GRAMD1B is a regulator of lipid homeostasis, autophagic flux and phosphorylated tau.
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
DESCRIBED WELL ENOUGH and reproduced ~1:1 for the in-scope scRNA-seq pipeline; paper is otherwise wet-lab (see scope.md). Code (eturkes/acosta-organoid-scRNAseq @dc364fb, GPL-3.0) and data (GEO GSE278619, open) both resolve. Compute ran on «our HPC» SLURM compute nodes (strict SLURM-only; env-build + analysis in-job): «job» built a functionally-equivalent conda env (R4.3.3/Seurat5.3.0/edgeR/limma/scuttle/DropletUtils) and re-derived per-sample QC; «job» re-ran the documented pseudobulk-limma DEG. RESULTS: C5 EXACT and GENUINE -- independent per-sample 3-MAD QC (01_prep.Rmd) on the 12 raw GEO matrices yields 36782 cells passing QC, matching the deposited annotated object to the cell (44697 barcodes -> 36782). C3 HEADLINE reproduced -- GRAMD1B significantly up in mutant (HET&HOM>WT) D120 excitatory neurons (global-F.adj=4.24e-4) and EX-specific (not significant in Inhibitory/RadialGlia/Mural; borderline IPC 8.9e-3 via HOM-WT only, consistent with the paper noting GRAMD1B is expressed in EX & IPC); not significant at D72 or all-timepoints (within-tol). C3b ALL 7 named lipid DEGs (MIAT,COMT,NRP1,VEGFB,GRAMD1B,DSEL,FUT9) EXACT (significant + up in mutant; F.adj 5.2e-6..5.0e-4). C4 EX robust at D120 (56.7%) vs D72 (28.2%) EXACT. C1 five broad types and C2 subclusters present as reported (EXACT on value) but READ from the deposited annotated object -- the RCAv2+ClusterMap annotation (steps 02-03) was NOT independently re-run (env-fragile, needs external Bhaduri 2020 ref), so C1/C2 are descriptive reads, not from-scratch re-derivations (flagged honestly). Env functionally-equivalent (not byte-identical to authors' apptainer rocker4.2/Seurat-develop; deposited object needed UpdateSeuratObject), so exact p-values may differ slightly while directions, significance and named-gene sets all match. NOT ATTEMPTED: from-scratch re-annotation, GSVA(05)/WGCNA(06), and all wet-lab modalities (lipidomics MS, RNAscope, IF/WB, overexpression/KD, autophagy assays, PS19 mouse, post-mortem tissue, qRT-PCR). No possible-fabrication flags: deposited object, raw matrices and DEG outputs are mutually consistent with the paper's claims. INFRASTRUCTURE NOTE: HOME and per-user «infra» quotas (shared across the concurrent multi-room batch) were intermittently exhausted; worked around by redirecting HOME/caches to «infra» and making the job quota-resilient (node-local I/O + retry-copy of small results). All compute was run strictly as SLURM jobs on compute nodes (never on the login node).
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
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v1 current initial assessment Score 91assessed: 2026-06-16 ⛓ 9bb393fcf5e7
✎ I am an author of this paper
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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 2026-06-18no 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: sonnetThe relationship between lipid dyshomeostasis and tau pathology in FTLD and AD is unclear, and this study tests whether GRAMD1B, a nonvesicular cholesterol transporter, links lipid dyshomeostasis, autophagy dysfunction, and tau hyperphosphorylation in neurons.
- ★ GRAMD1B is increased in excitatory neurons of human neural organoids (HNOs) carrying the MAPT R406W mutation finding
- ★ GRAMD1B expression is increased in human FTLD and AD brain tissue and in PS19 tau mice finding
- ★ Overexpression of GRAMD1B increases free cholesterol and lipid droplets and impairs autophagic flux finding
- ★ Modulating GRAMD1B alters autophagy-related components (PI3K, phospho-AKT, p62) and increases phosphorylated tau and CDK5R1 in iPSC-derived neurons mechanism
- ★ Blocking or knocking down GRAMD1B decreases free cholesterol, lipid droplets, phosphorylated tau, and CDK5R1 expression finding
- ★ MAPT R406W HNOs exhibit increased tau phosphorylation (PHF1) at Day 120 but not at Day 60 finding
- ★ MAPT R406W HNOs show decreased neuroelectrical and network activity, including reduced firing rate, spikes, and increased interburst intervals finding
- GRAMD1B (Aster protein family) mediates nonvesicular lipid transport at endoplasmic reticulum-plasma membrane contact sites mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-sequencing (scRNA-seq) | human neural organoids (HNOs), MAPT R406W hiPSC-derived | MAPT R406W mutation (HET/HOM vs isogenic WT) | cell-type transcriptomic profiles, differentially expressed genes (e.g., GRAMD1B) in excitatory neurons | — |
| immunofluorescence (IF) staining | human neural organoids (D60, D120) | MAPT R406W mutation | cell-type markers (SOX2, PAX6, Ki67, TBR1, CTIP2, SATB2, NEUN, GAD1), total tau, GFAP, OLIG2, PHF1/total tau | — |
| Western blot | human neural organoids (D60, D120) | MAPT R406W mutation | PHF1, total tau (TAU/TauC), GAPDH protein levels | — |
| qRT-PCR | human neural organoids (D120) | MAPT R406W mutation | MAPT 3R vs 4R tau isoform mRNA expression | human-specific MAPT 3R/4R primers |
| ELISA | HNO culture media (D60, D120) | MAPT R406W mutation | secreted ptau181 levels | — |
| immunoprecipitation (IP) / Western blot | HNO culture medium (D120) | MAPT R406W mutation | secreted total tau (CP27, TauC) and phosphorylated tau (PHF1, 12E8) | — |
| microelectrode array (MEA) recording | human neural organoids (D75) | MAPT R406W mutation | firing rate, spike number, burst activity, interburst interval, local field potentials | MEA chips |
- ▲ Mutant HET HNOs show increased PHF1/total tau by IF staining at D120 P=0.0041 (WT vs HET)
- ▲ Mutant HOM HNOs show increased PHF1/total tau by IF staining at D120 P=0.0280 (WT vs HOM)
- – No significant increase in phosphorylated tau (PHF1) at D60 in mutant HNOs
- – Total tau protein levels unchanged at D120 by Western blot across genotypes
- – Homozygous HNOs show no significant difference in PHF1/Tau vs WT by Western blot, likely due to increased inhibitory neuron populations
- ▼ Mean firing rate decreased in mutant HET HNOs vs WT HNOs (MEA, D75) P=0.033
- ▼ Number of spikes decreased in mutant HET HNOs vs WT HNOs (MEA, D75) P=0.016
- ▲ Interburst interval increased and synchronized burst firing (ASDR) reduced in mutant HET HNOs vs WT
- pvalue P=0.037 (WT vs HET) (HNO diameter growth, two-way ANOVA with Tukey post hoc)
- pvalue P=0.009 (WT vs HOM) (HNO diameter growth, two-way ANOVA with Tukey post hoc)
- pvalue P<0.001 (Day 80, WT vs HOM) (HNO diameter growth, two-way ANOVA with Tukey post hoc)
- pvalue P=0.0041 (WT vs HET) (PHF1/total tau IF quantitation at D120 (WT n=10, HET n=9))
- pvalue P=0.0280 (WT vs HOM) (PHF1/total tau IF quantitation at D120 (WT n=10, HOM n=3))
- pvalue P=0.0005 (WT vs HOM) (TauC/GAPDH Western blot quantitation at D60, one-way ANOVA)
- pvalue P=0.033 (WT vs HET) (Mean firing rate by MEA at D75 (WT n=8, HET n=11), Mann-Whitney two-tailed)
- pvalue P=0.016 (WT vs HET) (Number of spikes by MEA at D75 (WT n=8, HET n=11), Mann-Whitney two-tailed)
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 employs a multi-assay design across isogenic MAPT R406W HNO genotypes (WT, HET, HOM), comparing protein expression by immunofluorescence and western blot, organoid growth, electrophysiology, and single-cell transcriptomics. Group differences are tested primarily with one-way ANOVA, a two-way ANOVA with Tukey post hoc for the longitudinal growth data, and non-parametric Mann-Whitney U tests for IF quantitation and MEA metrics; scRNA-seq data underwent differential gene expression analysis by an unspecified method. Results are uniformly presented as mean ± SEM with exact or near-exact p-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-way ANOVA with Tukey post hoc | HNO longest-diameter growth over time across genotypes (Fig. 1b) | n = 10 HNOs per group per time point (n = 9 for Day 70 HOM) | not stated |
| One-way ANOVA (post-hoc procedure not named) | PHF1/TAU ratio and TauC/GAPDH by western blot and IF at D60 and D120 (Figs. 2b, 2g, 2h, 2k, 2l) | n = 3–6 biological replicates per group at D60; n = 4 per group at D120 | not stated |
| Mann-Whitney U, two-tailed | PHF1/Tau IF quantitation at D120 (Fig. 2d); MEA mean firing rate (Fig. 3d), number of spikes (Fig. 3e), interburst interval (Fig. 3f) | D120 IF: WT n=10, HET n=9, HOM n=3; MEA Figs. 3d–e: n=8 WT, n=11 HET HNOs; Fig. 3f: n=4 independent experiments per genotype | not stated |
| Differential gene expression analysis (scRNA-seq; specific algorithm not stated in visible text) | Excitatory neuron cluster at D72 and D120 (Fig. 4a; Supplementary Data 2 & 3) | 2 independent experiments; 6 samples per experiment (5 HNOs per sample) per genotype | not stated |
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Dispersion around means is reported as SEM uniformly across all figures with n ranging from 3 to 11↳ Could also: Report SD or 95% confidence intervals instead of or alongside SEM — With small replicate counts (n = 3–11), SD directly reflects sample-level biological variability rather than the precision of the mean estimate; 95% CIs additionally convey both effect magnitude and inferential uncertainty in a single interval, which can aid interpretation of small-n comparisons
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Multiple one-way ANOVAs extract several pairwise p-values without naming a post-hoc procedure↳ Could also: Name and apply a standard post-hoc correction such as Tukey HSD (all pairwise) or Dunnett's test (all mutants vs. WT control only) — When multiple pairwise comparisons follow a significant omnibus F-test, a named post-hoc method controls the family-wise error rate within that comparison set; Dunnett's is particularly parsimonious when mutant lines are each compared to a single reference
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Mann-Whitney U was chosen for some three-way genotype comparisons (e.g., Fig. 2d) while one-way ANOVA was used for the same genotype structure in parallel western blot panels↳ Could also: Apply a single consistent test family across equivalent comparisons—e.g., Kruskal-Wallis with Dunn's post hoc for all three-group non-parametric comparisons—with a pre-specified rationale for when non-parametric tests are selected — Consistent test selection across parallel assays of the same biological comparison makes effect-size interpretation and cross-modality comparison more straightforward; stating criteria for non-parametric test choice (e.g., non-normality confirmed by Shapiro-Wilk) increases transparency
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The scRNA-seq differential gene expression method is not named in the visible text, and no multiple-testing correction procedure is specified for the DEG analysis↳ Could also: Explicitly name the DEG algorithm (e.g., DESeq2 pseudo-bulk Wald test, MAST hurdle model, or Wilcoxon rank-sum) and the FDR correction applied (e.g., Benjamini-Hochberg) — Single-cell DEG methods differ substantially in how they handle zero-inflation, pseudo-replication across cells within a sample, and multiple testing across thousands of genes; naming the method and correction threshold allows readers to evaluate assumptions and reproduce the analysis
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Cell-type composition differences across genotypes and time points are described from abundance plots without a formal statistical test↳ Could also: Apply a compositional data analysis method (e.g., Dirichlet regression, scCODA, or a permutation test on proportions) to formally compare cell-type fractions across genotypes — Cell-type proportions are compositional (they sum to 1 within each sample), so standard tests on individual fractions can be anti-conservative; dedicated compositional methods account for this constraint and for the hierarchical structure of cells nested within organoids
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MEA metrics were compared with Mann-Whitney U tests without accounting for the nested structure of HNOs recorded within independent experiments↳ Could also: Use a linear mixed-effects model with independent experiment as a random effect, or a nested ANOVA — HNOs recorded within the same experiment may be more correlated with each other than with HNOs from a different batch; a mixed-effects model that includes experiment as a random effect accounts for this nesting and yields inference that generalizes across experimental runs rather than within them
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.
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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-40204713 (GRAMD1B / Acosta-Ingram et al., Nat Commun 2025)
- Paper: "GRAMD1B is a regulator of lipid homeostasis, autophagic flux and phosphorylated tau." DOI 10.1038/s41467-025-58585-w, PMCID PMC11982250.
- Code: https://github.com/eturkes/acosta-organoid-scRNAseq (commit
dc364fb8fb1d1a1bcf80dfe97540ceb53c5f67b4, GPL-3.0, public; authors' own code). - Data: GEO GSE278619 (public, open). Ships 12 per-sample 10x matrices (GSM8551336..8551347 = D72/D120 × WT/HET/HOM × rep1/rep2) and
GSE278619_annotated_processed_seurat.rds.gz(final annotated Seurat object, 36782 cells).
The paper is multi-modal. Only the scRNA-seq pipeline is in scope.
IN SCOPE (pipeline-derived, scRNA-seq, reproducible)
Per Methods + repo R/:
- Per-sample prep/QC (
R/<sample>/01_prep.Rmd):Read10X→ SCE →addPerCellQC(mito ^MT-) →quickPerCellQC(3-MAD adaptive thresholds) → cells passing QC per sample. [REPRODUCED from raw matrices, «job»] - Merge + SCTransform v2 (
comb/01). - Broad annotation (
comb/02): RCAv2 vs Bhaduri 2020 cortical ref → 5 broad cell types. [NOT re-run — env-fragile; used deposited object labels] - Subclustering + ClusterMap (
comb/03). [NOT re-run — used deposited object labels] - DEGs (
comb/04_DEGs.Rmd): pseudobulk per sample (aggregateAcrossCells) →filterByExpr(min.total.count=10)→ edgeRcalcNormFactors→cpm(log,prior.count=3)→ limmalmFit(~0+genotype)→ contrasts HET-HOM/HET-WT/HOM-WT →eBayes(trend=TRUE)→decideTests(,"global"), BH. Headline: GRAMD1B up in mutant EX, only in EX. [REPRODUCED, «job»] - Abundance (
comb/10_abundance.Rmd): per-timepoint cell-type proportions. [REPRODUCED, «job»] - GSVA (
05), WGCNA (06), markers (12) — secondary, NOT attempted.
Concrete claims (see original/claims.tsv): C1 five broad types, C2 subclusters, C3 GRAMD1B up in mutant EX & EX-specific, C3b 7 named lipid genes, C4 robust EX at D120, C5 sample/cell counts, C6 top-24 DEG heatmap.
OUT OF SCOPE (wet-lab / not a computational pipeline → not attempted)
Lipidomics MS, RNAscope HiPlex smFISH (ImageJ only), IF / Western blots / filipin / lipid-droplet imaging, GRAMD1B overexpression/knockdown, autophagy-flux assays, PS19 mouse, human post-mortem FTLD/AD tissue, qRT-PCR (4R/3R tau).
Reproduction strategy (valid per brief P16)
Anchor the DEG re-run on the deposited final annotated object (faithful re-run of documented step 04 on its EX cells); independently re-derive per-sample QC cell counts from the raw GEO matrices (step 01). Full RCAv2+ClusterMap annotation (02-03) is the env-fragile part and was not re-run — cell-type labels (C1/C2) are read from the deposited object and clearly flagged as such.
Env
Authors ship apptainer (rocker/rstudio:4.2.0 + Seurat develop + RCAv2 + ClusterMap). Reproduced on a functionally-equivalent conda env (R 4.3.3, Seurat 5.3.0, edgeR 4.0.16, limma 3.58.1, scuttle, SingleCellExperiment, DropletUtils) built inside a «our HPC» SLURM compute job. Deposited object required UpdateSeuratObject() to load.
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 in-scope scRNA-seq computational claims reproduce ~1:1 from public GEO data (GSE278619) and the authors' GPL-3.0 code: GRAMD1B is significantly up in MAPT R406W D120 excitatory neurons (F.adj=4.2e-4, up in both HET and HOM), all 7 named lipid DEGs replicate, and the cell-type/abundance descriptives match exactly. The only deviations are negligible env-driven p-value jitter (functionally-equivalent conda stack vs the authors' apptainer image), which sits on our/technical side, not the authors'. Caveats are limited to scope: the top-24 heatmap membership was not pixel-verified (C6 partial) and descriptive claims were read off the deposited annotated object rather than recomputed from a full from-scratch re-annotation. No fabrication or derivability concern.
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.