Spns1-dependent endocardial lysosomal function drives valve morphogenesis through Notch1-signaling.
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 authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- 🟡A deviation arose in the data or preprocessing
- 🟡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
Described well enough to reproduce: YES. The paper's own GitHub repo (MercaderLabAnatomy/PUB_Chavez_et_al_2023, commit 4f9f7ef) ships the R/Seurat downstream snRNA-seq notebook, and GEO GSE246850 provides the processed 10x count matrices (12 MB, no fastq/cellranger needed). We re-implemented the per-sample QC stage faithfully (Read10X -> fixed-threshold filter nFeature 200-5000 / percent.mt<20 / nCount<10000 -> mt-gene removal -> SCTransform -> scDblFinder 1.14.0 singlet subset), pinned to the notebook's actual sessionInfo env (R 4.3.1, Seurat 4.3.0.1, SeuratObject 4.1.3, scDblFinder 1.14.0), and ran it on «our HPC». Outcome = essentially 1:1 within stochastic tolerance: all four reported final cell counts (sib_1 1673, sib_2 2193, mut_1 1643, mut_2 1940) reproduce within 1-4% (1656/2131/1604/1869). The deterministic QC filter alone gives 1717/2229/1687/1959; the entire residual is the scDblFinder doublet step (only non-deterministic step; reported finals lie between our filter and our doublet-removed counts). No fabrication concern: values are fully derivable from public data + public code. NOT attempted (honest 20%): the 18-cluster cell-type annotation, DE (Libra/edgeR), and notch1b/lysosome-gene findings (manual annotation + seed-sensitive Seurat integration); cellranger re-run from fastq (redundant with the public matrices); and all imaging-derived heart-parameter / image-processing results (no public image data, wet-lab/imaging not reproducible from any accession). Status 'partial' = the clearly-specified deterministic pipeline output reproduced cleanly; the seed/annotation-heavy downstream was deliberately not chased.
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 78assessed: 2026-06-14 ⛓ 30680d3604ee
✎ 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
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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: opusThe paper tests whether and how cell-type-specific autophagy-lysosomal degradation functions during cardiac valve development, hypothesizing that endocardial lysosomal activity (via Spns1) is required for proper valve morphogenesis and is coupled to a developmental signaling pathway.
- ★ Autophagosomes, autolysosomes and lysosomes accumulate preferentially in the atrioventricular canal (AVC) and outflow tract (OFT)/bulboventricular regions and their nascent valves during zebrafish heart development finding
- ★ Spns1 deficiency (nrs mutant) impairs endocardial lysosomal function and disrupts valve formation and cardiac function finding
- ★ Lysosomal degradation plays a cell-autonomous (endocardial-specific) role in cardiac valve morphogenesis mechanism
- ★ Impaired lysosomal function alters notch1-signalling in nascent valves, linking lysosomal processing to valve development mechanism
- ★ Endocardial-specific overexpression of spns1 and notch1 rescues features of valve formation and function in nrs mutants finding
- ★ A new knock-in endogenous autophagosome reporter, TgKI(mRFP-Lc3), enables unbiased in vivo tracking of autophagosome/autolysosome vesicles across cardiac tissues resource
- An in vivo 3D+t light-sheet imaging and segmentation pipeline allows tracking and automated regional quantification of autophagic vesicles in the beating larval heart method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| in vivo 3D+t light-sheet microscopy of autophagosomes/autolysosomes | Tg(β-actin2:mRFP-GFP-Lc3) zebrafish larval heart (48,56,72,96 hpf) | chloroquine treatment (2 mM, 3 h) to block autophagic flux | number of mRFP-GFP+ (autophagosome) and mRFP+-only (autolysosome) puncta in ventricle, AVC and OFT regions | light-sheet microscopy, retrospective gating |
| in vivo light-sheet microscopy with knock-in endogenous Lc3 reporter | TgKI(mRFP-Lc3) crossed into endocardial Tg(fli1a:GFP) and myocardial Tg(myl7:GFP) zebrafish larvae | none | mRFP+ puncta (autophagosomes/autolysosomes) localization in endocardium vs myocardium near AVC/OFT | light-sheet microscopy |
| in vivo lysosome imaging with LysoTracker dye | Tg(cmv:EGFP-Lc3) zebrafish larvae (48-96 hpf) | LysoTracker Deep Red staining | accumulation of LysoTracker+ acidic lysosomal puncta in ventricle/AVC/OFT | light-sheet microscopy, LysoTracker Deep Red |
| in vivo lysosome imaging with transgenic Lamp2 reporter | TgBAC(lamp2:RFP) crossed into Tg(kdrl:EGFP-CAAX) endocardial and Tg(myl7:GFP) myocardial zebrafish larvae | none | lamp2:RFP+ lysosomal puncta in endocardium and myocardium of AVC | light-sheet microscopy |
| immunostaining on fixed cardiac sections | Tg(cmv:GFP-LC3);(lamp2:RFP) zebrafish larvae | none | colocalization of GFP/RFP double-positive puncta with ALCAM-demarcated myocardium at AVC valves | antibodies against GFP, RFP, ALCAM |
| single-nuclear (single-nuclei) transcriptome analysis | nrs (spns1) mutant zebrafish heart vs siblings | spns1 retroviral insertion mutant (loss of function) | cell-type-specific differential expression of lysosome-related genes and notch1-signalling alterations (endocardial-specific) | — |
| comparative morphology and function imaging (light-sheet, confocal, electron microscopy) | nrs mutant vs sibling zebrafish hearts | spns1 mutation | endocardial organization, valve formation, blood flow (retrograde), heart morphology/ultrastructure | light-sheet, confocal and electron microscopy |
| endocardial-specific rescue / overexpression | nrs mutant zebrafish heart (endocardium) | endocardial overexpression of spns1 and notch1 | rescue of valve formation and function | — |
- ▲ Autolysosomes (mRFP+-only puncta) increase during heart tube maturation from 48 to 96 hpf in ventricle, AVC and OFT
- ▲ Increase in mRFP+ autophagosome puncta during development was highest in endocardium close to AVC and OFT compared to rest of ventricle
- ▲ LysoTracker+ lysosome accumulation from 48 to 96 hpf was highest in AVC and OFT, prominent in developing valves at 96 hpf
- ▲ lamp2:RFP lysosomal puncta increase particularly in the AVC endocardium and myocardium, peaking at 72 hpf
- – nrs larvae display abnormal endocardial organization, impaired valve formation and retrograde blood flow
- – Endocardial-specific overexpression of spns1 and notch1 rescued features of valve formation and function in nrs mutants
- – Single-nuclear analysis revealed endocardial-specific differences in lysosome-related genes and notch1-signalling alterations
- other chloroquine 2 mM, 3 h treatment (lysosomal inhibitor pretreatment before imaging)
- other imaging stages 48, 56, 72, 96 hpf (developmental timepoints of valve formation)
- pvalue ∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001, ∗∗∗∗p ≤ 0.0001 (two-way ANOVA) (significance thresholds for puncta quantification)
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 zebrafish developmental biology study combined live fluorescence imaging with automated puncta quantification to track autophagosome and lysosome accumulation across four cardiac regions and four larval stages. Regional comparisons were made with two-way ANOVA, with results displayed as individual-animal values overlaid with median and quartile summaries. The provided text is truncated before the snRNA-seq and functional-rescue analyses, so the full statistical picture is only partially visible.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| two-way ANOVA | Comparison of fluorescent puncta counts (autophagosomes, autolysosomes, lysosomes) between cardiac regions (ventricle vs. AVC) across developmental stages (48, 56, 72, 96 hpf) — Figures 1D, 1E, 1F, 2D, 2E, 2F | individual larvae (each dot = one larva); exact n per group not stated in provided text | not stated |
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Statistical significance was reported using asterisk threshold bands (p ≤ 0.05, ≤ 0.01, ≤ 0.001, ≤ 0.0001) rather than exact p-values↳ Could also: Report exact p-values for each comparison (e.g., p = 0.003 rather than p ≤ 0.01) — Exact p-values allow readers to assess the full range of evidence and are increasingly required by journals; they also facilitate meta-analysis and replication efforts
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Dispersion in graphs was conveyed via median and interquartile range with individual data points↳ Could also: Add 95% confidence intervals around the median or mean, or report mean ± SD alongside the individual-point plots — CIs directly communicate uncertainty about the group estimate and are useful when comparing across studies; SD conveys biological variability and is common in small-n developmental biology papers
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Two-way ANOVA was used to compare puncta counts across region × time combinations; post-hoc correction method was not stated↳ Could also: Explicitly name and apply a post-hoc correction (e.g., Tukey HSD, Dunnett, or Bonferroni) after the omnibus ANOVA, and report which specific pairwise contrasts were tested — When an ANOVA is followed by multiple pairwise comparisons, a named post-hoc procedure controls the family-wise error rate and makes the inferential logic transparent to readers
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Puncta counts per larva were compared between regions with parametric ANOVA↳ Could also: A non-parametric alternative such as the Kruskal-Wallis test with Dunn's post-hoc could also be used for the region × stage comparisons — Fluorescence puncta counts in small developmental samples are often right-skewed and may not meet ANOVA normality assumptions; a rank-based test makes no distributional assumption and can be reported alongside ANOVA as a robustness check
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Effect sizes are not reported for any comparison↳ Could also: Report a standardized effect size such as eta-squared (η²) or partial η² for the ANOVA, or Cohen's d for key pairwise contrasts — Effect sizes quantify biological magnitude independently of sample size and p-value, enabling readers to judge practical relevance and compare across studies
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Sample sizes (n per group per time point) are not explicitly stated in the text or figure legends visible in the excerpt↳ Could also: Provide exact n for every group in figure legends or a supplementary table, and include a brief power or sample-size rationale — Explicit n allows readers to evaluate whether group sizes are sufficient to detect biologically meaningful differences and supports reproducibility assessment
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-39720516
Paper: Chávez et al. 2024, iScience. Spns1-dependent endocardial lysosomal
function drives valve morphogenesis through Notch1-signaling.
Repo: https://github.com/MercaderLabAnatomy/PUB_Chavez_et_al_2023 (authors' own;
last push 2024-10-25; commit 4f9f7ef62195547de7b4ddaf4b2ed546862b5418; not archived).
Data: GEO GSE246850 — 4 samples (sib_1, sib_2, mut_1, mut_2), processed 10x
count matrices (barcodes/features/matrix), GSE246850_RAW.tar = 12 MB. No fastq
download / cellranger needed for the downstream analysis.
Repo components
snRNASeq/Myra_spns_snRNAseq_submission.ipynb— R/Seurat downstream analysis of the snRNA-seq count matrices (QC → SCTransform → scDblFinder → Seurat integration → clustering → cell-type annotation → DE via Libra/edgeR → GO). IN SCOPE. Pipeline = R 4.3.1 + Seurat 4.3.0.1 + scDblFinder 1.14.0 + harmony (per notebooksessionInfo()). Input = the GEO matrices.snRNASeq/Cell_ranger/*.sh— cellranger v6.0 from raw fastq. OUT OF SCOPE for 80/20: the processed matrices are already public on GEO, so re-running cellranger from SRA fastq is the redundant heavy 20%. (Could be done on «our HPC» but adds no new auditable checkpoint the matrices don't already give.)Heart_parameter_measurements/*.ipynb— cardiac function/heartbeat/RMSSD/size from light-sheet/confocal imaging of larval hearts. OUT OF SCOPE: the imaging raw data is not in any public accession (GEO is sequencing only) → wet-lab / imaging-derived, not reproducible from public data.image_processing/*.{r,py}— region-props on microscopy images. OUT OF SCOPE: same reason (no public image data).
In-scope reproduction targets (deterministic, clearly-specified)
The Methods state the final per-sample cell counts after QC explicitly:
"Cells with a minimum of 200 genes and less than 5000 genes per cells were chosen for quality purposes. The final number of cells used for further processing was as follows: sib_1: 1673 cells, sib_2: 2193 cells, mut_1: 1643 cells, mut_2: 1940 cells."
These are the cleanest checkpoints: a fixed-threshold QC filter + scDblFinder doublet
removal applied to the public matrices, with seeds set in the notebook
(set.seed(1), MulticoreParam(RNGseed=1234)). Primary 1:1 comparison.
Secondary (reported, less deterministic — depends on integration/clustering seed+versions+manual annotation): "18 cell populations including 3 myocardial and 5 endocardial subclusters" (Fig 6A). Attempted as a bonus / structural check only.
Not attempted (the hard 20%, stated honestly)
- Exact cluster identities / DE gene lists / GO enrichment / notch1b & lysosome-gene findings: depend on manual cluster→cell-type annotation and seed/version-sensitive integration; not a clean numeric 1:1.
- cellranger re-run from fastq (redundant with public matrices).
- All imaging-based heart-parameter and image-processing results (no public data).
Env discrepancy to flag
Paper Methods say "R (v4.0) ... Seurat (v4.0)"; the notebook's own sessionInfo()
says R 4.3.1 / Seurat 4.3.0.1 / scDblFinder 1.14.0 (Bioconductor 3.17). We pin to
the notebook's actual versions (the executed environment), and note the paper's
text understates them.
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.
The four reported per-sample QC cell counts reproduce within 1–4% from the public GEO matrices and the authors' own Seurat notebook (e.g. mut_2 1940→1869), with the small residual fully attributable to stochastic scDblFinder doublet removal — a technical/expected deviation on our side, no fabrication concern (q5 green). The deviation is negligible in magnitude and the values are cleanly derivable. However, only the deterministic QC checkpoint was attempted; the central biological conclusion (Spns1→Notch1 valve morphogenesis, 18-cluster annotation, DE, imaging) was deliberately not reproduced (annotation/seed-sensitive code + no public image data), so the core claim is confirmed only at pipeline entry — hence q7/q8 yellow rather than green.
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.