": "Scientific reports", "year": 2025, "abstract": "The search for pathological biomarkers in biological fluids that can provide valuable insight into an individual's health status, is a relevant area of research for multiple pathologies. Currently, the use of proteomics for the identification of differences in protein expression profiles between samples from healthy subjects and patients, has emerged as a powerful strategy to improve the current diagnosis of various pathologies or propose novel therapeutic approaches. Among the biological fluids from which new pathological biomarkers can be identified, tear secretion is highly attractive, since it can be collected non-invasively and could better concentrate proteins that sensitively reflect allergic responses, owing to their exposure to environmental factors and its connection to the respiratory system. Despite its potential, tear fluid remains underexplored, offering significant research opportunities. In this study, we collected human tear samples using the Shirmer Test from healthy and allergic individuals. Our optimized workflow, combining sample preparation and high-throughput proteomics using the data-independent acquisition (DIA) strategy, identified 2542 proteins and enabled the successful differentiation of the two groups. We identified 99 differentially expressed proteins. Our results show the feasibility of protein analysis in human tear samples, highlighting tears as a highly sensitive fluid for detecting health conditions. Data are available via ProteomeXchange with identifier PXD067099.", "claims": [{"id": "claim-method", "statement": "Significant differential protein expressions were determined using a Bayesian moderated t-test implemented in the limma package v.3.54.2.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40855359", "locator": "sections[18] \"Bioinformatics analysis\" paragraph 2", "quote": "Significant differential protein expressions were determined using a Bayesian moderated t-test implemented in the limma package v.3.54.2."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available via ProteomeXchange with identifier PXD067099.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40855359", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Data are available via ProteomeXchange with identifier PXD067099."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The optimized tear-proteomics workflow identified 2542 proteins and revealed 99 differentially expressed proteins that differentiated healthy from allergic individuals.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40855359", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Our optimized workflow, combining sample preparation and high-throughput proteomics using the data-independent acquisition (DIA) strategy, identified 2542 proteins and enabled the successful differentiation of the two groups."}, {"asset": "source:pmid:40855359", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We identified 99 differentially expressed proteins."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The optimized tear-proteomics workflow identified 2542 proteins and revealed 99 differentially expressed proteins that differentiated healthy from allergic individuals.", "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:40855359"], "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:PXD067099"], "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:PXD067099"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40855359", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-17105-y", "sha256": "sha256:afcffed2919381a0c1154505e0b7b9a273064ba1944d007442c964f6d43582f9", "bytes": 48957, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-17105-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12379103", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:PXD067099", "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=PXD067099", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD067099", "version": null, "note": "Data are available via ProteomeXchange with identifier PXD067099.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Data are available via ProteomeXchange with identifier PXD067099.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": "High throughput tear proteomics with data independent acquisition enables biomarker discovery in allergic conditions", "resolution_checked_at": "2026-11-01T20:16:18Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:PXD067099 not downloaded", "asset:data:geo:PXD067099 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Significant differential protein expressions were determined using a Bayesian moderated t-test implemented in the limma package v.3.54.2."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}, {"id": "method-3", "family": "differential_expression", "specific": "edger", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {"fold_change": "represents the log2 of fold change (FC) and the y-axis the"}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "EdgeR"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-5", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "hierarchical clustering"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["PXD067099"], "resolved_accessions": ["PXD067099"], "statements": ["Data are available via ProteomeXchange with identifier PXD067099."], "basis": "The paper names PXD067099 in ProteomeXchange Consortium via the PRIDE partner repository and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:40855359", "data:geo:PXD067099"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:PXD067099"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:00:29Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13652, "completion_tokens": 876, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40855359", "type": "paper", "label": "pmid:40855359"}, {"id": "source:pmid:40855359", "type": "manuscript", "label": "PMID 40855359 full text"}, {"id": "data:geo:PXD067099", "type": "dataset", "label": "GEO PXD067099"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40855359", "type": "paper", "label": "pmid:40855359"}, {"id": "source:pmid:40855359", "type": "manuscript", "label": "PMID 40855359 full text"}, {"id": "data:geo:PXD067099", "type": "dataset", "label": "GEO PXD067099"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40855359", "type": "extraction", "used": ["source:pmid:40855359"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-PXD067099", "type": "accession_resolution", "used": ["data:geo:PXD067099"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40855359", "parent_hash": "sha256:ec657df998049d47d8112d83f23349460cc0b080860c03df3f0b4eb09884c262", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:PXD067099 licence not read", "asset:data:geo:PXD067099 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Significant differential protein expressions were determined using a Bayesian moderated t-test implemented in the limma package v.3.54.2."], "order_basis": "analysis_only", "id": "assay:1"}]}