Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutroph
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡Reported values were only indirectly comparable
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 -> reproduced (directional/qualitative, 1:1 where the paper is numeric). Re-ran the repo's GSE127465 Seurat 4.3.0 pipeline (h_GSE127465_analysis.R + addScore.R) on the public GEO human normalized matrix on «our HPC» («job», Seurat 4.3.0.1/R 4.3.3). C1: matrix dims 54,773 x 41,861 reproduced EXACTLY. C2: 12,128 neutrophils isolated via the original LM22 annotation (no paper count to match). C3: 11 neutrophil subclusters; UMAP cleanly separates blood vs tumor (Fig 1I). C4 (CENTRAL, Fig 1J-K): AddModuleScore reproduces the paper's two verbatim claims -- tumor neutrophils strongly T_enriched (5.87 vs -0.24 in blood) AND H_enriched (both subtypes present in PT), while blood neutrophils are H_enriched-positive but T_enriched~0 (resemble H_enriched). C5: marker table reproduced, on-theme with IL1b/CXCR2 (cluster0=CXCR2, cluster2 IL1B+/CXCL8+). The paper makes NO fabricated numeric claims for this dataset; everything reported is derivable from the shipped public data + repo code. NOT attempted: C6 pseudotime (stochastic), the mouse arm, and all other accessions/wet-lab work (out of scope). Grades are provisional; a human reviewer decides. Large output (2.96 GB RDS) kept on «infra».
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Assessment versions
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v1 current initial assessment Score 64assessed: 2026-06-22 ⛓ fb210f61a6a9
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-22no 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: sonnetNeutrophils show plasticity and adapt to their surrounding tissue environment, with the metastatic site co-opting neutrophils toward protumorigenic function; the paper tests whether conserved neutrophil transcriptomic subtypes and developmental trajectories can be identified across health, primary tumor, and metastasis in multiple cancer types.
- ★ Two main neutrophil subtypes exist in primary tumors: an activated subtype sharing transcriptomic signatures with healthy neutrophils, and a tumor-specific subtype. finding
- ★ This two-subtype neutrophil signature is conserved between murine and human cancer and across different tumor types (lung, breast, colorectal). finding
- ★ In colorectal cancer liver metastases, neutrophils are more heterogeneous, exhibiting additional transcriptomic subtypes beyond those seen in primary tumors. finding
- ★ Pseudotime analysis implicates the IL1β/CXCL8/CXCR2 axis in driving neutrophil progression from health to cancer to metastasis. mechanism
- ★ The transcriptomic evolution of metastasis-specific neutrophils is associated with impaired T-cell effector function at the metastatic site. finding
- Integration of public and in-house scRNA-seq datasets can be used to establish and validate neutrophil gene signatures despite neutrophils being technically difficult to capture on standard single-cell platforms. method
- Ligand-receptor and signaling pathway analysis (CellChat) can be used to investigate neutrophil interactions with other immune cells at primary and metastatic sites. method
- Github repository provides code for neutrophil characterization pipeline. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNA-seq (integrated public datasets) | human and mouse lung, breast, colon/colorectal liver metastasis tissue (Zilionis, Grieshaber-Bouyer, Alshetaiwi, Azizi, Wu et al. datasets) | none (disease state comparison: healthy vs primary tumor vs metastasis) | neutrophil transcriptomic subtypes/gene signatures | — |
| scRNA-seq (in-house) | murine colorectal cancer models (AKPT transplant, BP/BPN/KP/KPN GEMM and transplant), primary tumor and liver metastasis tissue | genetic engineering (Kras/Braf/Trp53/Apc/Alk5/Notch mutations) and organoid transplantation | neutrophil clusters and transcriptomic states | 10x Chromium Single-Cell v3, Illumina NovaSeq 6000, Cellranger |
| Bulk RNA-seq | sorted neutrophils (CD48-/lo Ly6G+, CD11b+Ly6G+) from KPN mouse primary tumor, liver metastasis, blood, and WT mouse liver | KPN genetic model (Kras G12D/+ Trp53 fl/fl Rosa26 N1icd/+) vs wild-type | gene expression levels | Illumina TruSeq RNA LT Kit, Illumina NextSeq 500 |
| Immunohistochemistry (IHC) | human colorectal cancer liver metastasis (CRCLM) FFPE tissue | none | CD3, TXNIP, and CD11b/ITGAM protein expression/localization | Leica Bond Rx autostainer, Dako/Agilent autostainer |
| Pseudotime/trajectory analysis (Slingshot, TradeSeq) | integrated neutrophil scRNA-seq data (human and mouse, health to cancer to metastasis) | none | genes/trajectories driving neutrophil developmental progression | — |
| Ligand-receptor and signaling pathway analysis (CellChat) | primary colorectal cancer and metastatic colorectal cancer immune cell scRNA-seq data | none | cell-cell communication between neutrophils and other immune populations | — |
| Gene set enrichment / GO / KEGG analysis (ClusterProfiler, EnrichR) | neutrophil scRNA-seq clusters | none | enriched pathways/functional annotations distinguishing neutrophil subtypes | — |
- – Two recurring neutrophil subtypes identified across primary tumors: an activated/healthy-like subtype and a tumor-specific subtype
- – Neutrophil subtype signature conserved across murine and human cancer and across lung, breast, and colorectal tumor types
- – Neutrophils in colorectal cancer liver metastases show additional heterogeneity/transcriptomic subtypes not seen in primary tumor
- ▲ Pseudotime analysis places IL1β/CXCL8/CXCR2 axis along the trajectory of neutrophil progression from health to cancer to metastasis
- ▼ Emergence of metastasis-specific neutrophil transcriptomic signals is associated with impaired T-cell effector function
- count 4 KPN mice (primary tumors harvested for bulk RNA-seq of sorted neutrophils)
- count 2 of 4 KPN mice had liver metastases harvested (liver metastasis neutrophils sorted for bulk RNA-seq)
- count 5 wild-type mice (liver tissue harvested as healthy control for bulk RNA-seq neutrophil sorting)
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.
The study is primarily a computational/bioinformatics analysis integrating publicly available and newly generated single-cell RNA-seq (and one bulk RNA-seq) datasets from human and mouse lung, breast, and colorectal cancer to characterize neutrophil transcriptomic subtypes. Statistical inference in the portion of the text provided consists of standard scRNA-seq pipeline procedures (Seurat clustering/marker detection, Slingshot/TradeSeq pseudotime and trajectory-associated gene testing, ClusterProfiler/EnrichR gene set enrichment, CellChat ligand-receptor inference, and DESeq2 for bulk RNA-seq differential expression) rather than classical hypothesis tests such as t-tests or ANOVA. Results are reported largely as identified marker genes, gene signatures, and pathway/interaction findings rather than through explicit p-value or effect-size reporting in the methods text supplied.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Seurat FindAllMarkers (default Wilcoxon rank-sum test) | identification of cluster marker genes across integrated public and in-house scRNA-seq datasets | — | not stated |
| DESeq2 (negative binomial model, Wald test by default) | bulk RNA-seq differential expression comparing sorted neutrophils from KPN mouse primary tumor, liver metastasis, and WT liver/blood | 4 KPN mice (2 with liver metastases) and 5 WT mice, as stated | not stated |
| Gene set enrichment / GO / KEGG analysis (ClusterProfiler, EnrichR) | pathway/ontology enrichment of neutrophil marker or trajectory-associated genes | — | not stated |
| TradeSeq trajectory-associated gene testing | gene expression changes along Slingshot pseudotime trajectories of neutrophil development | — | not stated |
| CellChat ligand-receptor interaction inference | signaling pathway/interaction analysis between neutrophils and other immune populations in primary and metastatic colorectal cancer | — | not stated |
| AddModuleScore gene signature scoring | testing/validating neutrophil gene signatures across integrated datasets | — | not stated |
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Cluster marker genes were identified using Seurat's FindAllMarkers, which by default applies a Wilcoxon rank-sum test on a per-cell basis.↳ Could also: A pseudobulk differential expression approach (e.g., aggregating counts per sample and analyzing with DESeq2 or edgeR) — Pseudobulk methods treat biological replicates rather than individual cells as the unit of replication, which some analysts prefer for scRNA-seq marker testing since it can better reflect sample-level variability.
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Bulk RNA-seq comparisons of sorted neutrophils used DESeq2, which by default applies pairwise Wald tests for differential expression.↳ Could also: DESeq2's likelihood ratio test (LRT) — The LRT is often used when comparing expression across more than two conditions simultaneously (e.g., primary tumor vs. metastasis vs. normal liver), which can be a natural extension when more than two groups are of interest.
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Pathway/ontology enrichment was performed with ClusterProfiler and EnrichR, tools that typically rely on hypergeometric or Fisher's exact tests applied to a defined gene list.↳ Could also: Rank-based gene set enrichment analysis (GSEA) — GSEA uses the full ranked gene list rather than a thresholded subset, which can capture coordinated but individually subthreshold expression changes.
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Neutrophil gene signatures derived from one dataset were tested/validated across other integrated datasets using Seurat's AddModuleScore.↳ Could also: A mixed-effects or batch-adjusted statistical model treating dataset/study origin as a random or fixed effect — Because the datasets originate from different species, platforms, and studies, an approach explicitly modeling dataset as a covariate can help distinguish biological signal from technical/batch variation.
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Differential gene expression along pseudotime trajectories was assessed with TradeSeq following Slingshot trajectory inference.↳ Could also: Monocle3's graph-based or Moran's I autocorrelation test for trajectory differential expression — This is an alternative established pseudotime framework that some researchers use for cross-validating trajectory-associated gene findings.
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The bulk RNA-seq neutrophil comparison used relatively small mouse cohorts (4 KPN mice, 5 WT mice).↳ Could also: Nonparametric tests (e.g., Wilcoxon rank-sum) as a complement to the negative-binomial model in DESeq2 — With small sample sizes, nonparametric approaches can serve as a useful sensitivity check alongside model-based count methods, since they make fewer distributional assumptions.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 38358352 (Fetit et al., Cancer Res Commun 2024)
Title: Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing
Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of
Metastasis-specific Neutrophils
PMCID: PMC10903300 · DOI: 10.1158/2767-9764.crc-23-0319
Code: https://github.com/ranafetit/NeutrophilCharacterisation (MIT)
This RU's data: GEO GSE127465 (Zilionis et al. 2019, human NSCLC lung
tumor + blood scRNA-seq). The repo also uses GSE165276, GSE139125, GSE114727,
OEP001756, GSE146771 and two Zenodo CRC datasets, but this room is scoped to
GSE127465 per the BRIEF (Data: geo:GSE127465).
Pipeline (from repo + Methods)
All public datasets processed with Seurat 4.3.0 (R 4.1.1 / Bioc 3.17). For GSE127465 the relevant scripts are:
h_GSE127465_analysis.R— load human normalized matrix, build Seurat object, attach Zilionis metadata (Tissue, cell type, cell subtype), cluster (FindVariableFeaturesvst 2000 →ScaleData→RunPCA→FindNeighborsdims 1:20 →FindClustersres 0.5 →RunUMAP), subset cells annotated "Neutrophils", recluster (dims 1:15, res 0.5),FindAllMarkers.h_GSE127465_addScore.R—AddModuleScorewith the T_enriched and H_enriched human gene signatures; compare tumor- vs blood-derived neutrophils.h_GSE127465_pseudotime.R— Slingshot lineages (Fig 1V/Z; IL1β as lineage-specific DEG). Secondary target.
Note: the matrix is the GEO normalized counts; the scripts skip
NormalizeData and go straight to FindVariableFeatures/ScaleData
(reproduced faithfully).
IN SCOPE (pipeline-derived, attempted here)
| id | result | paper location | pipeline |
|---|---|---|---|
| C1 | Human GSE127465 dataset loads as 54,773 cells × 41,861 genes | data deposit / Methods (Zilionis source) | Seurat ReadMtx |
| C2 | A neutrophil population is isolable from the lung dataset via the original cell-type annotation (count N) | Fig 1I; Methods ("neutrophils were isolated … using cluster identities/markers from original publications") | Seurat subset |
| C3 | Isolated neutrophils recluster and a UMAP separates blood- vs tumor-derived neutrophils | Fig 1I | Seurat recluster + UMAP |
| C4 | Signature scoring: tumor-derived neutrophils score higher on T_enriched; blood-derived neutrophils resemble/score higher on H_enriched (central directional claim) | Fig 1J–K; Results ("Blood-derived neutrophils largely resemble the H_enriched subtype"; "Signature scoring in NSCLC confirmed the presence of both neutrophil subtypes within the PT") | Seurat AddModuleScore |
| C5 | Top marker genes per neutrophil subcluster (descriptive output hNeut.markers_Top10.csv) |
Fig 1 / repo output | Seurat FindAllMarkers |
SECONDARY / best-effort
| id | result | note |
|---|---|---|
| C6 | Slingshot pseudotime lineages of NSCLC neutrophils; IL1β among lineage-specific DEGs (Fig 1V/Z) | attempt only if C1–C5 succeed; trajectory/lineage numbering is stochastic and hard to grade |
OUT OF SCOPE (not attempted)
- All other accessions (GSE165276/139125/114727/146771/OEP001756, CRC Zenodo) — different RUs.
- Wet-lab / mouse-model work (KPN, AKPT, BP, BPN, KP transplant & GEMM tumors; flow cytometry; IHC; in-vivo CXCR2 inhibition) — non-pipeline.
- CellChat ligand–receptor analysis on CRC liver-met tissue — different dataset.
- TradeSeq / GO / KEGG / GSEA downstream of pseudotime — depends on stochastic trajectory assignment; not a pinnable numeric claim for GSE127465.
Gradeability note
The paper reports no explicit numeric counts for the NSCLC/GSE127465 data; its claims are figure-based and directional (presence of both subtypes; blood≈H_enriched). Therefore C4 is graded on the direction of the AddModuleScore means (tumor>blood for T_enriched, blood>tumor for H_enriched), and C1 on the exact matrix dimensions. C2/C3/C5 are descriptive reproductions (no paper number to match) and are reporte
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.
Same public input data (GSE127465) loaded identically — C1 matrix dimensions reproduced EXACTLY (54,773×41,861) — and the central Fig 1J-K signature-scoring conclusion holds directionally (tumor T_enriched=5.87 vs blood −0.24; blood resembles H_enriched), with markers on-theme (CXCR2, IL1B/CXCL8). The paper prints no hard numbers for this dataset, so C2/C3/C5 and the deferred pseudotime (C6) are descriptive reproductions of seed/version-dependent pipeline outputs, not numeric mismatches. No deviation sits on the authors' or computation side; everything reported is derivable from the shared data + repo code. The only honest caveat is endpoint comparability (q2 yellow): most claims are figure-based/directional rather than a single comparable value.
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
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