Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in DSRCTs.
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.
- ✓The central claim held under reproduction
- 🟡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
- 🟡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
DESCRIBED WELL ENOUGH for a 1:1-in-distribution reproduction of the Int_sc 3' scRNA-seq pipeline; NOT exact-to-the-digit on the headline cell count. The paper reports NO original code ('available upon request'); the brief's code_url is the third-party Harmony tool (Harmony v1.0, cited in STAR Methods). Per P16 we reproduced by applying harmonypy (Python port of Harmony) + a standard scanpy QC/clustering pipeline to the paper's OWN GEO count matrices (GSE263523, 11 3' scRNA-seq libraries GSM8193966-76), all compute on «our HPC»/«infra» (final «job»; earlier jobs fixed conda-on-«infra» env + a harmonypy obsm-orientation bug). RESULTS: (C1) median genes/cell = 1,967 vs reported 1,971 -- essentially EXACT (0.2%); the total high-quality cell count is NOT reproducible to the digit because authors' QC/cell-calling thresholds are unspecified and GEO ships mostly RAW matrices -- we bracketed it [40,276 @floor500 .. 94,706 @floor200] and 51,671 falls inside. (C2) malignant fraction 85.0% vs 86.7% -- within 1.7 pts. (C3) microenvironment composition reproduces strikingly: identical rank order CAF>myeloid>lymphoid>mesothelial>endothelial and per-population proportions within ~1.5 pts (endothelial 9.5% identical). NO fabrication indicated -- the paper's 5 stromal counts sum exactly to the non-malignant total (6,890) and our independent re-run recovers the same structure. NOT ATTEMPTED (hard ~20%): exact cell count (unspecified QC), snMultiome/snATAC peak+WNN analysis, Visium spatial deconvolution, inferCNV-based malignant calling, SCENIC/Hotspot regulons, CellPhoneDB interactions, bulk RNA-seq DE, ChIP/CUT&RUN, and all wet-lab/functional assays -- separate pipelines/assays out of scope.
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v1 current initial assessment Score 76assessed: 2026-06-14 ⛓ a893971c629a
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- Reproduced
- 2026-06-14
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- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-09-19
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: sonnetDSRCT tumor cells display polyphenotypic differentiation despite being driven by a single oncogenic fusion (EWSR1::WT1); the paper tests whether this heterogeneity arises from cell-intrinsic transcriptional plasticity (linked to variable EWSR1::WT1 activity/DNA-binding) and/or tumor microenvironment-driven cues, rather than from genetic subclonal evolution.
- ★ DSRCT tumor cells cluster into consistent subpopulations with partially overlapping lineage- and metabolism-related transcriptional programs across patients and samples finding
- ★ High EWSR1::WT1 DNA-binding activity associates with most lineage-related states in vitro, in contrast to glycolytic and profibrotic states finding
- ★ Single-cell chromatin accessibility suggests EWSR1::WT1 binding site variability may drive distinct lineage-related transcriptional programs, supporting cell-intrinsic plasticity mechanism
- ★ Glycolytic and profibrotic tumor cell states localize within hypoxic niches at the periphery of tumor cell islets, suggesting a tumor-extrinsic microenvironmental contribution finding
- ★ A single-cell transcriptomics-derived epithelial signature is associated with improved patient survival finding
- ★ Copy number variations are rare and highly consistent across tumor cell clusters and samples, indicating heterogeneity is transcriptional rather than driven by genetic subclonal evolution finding
- ★ Variable EWSR1::WT1 transcript expression levels do not explain DSRCT tumor cell transcriptional heterogeneity finding
- ★ EWSR1::WT1 ChIP-seq in JN-DSRCT-1 cells identifies known and novel target genes and binding motifs related to EGR1/WT1 finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| 3' scRNA-seq (10x Genomics Chromium) | 10 fresh human DSRCT samples + 1 juxtatumoral peritoneal sample | none | cell type/cluster composition, gene expression programs | 10x Genomics Chromium 3' scRNA-seq |
| single-nucleus Multiome (snRNA-seq + snATAC-seq) | 1 fresh DSRCT tumor sample | none | transcriptome and chromatin accessibility | — |
| bulk whole exome sequencing (WES) | 10 samples matched to single-cell assays | none | copy number variation, mutations | — |
| bulk RNA-seq with CIBERSORTx deconvolution | 29 archived frozen DSRCT samples | none | estimated cell type proportions | CIBERSORTx |
| immunohistochemistry (IHC) | DSRCT tumor tissue sections | none | protein expression of THY1, CD68/CD163, CD3, WT1, AE1/AE3, DES, CD56 | — |
| ChIP-seq | JN-DSRCT-1 cell line | WT1 C-terminus antibody pulldown of EWSR1::WT1 vs isotype control | genome-wide EWSR1::WT1 binding sites/peaks and motifs | — |
| CytoTRACE / single-cell entropy analysis | scRNA-seq Int_sc dataset | none | predicted differentiation/stemness state of tumor cell clusters | — |
| spatial transcriptomics | DSRCT tumor tissue | none | spatial localization of glycolytic/profibrotic tumor cell states relative to hypoxic niches | — |
- – Identification of 51,671 high-quality cells clustering into malignant and non-malignant cell types across the integrated Int_sc dataset n=51,671 cells
- – Delineation of 13 tumor cell clusters: 6 lineage-related, 2 metabolic, 5 pseudostate-related
- ▼ Malignant cells comprised the large majority of detected cells, with CAFs and myeloid cells representing minor fractions malignant 92%; CAFs <5%; myeloid <4%
- – Inferred CNVs were rare and highly consistent across single-cell clusters within each sample; chromosome 5 gain was most frequent, chromosome 1q gain was not detected
- – EWSR1::WT1 transcript expression variation across cancer cells was limited and did not correlate with DGE-derived clusters
- – EWSR1::WT1 ChIP-seq identified 8,806 and 4,541 peaks in two replicates, with 1,587 consensus peaks shared between replicates 1,587 shared peaks
- – GGAGGA 6-mers were predominant in EWSR1::WT1 binding peaks; de novo motif analysis identified 8 significantly enriched motifs matching WT1+/-KTS, EGR1/2/3, and ZNF263
- – Comparison of pre- and post-chemotherapy samples from the same patient (GR4/GR4_PC) showed overall transcriptomic cluster stability but shifted cluster proportions, with increases in Cycling cells and Ribosomal_catabolic clusters
- count 51,671 high-quality cells (Int_sc scRNA-seq dataset after QC/filtering)
- other median 1,971 expressed genes per cell (Int_sc dataset quality metric)
- mean mean FRIP-score of 0.81% (EWSR1::WT1 ChIP-seq quality control in JN-DSRCT-1)
- other relative strand correlation 0.73 and normalized strand coefficient 1.02 (ChIP-seq peak-calling-independent quality metrics)
- count 8,806 and 4,541 peaks (replicate #1 and #2, respectively) (EWSR1::WT1 ChIP-seq peak counts per replicate)
- count 1,587 shared consensus peak regions (Overlap between EWSR1::WT1 ChIP replicates #1 and #2)
- other malignant cells 92% of detected cells; CAFs <5%; myeloid cells <4% (Cell type composition after excluding juxtatumoral peritoneal sample)
- other median overall survival approximately two years (DSRCT clinical prognosis)
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 characterized DSRCT heterogeneity using single-cell RNA-sequencing (51,671 cells from 12 samples across 5 patients), single-nucleus multiome (snRNA-seq + snATAC-seq), bulk RNA-seq (n=29), bulk whole-exome sequencing, ChIP-seq, and spatial transcriptomics. Core analytical strategies were unsupervised gene expression clustering with differential gene expression (DGE) analysis to define cell subpopulations, and gene set enrichment analysis (GSEA) with Gene Ontology terms to characterize cluster-level pathway activity. Supporting analyses included CIBERSORTx deconvolution of bulk RNA-seq, copy number inference, RNA velocity, stemness scoring, and evaluation of a scRNA-seq-derived epithelial signature for prognostic significance in independent cohorts.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential gene expression (DGE) analysis — specific statistical method not named in excerpt (likely Wilcoxon rank-sum or Seurat default); used to derive top 50 DEGs per cluster | Identification of marker genes for 13 tumor cell clusters and 6 non-malignant cell types in Int_sc dataset; DSRCT vs. other sarcoma histotypes (Figure 2B, Figure 1G) | 44,781 tumor cells; 51,671 total cells across 12 samples | not stated |
| Gene set enrichment analysis (GSEA) with Gene Ontology (GO) terms; threshold p < 0.05; top 5 enriched selected then ranked by gene ratio, top 3 displayed | Pathway enrichment per Int_sc tumor cell cluster (Figure 2C); EWSR1::WT1 ChIP-seq target gene enrichment (Figure S2C) | — | not stated |
| CIBERSORTx reference-based cell-type deconvolution | Estimation of cell-type proportions across 29 bulk RNA-seq DSRCT samples (Figure 1I) | 29 bulk RNA-seq samples | na |
| HotSpot coexpressed gene module analysis | Identification of correlated gene expression modules across DSRCT tumor cells (Figure 2D) | 44,781 tumor cells | not stated |
| CytoTRACE and single-cell entropy (differentiation/stemness scoring) | Stemness assessment across Int_sc clusters relative to non-tumor cells (Figures S1C, S1D) | 51,671 cells | not stated |
| ChIP-seq peak calling and differential binding analysis comparing WT1 ChIP to isotype control; quality assessed by FRIP score, RSC, and NSC; peak intersection across two replicates | EWSR1::WT1 binding target identification in JN-DSRCT-1 cell line (Figures S2A–S2B) | 2 ChIP replicates; 1 cell line | not stated |
| Copy number variation (CNV) inference from scRNA-seq (method not named in excerpt); compared to matched bulk WES | Genetic heterogeneity assessment across tumor cell clusters and samples (Figures 2E, 2F) | 10 scRNA-seq samples with matched WES | not stated |
| RNA velocity combined with trajectory inference (directed single-cell fate mapping); result was negative — no consistent trajectory identified | Transcriptional trajectory analysis across tumor cell clusters (data not shown) | — | not stated |
| Single-cell label transfer from fetal development reference gene expression atlas | Lineage annotation of Int_sc tumor cells against developmental cell types (Figure S1B) | — | not stated |
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Cluster marker genes were identified using DGE analysis treating individual cells as observations (specific test not named, likely Wilcoxon rank-sum per Seurat defaults)↳ Could also: Pseudobulk differential expression methods (e.g., DESeq2 or edgeR applied to per-sample aggregated counts) could also identify cluster markers while treating patient as the statistical unit — Pseudobulk approaches account for within-patient correlation across cells, which can reduce inflation of test statistics that arises when thousands of cells from the same individual are modeled as independent; they are increasingly recommended as a complement to single-cell-level tests in multi-patient designs
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GO pathway enrichment was reported using GSEA with a p < 0.05 threshold and secondary ranking by gene ratio; top 3 terms per cluster were displayed↳ Could also: Reporting Benjamini-Hochberg FDR-adjusted q-values (rather than a nominal p threshold) across all tested GO terms would also characterize enrichment significance while accounting for the large number of terms tested simultaneously — When hundreds of GO terms are tested per cluster, FDR-adjusted values provide a standard way to identify which enrichments are robust to multiple comparisons; gene-ratio ranking alone does not reflect term size or the breadth of testing
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Cell-type proportions in bulk RNA-seq were estimated using CIBERSORTx with scRNA-seq-derived reference signatures↳ Could also: Additional deconvolution methods such as EPIC, MuSiC, or BayesPrism could also use the same scRNA-seq-derived signatures and would allow cross-method comparison of proportion estimates — Different deconvolution algorithms apply different statistical frameworks and regularization strategies; concordance across methods strengthens confidence in estimated proportions, particularly for rare cell types such as lymphoid and endothelial cells
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ChIP-seq reproducibility was assessed by intersecting peaks across two replicates and reporting FRIP, RSC, and NSC quality metrics↳ Could also: Irreproducibility Discovery Rate (IDR) analysis is an additional standard approach for ChIP-seq replicate assessment that statistically ranks peak consistency based on rank concordance across replicates — IDR provides a probabilistic threshold for retaining reproducible peaks, complementing intersection-based approaches and giving a more quantitative estimate of which peaks are reliably detected across experiments
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Stemness was quantified as a continuous score per cell using CytoTRACE and single-cell entropy, with cluster-level comparisons shown↳ Could also: Reporting score distributions per cluster (e.g., violin or ridge plots with median and IQR) alongside a non-parametric test comparing clusters (e.g., Kruskal-Wallis with post-hoc Dunn correction) could also characterize differentiation heterogeneity quantitatively — Showing distributional spread within clusters and a formal comparison statistic would complement the directional interpretation (less differentiated vs. more differentiated) and characterize variability in stemness within each cluster
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EWSR1::WT1 mRNA expression variation across tumor cells was assessed by visual inspection of expression correlation with DGE-derived clusters (Figure S1H)↳ Could also: A Spearman or Pearson correlation between the EWSR1::WT1 expression score and cluster-level gene module activity scores could also quantify the association between fusion transcript level and transcriptional state — A correlation coefficient with a confidence interval would provide a quantitative, reproducible measure of the association (or lack thereof) between EWSR1::WT1 expression level and cluster identity, supplementing the visual cluster-overlay approach
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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EWSR1::WT1 ChIP-seq binding peaks are enriched at loci controlling developmental, stem cell, and fatty acid metabolic processesChIP-seq jn-dsrct-1 up 2024×1papers★ This paper is the founder (earliest)
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EWSR1::WT1 binding peaks are enriched for WT1 +/-KTS and EGR1/2/3 transcription factor motifs with GGAGGA hexamer predominantChIP-seq jn-dsrct-1 2024×1papers★ This paper is the founder (earliest)
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DSRCT tumor cell clusters are predominantly less differentiated than non-tumor cells with no consistent transcriptional differentiation trajectoryscRNA-seq human dsrct down 2024×1papers★ This paper is the founder (earliest)
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13 transcriptionally distinct DSRCT tumor cell subclusters identified, encompassing lineage-, metabolic-, and pseudostate-related programsscRNA-seq human dsrct 2024×1papers★ This paper is the founder (earliest)
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EWSR1::WT1 fusion transcript expression is uniform across DSRCT malignant cells and does not correlate with transcriptional cluster identityscRNA-seq human dsrct none 2024×1papers★ This paper is the founder (earliest)
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Malignant cells constitute ~92% of DSRCT single-cell transcriptomes, with CAFs <5% and myeloid cells <4%, indicating a tumor-dominated microenvironmentscRNA-seq human dsrct 2024×1papers★ This paper is the founder (earliest)
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Chromosome 5 gain is the most frequent somatic CNV in DSRCT and is consistently shared across intratumoral single-cell clusters within each patientWES human dsrct up 2024×1papers★ This paper is the founder (earliest)
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Data lineage
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38781959
Paper: Henon C, Vibert J, Eychenne T, et al. (2024) Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in DSRCTs. Cell Rep Med 5:101582. PMID 38781959 / PMC11228554 / DOI 10.1016/j.xcrm.2024.101582.
Nature of paper: Original research. Single-cell / single-nucleus multiomics of Desmoplastic Small Round Cell Tumors (DSRCT): 3′ scRNA-seq ("Int_sc" dataset), snMultiome (snRNA + snATAC), Visium spatial transcriptomics, bulk RNA-seq, plus wet-lab (ChIP/CUT&RUN, functional assays).
Code / data artifacts
- Code: The paper states "This paper does not report original code. All custom
code used for the analyses was written with existing software ... and is available
upon request." → No authors' repository. The brief's
code_url(github.com/immunogenomics/harmony) is the third-party Harmony batch-integration tool (Korsunsky et al.), cited in the STAR Methods key-resources table as Harmony v1.0. Per P16, applying this third-party tool to the paper's own data is an equally valid reproduction. Other named software: Cell Ranger v3.0.2, Seurat v3.1.4/4.0.4/4.1, R v3.5.1/4.3.0. - Data: GEO GSE263523 (SuperSeries), public, count-matrix format. The 3′ scRNA-seq matrices are samples GSM8193966–GSM8193976 (11 tumor + 1 juxtatumoral = 12 libraries) plus the snMultiome RNA matrix (GSM8193977). Spatial (Visium), ChIP/CUT&RUN bigWigs, and bulk RNA-seq are separate subseries. Note: most 3′ matrices are shipped as raw (unfiltered, ~6.8M-barcode whitelist; 20 MB barcode files), a few as filtered (20–30 KB barcode files), so reaching the authors' exact post-QC cell set requires their (unspecified) cell-calling + QC thresholds.
In scope (pipeline-derived, attempt) — the "Int_sc" 3′ scRNA-seq pipeline
| id | reported claim | paper location | how to reproduce |
|---|---|---|---|
| C1 | "we identified 51,671 high-quality cells ... with a median of 1,971 expressed genes per cell" | Results §1, Fig 1 | Load the 12 3′ scRNA matrices from GEO, run standard QC + Harmony integration (scanpy/harmonypy = Python port of the same Harmony algorithm); count cells, median genes/cell |
| C2 | "Tumor cells (n = 44,781)" vs non-malignant (6,890) → ~86.7 % malignant | Results §1, Table S2A | After integration + clustering, split malignant vs non-malignant by canonical markers; compare the fraction (robust to exact QC) |
| C3 | microenvironment composition: CAFs 2,360, myeloid 1,966, lymphoid 1,126, mesothelial 784, endothelial 654 (Σ = 6,890) | Results §1, Fig 1F/1G, Table S2A | Marker-based annotation of non-malignant clusters (COL1A1/SPARC, C1QA/CD68, NKG7/CD3E, CAV1/MSLN, VWF/PECAM1); recover the populations + relative proportions |
| C4 | 12 3′ scRNA-seq libraries incl. one juxtatumoral peritoneal sample (3,063 cells) | Results §1, Table S1A | Bookkeeping vs GEO sample list; per-sample barcode/cell counts |
Out of scope / not attempted (the hard ~20%)
- Exact 51,671 to the cell. QC thresholds (min genes, max mito %), the doublet-removal step, and the Cell Ranger cell-calling that produced the authors' filtered set are not specified in the paper, and GEO ships mostly raw matrices. We reproduce the headline number with a standard QC pipeline and report the value we obtain + sensitivity, but do not claim a to-the-cell match.
- snMultiome (snATAC) peak/WNN analysis, Visium spatial deconvolution, SCENIC/ Hotspot regulons, CellPhoneDB interactions, bulk RNA-seq DE, ChIP/CUT&RUN, and all wet-lab/functional results — separate pipelines/assays, not attempted here.
- CNV-based malignant calling (inferCNV) — the paper labels tumor cells via DSRCT-specific features (EWSR1-WT1 signature); we approximate malignant vs non-malignant by canonical lineage markers, which is sufficient for the composition fr
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
A solid, in-distribution reproduction of the DSRCT single-cell landscape with no fabrication signal: median genes/cell reproduces to 0.2% (1,967 vs 1,971), the malignant fraction lands within 1.7 pts (85.0% vs 86.7%), and the five-population microenvironment recovers in identical rank order within ~1.5 pts. The deviations are on our-methodology / authors'-incompleteness side — the paper ships no code and omits QC thresholds while GEO provides raw matrices, so the headline 51,671 count can only be bracketed [40,276–94,706] and absolute counts run ~12-22% low. Severity is moderate but direction- and magnitude-preserving, and the central conclusion holds, so overall yellow rather than red.
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.
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