Single-cell dissection of chronic lung allograft dysfunction reveals convergent and distinct fibrotic mechanisms.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
PARTIAL, honest. Two metadata corrections first: (1) the seeded data accession GSE94555 is an unrelated 2017 IPF paper (text-mining false positive) - the paper actually deposits its CLAD scRNA-seq under GSE289881; (2) the cited code github.com/yuanqingyan/singleGEO @ cb24839 is NOT the figure pipeline but the authors' R toolkit to query/download/integrate public single-cell GEO data. Under brief P16 we reproduced what is feasible: C1 the deposited data structure of GSE289881 (10 samples / 96,002 raw cells) matches GEO+paper EXACTLY; C2 the shipped singleGEO toolkit runs end-to-end on «our HPC» and reproduces its documented example query hits (GSE158127, GSE142285) and a working integration (Seurat 5.3.0) EXACTLY; C3 the paper's central KRT17+KRT5- aberrant-cell population is detectable in the deposited CLAD data via the authors' own QC pipeline (qualitative, partial). 1:1 where reproducible; described well enough to reproduce these without author contact. NOT ATTEMPTED (the hard ~80%): the 1,576,567-cell cross-disease scVI atlas and its derived metrics (37 cell types, silhouette 0.72, NMI 0.79, 360/274-gene signatures, CellChat) - these require an UNSHIPPED scvi-tools integration pipeline over dozens of external datasets plus GPU, and are not independently verifiable from the shipped code+data (flagged provisionally, NOT an accusation).
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
-
v1 current initial assessment Score 75assessed: 2026-06-14 ⛓ 2b2c9e6f85f7
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusWhat are the molecular and cellular mechanisms driving chronic lung allograft dysfunction (CLAD), and which of these are CLAD-specific versus shared with other fibrotic lung diseases? The study tests whether integrative single-cell transcriptomics can distinguish CLAD-specific signatures from convergent fibrotic mechanisms.
- ★ CLAD harbors disease-specific cellular subsets including Fibro.AT2 cells, exhausted CD8+ T cells, and superactivated macrophages finding
- ★ Pathogenic KRT17+KRT5- epithelial cells represent a convergent fibrotic mechanism shared across CLAD and other fibrotic lung diseases mechanism
- ★ Donor-recipient cell deconvolution reveals recipient-derived stromal and immune cells with enhanced pro-fibrotic and allograft rejection pathways compared with donor counterparts finding
- ★ No significantly upregulated CLAD-specific disease-unique genes were detected; CLAD pathogenesis involves dysregulation of shared fibrotic pathways rather than entirely unique programs finding
- ★ Fibro.AT2 cells with upregulated coagulation cascade genes (FGG, FGA, HP) appear universally across CLAD samples, implicating coagulation dysregulation as a core CLAD mechanism mechanism
- ★ An integrated reference atlas of ~1.6 million cells combining CLAD with 15 published fibrotic lung disease studies, plus the SingleGEO toolkit resource
- A pseudo-bulk approach with offsets and ComBat-seq batch correction enables robust CLAD-specific signal detection despite small sample sizes method
- KRT17+KRT5- cells orchestrate local fibrotic niches via paracrine PDGF, TGF-β, GDF, and IL-4 signaling, suggesting targetability with PDGFR inhibitors like nintedanib mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-Seq (scRNA-Seq) | 4 CLAD lung explants (3 BOS, 1 RAS) at retransplantation, Northwestern University | none (disease tissue) | single-cell transcriptomes / cell type composition | — |
| single-nucleus RNA-Seq (snRNA-Seq) | 3 COVID-19 lung explants from fibroproliferative ARDS patients | none (disease tissue) | single-nucleus transcriptomes | — |
| integrative single-cell transcriptomic meta-analysis | 1,576,567 cells from CLAD, IPF, non-IPF ILD, COPD, COVID-19, HP, NSIP, scleroderma, sarcoidosis (15 published studies + newly generated) | none | cell type annotation, differential expression, pathway enrichment | scvi-tools integration, Leiden clustering, CellTypist annotation |
| image-based spatial transcriptomics | FFPE lung samples from 3 IPF patients | none | spatial localization of KRT17+KRT5- cells, myofibroblast/immune crosstalk | Xenium |
| donor-recipient cell deconvolution (genetic variant calling) | end-stage CLAD lungs at retransplantation | transplantation (allograft) | chimerism / donor vs recipient cell origin and transcriptional programs | — |
| pseudo-bulk differential expression with batch correction | 37 cell types across integrated fibrotic datasets | disease vs control | disease-unique genes, KRT17+KRT5- core signature | ComBat-seq, pseudo-bulk with offsets |
| FACS-sorted AT2 validation | AT2 cells from IPF lungs | none | differential gene expression concordance | — |
- – Integrated dataset encompassed 1,576,567 total cells including 141,734 newly generated CLAD and COVID-19 cells 1,576,567 cells (141,734 new)
- – No significantly upregulated CLAD-specific disease-unique genes detected across 37 cell types
- – Defined a refined 360-gene core signature for KRT17+KRT5- cells with EMT as the most enriched pathway 360 genes
- ▲ Fibro.AT2 cells upregulate coagulation cascade genes (FGG, FGA, HP) consistently across all CLAD samples
- ▲ CSTB, PLA2G7, and LGALS3BP significantly elevated in CLAD MoMs; LGALS3BP higher than IPF, PLA2G7 higher than COPD
- ▲ COPD unexpectedly showed significant upregulation of allograft rejection gene sets despite no transplantation q=0.018
- – Immune cells overwhelmingly recipient-derived while epithelial/endothelial compartments predominantly donor-derived; recipient fibroblasts and bronchial endothelium hyperactivated
- – Batch integration achieved successful batch removal with preserved biological variation silhouette=0.72, NMI=0.79
- count 1,576,567 cells (total integrated cells across all datasets)
- count 141,734 newly generated cells (new CLAD and COVID-19 cells)
- count 8 CLAD samples (total CLAD samples (4 NU explants + Khatri et al.))
- count 360-gene signature (KRT17+KRT5- cell core signature)
- count 37 cell types (cell types with sufficient representation for CLAD-comparative analysis)
- other silhouette score 0.72 (batch effect removal quality metric)
- other normalized mutual information 0.79 (preservation of biological variation)
- pvalue q value = 0.018 (COPD upregulation of allograft rejection gene sets in PPI analysis)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This study performed integrative single-cell RNA-seq analysis of 8 CLAD lung samples combined with approximately 1.6 million cells from 15 published fibrotic lung disease datasets. Differential gene expression was assessed using a pseudo-bulk approach with offsets and FDR control, with ComBat-seq applied for batch correction across heterogeneous multi-platform datasets. Cell clustering used the Leiden algorithm via scvi-tools, and results were reported primarily as differentially expressed gene lists, pathway enrichment categories, and q values, supplemented by spatial transcriptomic validation in IPF tissue.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pseudo-bulk differential expression with FDR control and offsets (stated to be comparable in power to generalized linear mixed models) | CLAD vs other fibrotic diseases across 37 cell types; KRT17+KRT5- core signature definition (360-gene set) | 8 CLAD samples; 37 cell types with sufficient representation | not stated |
| Pairwise differential expression comparisons | CLAD vs individual diseases (e.g., IPF, COPD) in monocyte-derived macrophages for CSTB, PLA2G7, LGALS3BP | null | not stated |
| Gene set enrichment analysis (GSEA) | Pathway enrichment across cell types and disease comparisons (collagen formation, ECM, interferon signaling, neutrophil degranulation, allograft rejection gene sets) | null | not stated |
| Protein-protein interaction (PPI) network analysis with q value | Allograft rejection gene set enrichment in COPD (q = 0.018 explicitly reported) | null | not stated |
| Leiden clustering algorithm | Cell type identification from integrated scRNA-seq/snRNA-seq data | 1,576,567 total cells | not stated |
| Silhouette score and normalized mutual information (NMI) as integration quality metrics | Validation of batch effect removal after scvi-tools integration (silhouette: 0.72; NMI: 0.79) | null | not stated |
-
Differential expression across disease groups used a pseudo-bulk approach with offsets and FDR control, chosen for performance with small sample sizes↳ Could also: Cell-level mixed-effects models (e.g., MAST, glmmTMB) with donor as a random effect, or edgeR/DESeq2 pseudo-bulk with explicit blocking on donor identity, could also have been applied — Mixed-effects models explicitly account for within-donor correlation at the cell level, which may further reduce inflation of Type I error when donor counts per group are small; benchmarking studies differ on which approach is preferable at n=8, so noting this tradeoff helps readers contextualize the choice
-
Batch correction was performed with ComBat-seq, selected by benchmarking four unnamed methods on AT2 cells↳ Could also: Harmony, scANVI (available within scvi-tools), BBKNN, or scDREAMER are widely used alternatives for multi-dataset single-cell integration — These methods differ in whether batch is modeled as a linear covariate (ComBat-seq) or as a latent variable; scANVI additionally leverages cell-type labels during integration, which can improve preservation of biological signal — relevant context for readers designing their own multi-cohort studies
-
Integration quality was summarized with two scalar metrics: silhouette score and normalized mutual information↳ Could also: kBET (k-nearest-neighbor batch-effect test), LISI (local inverse Simpson's index per cell type), or the full scIB benchmark suite are also commonly used for multi-metric integration evaluation — Multi-metric panels provide a more comprehensive picture of the tradeoff between batch removal and biological signal preservation; reporting multiple metrics is increasingly standard in integration benchmarking and can help readers judge robustness of the integration
-
Spatial transcriptomic validation of KRT17+KRT5- cell localization was performed in IPF tissue used as a CLAD surrogate↳ Could also: Multiplexed immunofluorescence (e.g., CODEX/PhenoCycler) or in situ hybridization (RNAscope) applied directly to available CLAD biopsy or explant sections would also provide spatial context — Authors explicitly acknowledge the use of IPF as a surrogate; noting these alternatives helps readers understand what additional evidence would extend spatial findings specifically to CLAD pathology
-
Cell type clustering relied on the Leiden algorithm with resolution parameters determined upstream in the scvi-tools pipeline↳ Could also: Supervised or semi-supervised label transfer from a reference atlas (e.g., via scANVI, Seurat label transfer, or SingleR) is also widely used, particularly when a high-quality reference such as the Human Lung Cell Atlas is available — Supervised transfer can reduce subjectivity in cluster boundary decisions and may improve cross-dataset reproducibility, which is particularly relevant when integrating 15+ studies with heterogeneous cell compositions
-
Differential expression results were reported as categorical gene lists and pathway enrichment categories without numerical fold-change magnitudes↳ Could also: Reporting log2 fold changes and their standard errors alongside FDR-adjusted q values, and AUC or Cohen's d as effect-size summaries, is also standard in single-cell DE reporting — Quantitative effect sizes allow readers to gauge biological magnitude independently of sample size and dataset composition, and facilitate future meta-analyses or cross-study comparisons
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41122970
Paper: Yan Y, et al. Single-cell dissection of chronic lung allograft dysfunction reveals convergent and distinct fibrotic mechanisms. JCI Insight 2025. PMID 41122970 · PMCID PMC12581678 · DOI 10.1172/jci.insight.197579
Code: https://github.com/yuanqingyan/singleGEO (commit cb24839, default branch main, R)
Data (authors' own): GEO GSE289881 (NOT GSE94555 — see note below)
IMPORTANT corrections to the harvested metadata
- The RU was seeded with
GSE94555, which is an unrelated 2017 IPF paper ("Single Cell RNA-Sequencing Identifies Diverse Roles of Epithelial Cells in Idiopathic Pulmonary Fibrosis", Yan Xu, Cincinnati; 6 samples, HiSeq 2500). This is a text-mining false positive. The paper's own data-availability statement deposits the CLAD scRNA-seq underGSE289881(submitted 2025-02-18; 10 samples: CLAD1–5 + Donor1–5; 10x Chromium V3; Cell Ranger 6.0; HG38). We reproduce against GSE289881. - The repo
singleGEOis not the analysis pipeline that produced the paper's integrated atlas. Per the paper, singleGEO is a computational toolkit for systematic identification / download / integration of public single-cell GEO datasets — a discovery+download+integration helper. It ships a vignette and bundled test data (GSE134174, GSE104154+GSE161648). Under brief rule P16, running this authors' tool on its documented data / on the paper's own data is a fully valid reproduction.
Pipeline-derived results
IN SCOPE (clearly specified, low-hanging — the 80/20 "20%")
| id | claim | pipeline | how reproduced |
|---|---|---|---|
| C1 | GSE289881 deposited scRNA-seq = 10 samples (5 CLAD + 5 donor), 10x V3, processed matrices public | Cell Ranger output / GEO deposit | Download GSE289881 to «infra»; load MTX/processed matrix with scipy; count samples, cells, genes — exact-comparable data-structure fact |
| C2 | singleGEO toolkit runs as documented (metadata query + Seurat-object build + within/cross-dataset integration) |
singleGEO R package vignette on bundled test data | install_github(...,ref=cb24839); run vignette functions (Get_Keyword_Meta, MakeSeuObj_FromRawRNAData, SeuObj_integration) on bundled GSE134174 / GSE104154+GSE161648; confirm documented objects/queries reproduce |
| C3 | KRT17+KRT5− aberrant (basaloid) cells are a core CLAD fibrotic signature | Seurat QC+clustering of CLAD samples | Build Seurat obj from GSE289881 CLAD samples via singleGEO MakeSeuObj_FromRawRNAData; QC per Methods (≥200 & ≤7500 genes, mito ≤10%, 3000 HVG); test for cells expressing KRT17 but not KRT5 — qualitative presence check |
OUT OF SCOPE (the hard ~80% — not attempted, with reason)
| reported result | why not attempted |
|---|---|
| Integrated atlas of 1,576,567 cells across many fibrotic diseases; 141,734 newly generated | requires assembling dozens of external GEO datasets + the unshipped scvi-tools integration pipeline; heavy GPU compute |
| 37 cell types; Leiden clustering of the full atlas | downstream of the unshipped scVI integration |
| Integration quality: silhouette 0.72, NMI 0.79 | metrics of the unshipped full-atlas integration; not derivable from shipped code+data |
| 360-gene KRT17+KRT5− core signature; 274 chemistry-biased genes | derived from cross-dataset DE on the full atlas; analysis code not shipped |
| CellChat interactome; ComBat-seq cross-disease batch correction | separate unshipped analyses |
Auditability / fabrication note (provisional, NOT an accusation): the headline integration metrics (1.5M cells, 37 types, silhouette 0.72, NMI 0.79, 360/274-gene signatures) are not independently verifiable from the shipped artifacts — the deposited code (singleGEO) is a download/query toolkit, not the integration pipeline, and the constituent external datasets are not enumerated as a runnable manifest. We do not claim these are wrong; we rec
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
All feasible, clearly-specified outputs reproduced 1:1: the GSE289881 deposit structure (10 samples / 96,002 raw cells) and the singleGEO toolkit's documented query hits (GSE158127, GSE142285) matched exactly, and the central KRT17+KRT5- population is detectable in all 9 processed samples (0.17-1.85%). However the paper's headline ~80% — the 1,576,567-cell scVI cross-disease atlas, 37 cell types, silhouette 0.72 / NMI 0.79, and 360/274-gene signatures — is not independently verifiable from the shipped artifacts, because the deposited code is a GEO toolkit rather than the atlas pipeline and the constituent datasets are not provided as a runnable manifest. This is an availability/completeness gap on the artifact side, not a demonstrated error or fabrication: no numeric discrepancy was observed where comparison was possible, so the core convergence claim is supported only in limited, qualitative form. Overall a solid partial reproduction with the central quantitative results left unverified.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.