Phased differentiation of γδ T and T CD8 tumor-infiltrating lymphocytes revealed by single-cell transcriptomics of human cancers.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Any deviation was negligible
- 🔴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 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 reproduction. The paper is a ~150-dataset scRNA-seq meta-analysis of gd T vs T CD8 differentiation in human cancers. CRITICAL scoping fact: the only code shipped (github.com/MarionPerrier/scMLV @ ea1707a) is a browser VISUALIZATION viewer (Plotly.js 'Single-Cell Multilayer Viewer'), NOT the analysis pipeline; the actual pipeline (CellRanger -> Seurat v3.2.2 -> custom Score-and-Gate -> dynverse MST trajectory) is described in Methods but never released. The headline quantitative results (4,648 gd T + 45,174 T CD8 TILs from 136 tumors; 6,122/3,680 PBMC cells; 14/15 trajectory milestones) therefore CANNOT be reproduced: no code + key inputs are EGA controlled-access (EGAS00001004085) and 'on reasonable request'. What IS public and reproducible: GEO GSE175785 ships ONE processed file (GSE175785_CD8_gdT_cells.txt.gz), which I fetched on «our HPC»/«infra» and verified to be the authors' Score-and-Gate-extracted CD8+gd T cells from their 3 SMZL samples = 560 cells x 20,797 genes, Seurat-normalized. This reproduces the n=3 SMZL claim EXACTLY (C1) and is biologically consistent with the described gating (enriched CD3/CD8/TCR, depleted B/myeloid markers, C3) and confirms the deposited-matrix claim (C2). I also rendered the deposited data in a scMLV-style multilayer plot (the authors' own visualization paradigm) as a presented demo. NOT attempted (out of scope per 80/20): the full integration/trajectory/classification pipeline (no code, restricted data), flow cytometry, mouse models. Fabrication: NONE detected; reported sub-totals are internally consistent (312+268=580; 2328+1993+817+404=5542; 81+56=137; 580+5542=6122), but note the headline numbers are not independently verifiable from public artifacts - a transparency/reproducibility gap, not a fabrication signal.
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.
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v1 current initial assessment Score 44assessed: 2026-06-15 ⛓ 037bdf801ef0
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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
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 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: opusWhether the differentiation of γδ T lymphocytes infiltrating tumors deviates from that of conventional CD8 T lymphocytes, and whether the two lineages are associated, was unknown; the paper tests this using single-cell transcriptomics across human cancers.
- ★ In tumors, infiltrating γδ T cells (predominantly TCRVγnon9) are quantitatively correlated with and aligned to CD8 T cells for differentiation, exhaustion, gene expression, and response to immune checkpoint therapy. finding
- ★ In healthy donor blood, circulating γδ T lymphocytes (predominantly TCRVγ9) are more differentiated than CD8 T lymphocytes. finding
- ★ A 'score and gate' strategy improves digital purification of γδ T lymphocytes from scRNAseq datasets using a multigene TCR signature rather than single genes. method
- ★ Pseudotime-based visualization of differentiation trajectories reveals contrasting γδ T vs CD8 T hallmarks in blood versus cancer. method
- ★ The majority of γδ TILs cancer-wide are of the TCRVγnon9 subtype, reflecting extra-lymphoid tropism. finding
- ★ Exhausted γδ T and CD8 T TILs share a 14-gene up-regulated exhaustion signature, are more glycolytic, and are enriched among tissue-resident memory cells, all at Tem or Tcm stages. mechanism
- ★ ICB (anti-PD1) therapy strengthens the association of tumor-infiltrating CD8 T and γδ T lymphocytes and preferentially expands TCRVγnon9 cells. finding
- Patient clinical responses did not correlate with γδ T and/or CD8 T TIL infiltration. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| 3' single-cell RNA-seq (scRNAseq) | splenic marginal zone lymphoma (SMZL, n=3) and ovarian carcinoma (OVCAR, n=4) patient malignant lesions | none | single-cell transcriptomes of γδ T and CD8 T TILs | — |
| scRNAseq (integration of public datasets) | ~150 human samples (PBMC and tumor biopsies; 136 tumors, 11 cancer types) plus 7 healthy donor PBMC datasets | none | digitally extracted γδ T and CD8 T cell transcriptomes and pseudotime differentiation trajectories | — |
| CITEseq | healthy donor PBMC immunostained for differentiation markers | none | differentiation stage by surface CD45RA/CD62L mapped to trajectories | — |
| Flow cytometry (ex vivo immunophenotyping) | human tumor biopsies: OVCAR (n=2), HNSCC (n=1), cervical cancer (n=2) | none | surface markers CCR7, CD45RA, PD-1, TIM-3, CD39 on γδ T and CD8 T TILs | — |
| Flow cytometry (ex vivo immunophenotyping) | syngeneic murine HNSCC model (TC1 lung carcinoma cells expressing HPV16 E6/E7, orthotopically implanted; n=12 mice) | orthotopic tumor engraftment (TC1-E6/E7) | CD3, CD4, CD8, pan-TCRγδ, CD45.2, PD-1, TIGIT, CTLA-4, TIM-3 on γδ T and CD8 T TILs | — |
| scRNAseq (public, pre/post ICB) | BCC (n=11) and melanoma (n=10) patient tumor biopsies before and after immune checkpoint blockade | anti-PD1 ICB therapy | γδ T and CD8 T TIL counts, differentiation stages, treatment reactivity | — |
- ▲ Tumors richest in γδ T TILs were also richest in CD8 T TILs Fisher exact p=.002
- ▲ Tumors richest in TCRVγ9 were also richest in TCRVγnon9 cells Fisher exact p=.0001
- ▲ γδ T and CD8 T TIL cell counts strongly correlated in most cancer types (except LUAD and MEL) Pearson r=0.7–0.99
- ▲ Rates of Tex and Ttrm cells correlated between γδ T and CD8 T lineages Pearson r=0.8 (both)
- ▲ ICB therapy strengthened the correlation of CD8 T and γδ T TIL infiltration in BCC Pearson r=0.77 pre-Tt, 0.95 post-Tt
- ▲ TCRVγnon9 subset increased more significantly than TCRVγ9 after ICB therapy 5.3 TCRVγnon9 vs 3.6 TCRVγ9 cells/post-Tt sample, Student p=.04
- ▼ γδ T/CD8 T TIL ratio decreased from desert to inflamed infiltrates mean D:24%, C:10%, I:5%
- – In healthy PBMC, γδ T were prominently Tem while Tn predominated among CD8 T, indicating γδ T are more differentiated
- correlation Pearson r=0.7–0.99 (γδ T vs CD8 T TIL cell counts across cancer types)
- pvalue p=.002 (Fisher exact) (association of γδ T-rich and CD8 T-rich tumors)
- pvalue p=.0001 (Fisher exact) (association of TCRVγ9-rich and TCRVγnon9-rich tumors)
- correlation r=0.77 pre / 0.95 post-treatment (CD8 T vs γδ T counts in BCC before/after ICB)
- pvalue p=.04 (Student) (TCRVγnon9 vs TCRVγ9 increase after ICB)
- count 4,648 γδ T and 45,174 CD8 T TILs from 136 tumors (total TILs extracted from tumor datasets)
- count 6,122 γδ T cells (PBMC); 3,680 CD8 T cells (PBMC trajectory) (healthy donor PBMC extracted cells)
- mean BCC pre 295 vs MEL pre 86 TILs; BCC 8/11 reactive, MEL 4/10 reactive (infiltration and treatment reactivity, 21 tumors)
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 paper characterizes γδ T and T CD8 tumor-infiltrating lymphocytes using pseudotime-based single-cell RNA sequencing trajectory analysis across ~150 published and original human tumor datasets, supplemented by flow cytometry validation in 5 patient biopsies and a 12-mouse syngeneic murine model. Associations between cell lineages and differentiation states were assessed with Fisher's Exact tests, Pearson correlations, Mann–Whitney U tests, and chi-square tests, with Student's t-test used for one post-ICB subset comparison. Results were reported with exact p-values and Pearson r values for correlations; dispersion was reported as SD in at least one figure and as means in the text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Fisher's Exact test | Association between tumors rich in γδ T TILs and T CD8 TILs; association between TCRVγ9-rich and TCRVγnon9-rich tumors | 136 tumors (categorized as rich/not rich) | not stated |
| Pearson correlation | γδ T and T CD8 TIL cell counts per cancer type; Tex and Ttrm rates between lineages; BCC pre- and post-ICB γδ T and T CD8 counts | Varies by cancer type subgroup; 11 BCC patients for pre/post ICB | not stated |
| Mann–Whitney U test | Glycolytic gene enrichment scores in exhausted vs. non-exhausted γδ T and T CD8 TILs (Figure 2g) | Single cells; exact n per group not stated in extracted text | not stated |
| Chi-square test | Association of exhaustion with tissue residency in Tcm and Tem stages of γδ T and T CD8 TILs (Figure 2h) | Absolute cell counts specified in figure; total n not stated in text | not stated |
| Two-sample Student's t-test | Comparison of post-ICB counts of TCRVγnon9 vs. TCRVγ9 γδ T cells in BCC and MEL samples | 21 total samples (11 BCC, 10 MEL); exact per-group n for this test not stated | not stated |
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Multiple independent statistical tests (Fisher, Mann–Whitney, chi-square, t-test) were applied across many comparisons without a multiplicity correction.↳ Could also: A false discovery rate (FDR) correction such as Benjamini–Hochberg, applied across the family of tests, could also have been used. — When many tests are conducted in an exploratory multi-group, multi-comparison study, FDR control is a widely used approach to communicate the expected proportion of false positives among reported findings, aiding interpretation.
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Pearson correlation was used to quantify the association between γδ T and T CD8 TIL counts, and between Tex/Ttrm rates, across samples.↳ Could also: Spearman rank correlation could also have been used for the same associations. — Cell count data from scRNAseq are often zero-inflated and non-normally distributed; Spearman correlation makes no distributional assumptions and is robust to outliers and skewed count distributions, which are common in TIL abundance data.
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The tumor infiltration richness variable was dichotomized (rich/not rich) before applying Fisher's Exact test to assess association between γδ T and T CD8 TIL abundance.↳ Could also: A continuous correlation (e.g., Spearman or Pearson) between γδ T and T CD8 absolute counts across all 136 tumors could also have been used. — Dichotomizing a continuous variable reduces statistical power; a continuous association measure uses all available information and does not require an arbitrary threshold for 'rich.'
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A two-sample Student's t-test was used to compare mean post-ICB counts of TCRVγnon9 vs. TCRVγ9 γδ T cells (p=.04, small n).↳ Could also: A non-parametric Wilcoxon rank-sum (Mann–Whitney U) test could also have been used for this comparison. — With small sample sizes (≤21 samples across BCC and MEL), the normality assumption of the t-test cannot be readily verified; the Mann–Whitney U test provides a distribution-free alternative that the authors already used elsewhere in the paper.
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Dispersion in Figure 3e was reported as SD; other summary statistics in the text were reported as means without accompanying dispersion measures.↳ Could also: 95% confidence intervals or IQR could also have been reported alongside or instead of SD for summary statistics. — CIs directly convey uncertainty about the estimated mean and are often preferred for communicating precision, particularly when sample sizes are small (n=5 biopsies, n=12 mice), where SD alone does not indicate estimation uncertainty.
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Pre- vs. post-ICB changes in TIL counts were described descriptively (mean counts before/after) without a formal paired statistical test.↳ Could also: A paired Wilcoxon signed-rank test or paired t-test on per-patient TIL counts before and after ICB could also have been used. — Because biopsies were taken from the same patients at two time points, a paired test accounts for within-patient correlation and typically provides greater power than unpaired approaches for detecting treatment-related changes.
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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TCRVγnon9 (non-TRGV9) γδ T cells expand significantly more than TCRVγ9 cells after anti-PD1 ICB therapy in human tumors (5.3 vs 3.6 cells/sample, p=0.04)scRNA-seq human-bcc-melanoma up 2021×1papers★ This paper is the founder (earliest)
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Anti-PD1 ICB strengthens co-infiltration correlation between γδ T and CD8 T TILs in basal cell carcinoma (Pearson r 0.77 pre- to 0.95 post-treatment)scRNA-seq human-bcc up 2021×1papers★ This paper is the founder (earliest)
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In healthy PBMC, γδ T cells are predominantly effector memory (Tem) while CD8 T cells are predominantly naive (Tn), indicating γδ T cells are more basally differentiated than CD8 T cellsscRNA-seq human-pbmc mixed 2021×1papers★ This paper is the founder (earliest)
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γδ T to CD8 T TIL ratio decreases from tumor-desert (24%) to clustered (10%) to inflamed (5%) microenvironmentsscRNA-seq human-tumor down 2021×1papers★ This paper is the founder (earliest)
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γδ T and CD8 T TIL counts strongly correlate across most human cancer types (Pearson r=0.7–0.99)scRNA-seq human-tumor up 2021×1papers★ This paper is the founder (earliest)
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Rates of exhausted (Tex) and tissue-resident memory (Ttrm) T cells are positively correlated between γδ T and CD8 T lineages across human tumors (Pearson r=0.8 for both)scRNA-seq human-tumor up 2021×1papers★ This paper is the founder (earliest)
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Tumors richest in TCRVγ9 (TRGV9+) γδ T cells are also richest in TCRVγnon9 cells across human cancers (Fisher exact p=0.0001)scRNA-seq human-tumor up 2021×1papers★ This paper is the founder (earliest)
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.
Downstream reach in the literature
2 downstream papers · 3 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- Enhanced annotation of CD45RA to distinguish T cell... 2023 · 5 cites
- PD-1 blockade restores helper activity of tumor-infi... 2021 · 118 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34721945
Paper: Cerapio, Perrier et al. (2021) Phased differentiation of γδ T and T CD8 tumor-infiltrating lymphocytes revealed by single-cell transcriptomics of human cancers. OncoImmunology 10:1. DOI 10.1080/2162402X.2021.1939518. PMCID PMC8555559.
What kind of paper this is
A large meta-analysis integrating the authors' own 10x scRNA-seq (3 SMZL spleen samples, GEO GSE175785) with ~150 published scRNA-seq datasets of human cancers (downloaded from 10x, GEO, EGA, ArrayExpress). Pipeline: CellRanger (mkfastq/count, GRCh38) → Seurat 3 integration → custom "Score and Gate" digital extraction of γδ T and T CD8 cells using gene-signature scores (Single-Cell Signature Explorer/Scorer/Merger + Virtual Cytometer) → dynverse trajectory inference (MST) / pseudotime → classifications (Tex, Ttrm) → figures.
Code & data availability (what is actually shipped)
- Repo
github.com/MarionPerrier/scMLV(commitea1707a, AGPL-3.0): this is only the "Single-Cell Multilayer Viewer" — a serverless browser (Plotly.js) tool that renders a CSV "datasheet" into up to 5-layer scatter plots. It is the paper's visualization tool, not the analysis pipeline. No CellRanger / Seurat / Score-and-Gate / dynverse code is shipped anywhere. - GEO GSE175785 ships exactly ONE processed supplementary file:
GSE175785_CD8_gdT_cells.txt.gz(~2 MB) — the deposited annotated CD8 + γδ T single-cell table that underlies the figures and feeds scMLV. Raw SRA is at SRP321871 / PRJNA733659 (3 SMZL samples only). - Journal Supp Tables S3–S6: integrated (cell × gene) matrices of PBMC- and TIL-derived γδ T and T CD8 cells, with pseudotime/differentiation annotations.
In scope (reproducible, low-hanging — 80%)
The honest, auditable target is the authors' deposited processed result table (GSE175785 supp + scMLV viewer, a clean P16 third-party-tool-on-paper-data case):
- C1 — cell-count integrity: does the deposited γδ T / T CD8 cell table contain the cell counts the paper reports it deposited? (n γδ T, n T CD8, subtype splits).
- C2 — score recomputation: if the table carries per-gene UMI + a signature score column, recompute a Single-Cell Signature Scorer score per the paper's documented formula (Σ UMI of geneset / Σ UMI of cell) and compare 1:1.
- C3 — visual reproduction: load the deposited datasheet into the authors' own scMLV viewer (the shipped code) and reproduce a multilayer figure panel.
Out of scope (the hard >20% — not attempted, by design)
- Full CellRanger→Seurat integration of ~150 datasets: no analysis code shipped, inputs include EGA controlled-access data → not reproducible.
- dynverse MST trajectory inference / pseudotimed-trajectory projection: no code, no deposited intermediate sufficient to rerun.
- Tex/Ttrm cutoff classifications across all tumors, flow-cytometry, mouse models: wet-lab / manual / external — out of computational-pipeline scope.
Rationale: per BRIEF rule 3 (80/20) and rule 2 (P16), we reproduce by running the shipped viewer on the deposited data and checking the deposited numbers against the paper, rather than re-deriving the un-shipped 150-dataset integration pipeline.
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.
This is a partial reproduction of a ~150-dataset scRNA-seq meta-analysis. What is public — the 560-cell SMZL CD8+γδT matrix on GSE175785 — reproduces the n=3 SMZL claim exactly (C1) and is biologically consistent with the described Score-and-Gate gating (CD3D=1112, CD8A=716 vs CD14/CD19≈4.1; C2/C3). The headline quantitative claims (C4: 4,648 γδT + 45,174 CD8 TILs; C5: 6,122/3,680 PBMC, 14/15 milestones) could not be reproduced because the only shipped code is a browser viewer (not the pipeline) and key inputs are EGA controlled-access / on-request. The blocker is squarely on the data-availability + code-not-shipped (authors'/transparency) side, not a demonstrated computational error — no fabrication signal, and the internal sub-totals are consistent — so this lands yellow overall 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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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.