Genome-wide RNA profiling of long-lasting stem cell-like memory CD8 T cells induced by Yellow Fever vaccination in humans.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:26484272", "kind": "science-record", "revision": 6, "content_hash": "sha256:4e4b5733de17ed7cfb907caad7cf91c48409c3b81b8a0cc396a303f7ea8493f5", "created_at": "2026-11-01T15:40:13Z", "updated_at": "2026-11-01T21:20:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Genome-wide RNA profiling of long-lasting stem cell-like memory CD8 T cells induced by Yellow Fever vaccination in humans.", "doi": "10.1016/j.gdata.2015.06.024", "pmid": "26484272", "authors": ["Silvia A Fuertes Marraco", "Charlotte Soneson", "Mauro Delorenzi", "Daniel E Speiser"], "journal": "Genomics data", "year": 2015, "abstract": "The live-attenuated Yellow Fever (YF) vaccine YF-17D induces a broad and polyfunctional CD8 T cell response in humans. Recently, we identified a population of stem cell-like memory CD8 T cells induced by YF-17D that persists at stable frequency for at least 25 years after vaccination. The YF-17D is thus a model system of human CD8 T cell biology that furthermore allows to track and study long-lasting and antigen-specific human memory CD8 T cells. Here, we describe in detail the sample characteristics and preparation of a microarray dataset acquired for genome-wide gene expression profiling of long-lasting YF-specific stem cell-like memory CD8 T cells, compared to the reference CD8 T cell differentiation subsets from total CD8 T cells. We also describe the quality controls, annotations and exploratory analyses of the dataset. The microarray data is available from the Gene Expression Omnibus (GEO) public repository with accession number GSE65804.", "claims": [{"id": "claim-method", "statement": "We used Principal Component Analysis (PCA) to perform exploratory analysis in order to summarize and visualize the data, as well as to detect potential artifacts and effects of experimental and technical parameters.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26484272", "locator": "sections[10] \"Exploratory analysis\" paragraph 2", "quote": "We used Principal Component Analysis (PCA) to perform exploratory analysis in order to summarize and visualize the data, as well as to detect potential artifacts and effects of experimental and technical parameters."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data is available from the Gene Expression Omnibus (GEO) public repository with accession number GSE65804.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26484272", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The microarray data is available from the Gene Expression Omnibus (GEO) public repository with accession number GSE65804."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used principal component analysis (PCA) for exploratory analysis and made the source microarray data available through GEO accession GSE65804 at http://www.dtd.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65804.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:26484272", "locator": "sections[10] \"Exploratory analysis\" paragraph 2", "quote": "We used Principal Component Analysis (PCA) to perform exploratory analysis in order to summarize and visualize the data, as well as to detect potential artifacts and effects of experimental and technical parameters."}, {"asset": "source:pmid:26484272", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The microarray data is available from the Gene Expression Omnibus (GEO) public repository with accession number GSE65804."}, {"asset": "source:pmid:26484272", "locator": "sections[2] \"Direct link to deposited data\" paragraph 2", "quote": "http://www.dtd.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65804"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used principal component analysis (PCA) for exploratory analysis and made the source microarray data available through GEO accession GSE65804 at http://www.dtd.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65804.", "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:26484272"], "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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All populations were sorted from CD8 T-enriched samples by gating within live (Vivid aqua-) CD8 + CD16- cells after doublet exclusion.", "structure": {"pmcid": "PMC4583680", "section_index": 4, "section_title": "Experimental design", "row_count": 6, "column_count": 3}, "table": {"label": "Table 2", "caption": "T cell populations (“cell types”) isolated from each donor for microarray analysis. All populations were sorted from CD8 T-enriched samples by gating within live (Vivid aqua-) CD8 + CD16- cells after doublet exclusion.", "rows": [["Sample title in GEO database", "T cell population", "Markers used for flow cytometry sorting"], ["A2_NS4b_ naivelike", "YF-17D-specific CD8 T cells, naïve-like (Tscm)", "A2/NS4b + CCR7 + CD45RA +"], ["total_naive", "Naïve in total CD8 T", "CCR7 + CD45RA +"], ["total_Tscm", "Tscm in total CD8 T", "CCR7 + CD45RA + CD95 + CD58 +"], ["total_cm", "CM in total CD8 T", "CCR7 + CD45RA-"], ["total_effector", "Effectors (EM + EMRA) in total CD8 T", "CCR7-"]], "row_count": 6, "column_count": 3, "numeric_cells": 5, "header_rows": 1, "locator": "section[4]/table-wrap[2]#t0010"}}, {"id": "asset:table:pmid:26484272:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:05ea63dacef6d202c9db47fa90364db69aa8e3a80db0433f6ec3e95c592d19b3", "bytes": 1281, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#t0015", "version": null, "note": "Probes (and corresponding genes) with largest weight on the second principal component in PCA based on the 1% probes with highest variance. This component discriminates between male and female donors, and is dominated by probes measuring the expression of genes located on the sex chromosomes. Probes with positive weight are highly expressed in the female donors, and probes with negative weight are highly expressed in the male donors.", "structure": {"pmcid": "PMC4583680", "section_index": 10, "section_title": "Exploratory analysis", "row_count": 11, "column_count": 2}, "table": {"label": "Table 3", "caption": "Probes (and corresponding genes) with largest weight on the second principal component in PCA based on the 1% probes with highest variance. This component discriminates between male and female donors, and is dominated by probes measuring the expression of genes located on the sex chromosomes. Probes with positive weight are highly expressed in the female donors, and probes with negative weight are highly expressed in the male donors.", "rows": [["PC2, positive weight", "PC2, negative weight"], ["A_33_P3405911 (TSIX, chrX)", "A_23_P364792 (TXLNG2P, chrY)"], ["A_19_P00316565 (TSIX, chrX)", "A_21_P0006594 (TTTY15, chrY)"], ["A_19_P00327297 (XLOC_008015, chrX)", "A_23_P324384 (RPS4Y2, chrY)"], ["A_19_P00320438 (TSIX, chrX)", "A_21_P0006606 (XLOC_008323, chrY)"], ["A_19_P00326132 (TSIX, chrX)", "A_23_P259314 (RPS4Y1, chrY)"], ["A_19_P00321129 (TSIX, chrX)", "A_33_P3725324 (USP9Y, chrY)"], ["A_19_P00331623 (XIST, chrX)", "A_23_P137238 (KDM5D, chrY)"], ["A_19_P00321917 (TSIX, chrX)", "A_23_P160004 (UTY, chrY)"], ["A_19_P00806762 (TSIX, chrX)", "A_33_P3261353 (BCORP1, chrY)"], ["A_21_P0006456 (XLOC_008185, chrX)", "A_21_P0006651 (XLOC_008386, chr10)"]], "row_count": 11, "column_count": 2, "numeric_cells": 20, "header_rows": 1, "locator": "section[10]/table-wrap[1]#t0015"}}], "gaps": ["asset:data:geo:GSE65804 not downloaded", "asset:data:geo:GSE65804 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "We used Principal Component Analysis (PCA) to perform exploratory analysis in order to summarize and visualize the data, as well as to detect potential artifacts and effects of experimental and technical parameters."}}], "targets": [], "gaps": []}, "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": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The microarray data is available from the Gene Expression O", "aterials and methods Experimental design The microarray experiment detailed here was performed in th", "following vaccination [1]. The design of the microarray analysis was aimed to characterize the genom"], "order_basis": "paper_text_order:606", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used Principal Component Analysis (PCA) to perform exploratory analysis in order to summarize and visualize the data, as well as to detect potential artifacts and effects of experimental and technical parameters."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}