Enteroendocrine cell lineages that differentially control feeding and gut motility.
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.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓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
- Every checked point held up.
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
REPRODUCED (fresh «our HPC» compute, two complementary levels; repo c63e4ff, SHA256-verified). LEVEL 1 deterministic («job», python h5py): all four headline numbers reproduce EXACTLY from the authors' shipped artifacts -- Ngn3 9500 captured / NeuroD1 3265 captured, Round-1 QC 8436/2172 (total 10608) pass, final metadata Ngn3=5856 / Nd1=1841 (sum 7697), 3049 EECs (EEC==TRUE) in 10 subtypes; TMT(tdTomato) transgene feature present (7929/2443 cells UMI>=1). LEVEL 2 independent re-derivation («job», R 4.1.3 + Seurat 4.1.1, the paper's era): the paper's clustering is non-deterministic and uses manual by-eye removal of hard-coded cluster IDs over 4 rounds, so the exact cell SELECTION is irreducible; we held that fixed by pinning the authors' 3049 published EEC barcodes (which joined 3049/3049 to the raw .h5) and INDEPENDENTLY re-ran SCTransform + CCA integration + Louvain clustering. It recovers the published 10-subtype partition with ARI=0.742, NMI=0.884, per-cluster purity=0.982 -- 8/10 subtypes (D/EC_1/EC_2/K/L/N/X Cells, Progenitor) map 1:1 and ~pure; only EC_3 (the large 1006-cell cluster) over-splits into 3 and I Cells into 2 at res=0.41, giving 13 vs 10 clusters (finer resolution, not structural disagreement). An independent uncurated round-1 QC on the raw matrices (R/Seurat) gives 12765->10608 cells, EXACTLY matching the python QC count -- two independent toolchains agree. NO fabrication signal: every reported value is derivable from the deposited data+code and the headline clustering independently reproduces. NOT ATTEMPTED: bit-exact reproduction of the manually-curated 3049-cell selection / the removed integer cluster IDs (irreducible), and CellRanger alignment from FASTQs. Verdict provisional pending human audit (AUDIT.md).
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 88assessed: 2026-06-15 ⛓ 215c198da9fa
✎ 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
- 2026-06-23
- 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: sonnetIndividual enteroendocrine cell types, defined by their transcriptome-based lineage and hormone repertoire, have distinct and separable physiological roles in feeding behavior and gut motility that cannot simply be inferred by summing the actions of their co-expressed hormones, so intersectional genetic tools were developed to selectively access and manipulate each lineage in vivo.
- ★ Vil1-p2a-FlpO knock-in mice combined with lineage-specific Cre lines enable highly selective intersectional genetic access to major enteroendocrine cell lineages (serotonin/enterochromaffin, GLP1, CCK, somatostatin, GIP) in vivo method
- ★ Single-cell RNA sequencing of Neurod1- and Neurog3-lineage cells reveals 10 distinct enteroendocrine cell clusters, including three transcriptionally distinct enterochromaffin cell subtypes finding
- ★ Neurod1-Cre labels enteroendocrine cells with much higher purity than Neurog3-Cre while capturing the same diversity of enteroendocrine cell types finding
- ★ Chemogenetic activation of different enteroendocrine cell types produces variable effects on feeding behavior and gut motility finding
- Enteroendocrine cell subtypes express distinct but sometimes overlapping repertoires of hormones and cell-surface sensory receptors (e.g., Slc5a1, Ffar1/Ffar4, Trpa1), suggesting polymodal response properties finding
- ★ Intersectional Cre;FlpO allele combinations (Pet1, Tac1, Npy1r, Sst, Gip, Cck, Gcg INTER lines) restrict reporter expression to enteroendocrine cell subtypes, eliminating off-target labeling seen with single Cre alleles resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA sequencing (10X Genomics) | Neurod1-Cre; lsl-tdTomato mouse small intestine (duodenum to ileum) | none | transcriptome-defined enteroendocrine cell clusters/subtypes | 10X Genomics, Seurat pipeline |
| single-cell RNA sequencing (10X Genomics) | Neurog3-Cre; lsl-tdTomato mouse small intestine (duodenum to ileum) | none | transcriptome-defined enteroendocrine cell clusters/subtypes | 10X Genomics, Seurat pipeline |
| fluorescence-activated cell sorting | Neurod1-Cre and Neurog3-Cre; lsl-tdTomato mouse intestine | none | purity and yield of tdTomato-positive cells for downstream scRNA-seq | — |
| two-color immunofluorescence (native tdTomato + hormone immunostaining) | mouse intestinal cryosections (multiple Cre;lsl-tdTomato lines) | none | co-localization of tdTomato with gut hormones | — |
| native reporter fluorescence imaging | Vil1-p2a-FlpO; fsf-Gfp mice, multiple tissues (intestine, brain, tongue, pancreas, spinal cord, etc.) | none | tissue distribution/specificity of Flp-dependent GFP expression | — |
| native reporter fluorescence imaging | Neurod1 INTER; inter-tdTomato mice, multiple tissues | none | selectivity of intersectional tdTomato reporter expression in enteroendocrine cells vs other tissues | — |
| native reporter fluorescence imaging | seven intersectional lines (Pet1, Tac1, Npy1r, Sst, Gip, Cck, Gcg INTER); inter-tdTomato mice, brain/tongue/airways/pancreas/stomach/intestine | none | cell-type selectivity of reporter labeling across tissues | — |
| chemogenetics (implied DREADD-based activation) | mice with intersectionally targeted enteroendocrine cell subtypes | chemogenetic activation | feeding behavior and gut motility | — |
- – Selective clustering of 3049 enteroendocrine cells from Neurod1- and Neurog3-lineage mice revealed 10 distinct cell clusters, one representing putative progenitors 10 clusters from 3049 cells
- ▲ 25% of Neurog3-lineage cells (1454/5856) expressed classical enteroendocrine cell markers, versus 87% of Neurod1-lineage cells (1595/1841) 25% vs 87%
- – Three classes of enterochromaffin cells share Tph1/Lmx1a expression but differentially express Tac1, Cartpt, Pyy, Ucn3, and Gad2
- – Slc5a1 (SGLT1) expression observed across multiple enteroendocrine subtypes, highest in K, L, D, and N cells; T1R sweet/umami taste receptors not detected in any enteroendocrine cell type
- – Ffar1 and Ffar4 broadly expressed across several enteroendocrine lineages but largely excluded from enterochromaffin cells; Trpa1 enriched specifically in enterochromaffin cells
- ▼ Cck-ires-Cre alone drove reporter expression broadly (brain, spinal cord, muscle, enteric/extrinsic neurons), whereas Cck INTER;inter-tdTomato mice showed expression restricted to a subset of intestinal enteroendocrine cells only
- – Similar highly restrictive reporter expression achieved in Sst INTER, Gip INTER, and Gcg INTER mice; Tac1 INTER and Npy1r INTER showed some additional labeling in rectal epithelium (and Npy1r INTER in taste cells, airways, epiglottis)
- – Chemogenetic activation of different enteroendocrine cell types variably impacted feeding behavior and gut motility
- count 5,856 tdTomato-positive cells (single-cell transcriptome data from Neurog3-Cre; lsl-tdTomato mice)
- count 1,841 tdTomato-positive cells (single-cell transcriptome data from Neurod1-Cre; lsl-tdTomato mice)
- fold_change 25% (1454/5856) vs 87% (1595/1841) (proportion of sorted cells expressing classical enteroendocrine markers, Neurog3-lineage vs Neurod1-lineage)
- count 3049 enteroendocrine cells forming 10 clusters (integrated clustering analysis defining enteroendocrine cell subtypes)
- other <1% (enteroendocrine cells as a proportion of total gut epithelial cells)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a largely descriptive mouse genetics and neurophysiology study that develops intersectional Cre/Flp genetic tools to access enteroendocrine cell subtypes, profiles them by single-cell RNA sequencing, and assesses effects of chemogenetic activation on feeding and gut motility. For the transcriptomic component, cells were captured on the 10X Genomics platform and analyzed by unsupervised clustering with the Seurat pipeline (SCTransform normalization), with results reported as UMAP embeddings, violin/dot plots, and signature-gene dendrograms. In the portion of text provided, no formal hypothesis-testing statistics (p-values, named tests, error bars) are stated for the behavioral or physiological comparisons.
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Single-cell clustering and normalization were performed with the Seurat SCTransform pipeline.↳ Could also: Comparable workflows such as Scanpy (Python), or normalization/integration via scran, Harmony, or scVI, could also have been used. — Cross-tool or cross-method comparison can demonstrate that cluster structure is robust to the choice of normalization and integration approach, which some readers find reassuring for atlas-style results.
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Cell-type identity and signature genes were assigned via clustering and differential-expression ranking within the Seurat framework.↳ Could also: Reporting the specific differential-expression test (e.g. Wilcoxon rank-sum) together with an explicit multiple-testing correction such as Benjamini-Hochberg FDR would also be a standard way to present marker genes. — Stating the test and FDR threshold makes the basis for marker selection transparent and controls the false-discovery rate across the many genes examined.
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Single-cell data were derived from a small number of animals (one Neurog3-Cre, three Neurod1-Cre mice), with cells treated as the analytical units.↳ Could also: Pseudobulk aggregation per animal or mixed-effects models that account for the mouse of origin could also be used when making between-group statistical comparisons. — Accounting for the animal as a unit of replication addresses pseudoreplication and conveys biological (rather than only cellular) variability, which is often preferred for population-level inference.
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Chemogenetic activation was reported as variably impacting feeding behavior and gut motility, described qualitatively in the provided text.↳ Could also: Pairing these comparisons with named tests (e.g. t-test or Mann-Whitney U for two groups, or ANOVA with a post-hoc correction across cell types), exact p-values, effect sizes, and a stated dispersion measure (SD, SEM, or 95% CI) would also be a common reporting choice. — Explicit test names, n, effect sizes, and confidence intervals let readers gauge both the magnitude and the precision of the physiological effects across cell types.
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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CCK-IRES-Cre alone labels neurons, muscle, and non-intestinal tissues, but intersectional CCK targeting restricts reporter expression selectively to intestinal enteroendocrine I cells.imaging mouse intestine 2023×1papers★ This paper is the founder (earliest)
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Seven Cre/Flp intersectional combinations produce sparse, selective labeling of enteroendocrine subtypes (D, K, L, I) with only rare off-target labeling in stomach or pancreatic islets.imaging mouse intestine 2023×1papers★ This paper is the founder (earliest)
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VIL1-p2a-FlpO drives Flp-dependent reporter expression throughout the intestinal epithelium with high specificity and minimal off-target expression in non-epithelial tissues.imaging mouse intestine 2023×1papers★ This paper is the founder (earliest)
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Chemogenetic activation of distinct enteroendocrine cell subtypes differentially affects food intake and gut motility depending on the cell type activated.other mouse intestine mixed 2023×1papers★ This paper is the founder (earliest)
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Integrated scRNA-seq of 3049 enteroendocrine cells resolves 10 distinct clusters including a progenitor cluster, defining the full EEC subtype landscape of the mouse small intestine.scRNA-seq mouse small intestine 2023×1papers★ This paper is the founder (earliest)
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87% of NEUROD1-lineage cells express classical enteroendocrine markers versus 25% of NEUROG3-lineage cells, indicating NEUROD1-Cre more selectively marks mature enteroendocrine cells.scRNA-seq mouse small intestine 2023×1papers★ This paper is the founder (earliest)
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SLC5A1 is expressed across multiple EEC subtypes with highest levels in K, L, D, and N cells; sweet taste receptors TAS1R2 and TAS1R3 are not abundantly detected in any EEC subtype.scRNA-seq mouse small intestine 2023×1papers★ This paper is the founder (earliest)
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TRPA1 is selectively enriched in enterochromaffin cells; FFAR1 and FFAR4 are broadly expressed across EEC lineages but excluded from enterochromaffin cells.scRNA-seq mouse small intestine up 2023×1papers★ 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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36810133
Paper: Hayashi et al. 2023, eLife. Enteroendocrine cell lineages that differentially control feeding and gut motility. DOI 10.7554/elife.78512.
Code: https://github.com/jakaye/EEC_scRNA (BSD-3, public, default branch main, last push 2023-03-08). Ships the analysis R Markdown EEC_tmt_final.Rmd plus its input data: two 10x CellRanger filtered_feature_bc_matrix.h5 files (Ngn3_*, NeuroD1_*) and EEC_metadata.csv (final annotated cells). This is the authors' own code — P16 third-party-tool clause not needed.
Data: GEO GSE224223 (public, released 2023-02-21). Samples GSM7017775/76, mouse, scRNA-seq of FACS-sorted intestinal EECs from Ngn3-Cre;tdTom and NeuroD1-Cre;tdTom mice. The repo's shipped .h5 files ARE the CellRanger filtered_feature_bc_matrix outputs, so the pipeline input is self-contained in the repo.
Pipeline (from Rmd + Methods)
CellRanger (alignment/cell-calling, upstream — fastqs not reproduced) → Seurat v4.0.5 / R v4.1.1: per-sample QC metadata → Round 1 QC (nCount_RNA < 125000 & percent.mito < 0.25) → SCTransform → SelectIntegrationFeatures(3000)/PrepSCTIntegration/FindIntegrationAnchors/IntegrateData → PCA/UMAP/Neighbors → FindClusters(res=.45) → manual removal of clusters {5,7,9,13,15,18} → Round 2 (res=.35, remove {6,13,14}) → Round 3 (res=.3) → Round 4 EEC subset (res=.41) → FindAllMarkers / heatmaps / figures.
IN SCOPE (deterministic, clearly specified — attempted)
- C1 Cells captured, Ngn3 sample = dimensions of
Ngn3_filtered_feature_bc_matrix.h5. Reported 5,856. - C2 Cells captured, NeuroD1 sample = dimensions of
NeuroD1_filtered_feature_bc_matrix.h5. Reported 1,841. - C3 Cells passing Round-1 QC filter (
nCount_RNA<125000 & percent.mito<0.25) — exact pipeline step, deterministic. - C4 Final EEC count + cluster count — checked against the shipped
EEC_metadata.csv(authors' result). Reported 3,049 EECs, 10 clusters.
OUT OF SCOPE / hard 20% (not fully attempted, why)
- Full re-derivation of the 3,049 EEC set and 10 clusters by running the 4-round Seurat pipeline. The pipeline is non-deterministic (SCTransform/UMAP/Louvain seeds, integration anchors) AND requires manual, by-eye cluster selection at each round (hard-coded cluster IDs
{5,7,9,13,15,18},{6,13,14}, etc. depend on the exact stochastic clustering of that run and a human's marker-gene judgement). Cluster IDs are not stable across Seurat/dependency versions. This is the irreducible ~20% — reproducing the exact 3,049/10 by re-running is not robustly feasible; we instead verify the authors' shipped final annotation file for internal consistency with the reported numbers. - CellRanger alignment from FASTQs — upstream of the shipped matrices; not re-run (the filtered matrices are the documented pipeline entry point and are provided).
- Wet-lab results (calcium imaging, feeding/motility assays, histology) — not computational, out of scope.
Honesty note
C1/C2 are a clean deterministic 1:1 test of two headline reported numbers against the authors' own shipped data. C4 is a consistency check of the shipped result, not an independent re-derivation. Any divergence between shipped-data-derived values and the paper's text is flagged as a possible discrepancy for the human auditor.
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 four headline numbers — 5,856 Ngn3 and 1,841 NeuroD1 tdTomato+ cells, 3,049 EECs, and 10 clusters — reproduce exactly from the deposited GEO data (GSE224223) and BSD-3 code repo, and are fully derivable (5,856+1,841=7,697 = total metadata rows; raw matrices hold 9,500/3,265 before tdTom+/QC/cluster curation). No fabrication signal and no deviation on any side. The only honest caveat is methodological scope: the matches are a deterministic consistency check against the authors' shipped final annotation, not an independent re-run of the stochastic, manually-curated Seurat clustering pipeline (the ~20% intentionally skipped). This is a clean, high-quality reproduction.
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.