Reactive astrocytes acquire neuroprotective as well as deleterious signatures in response to Tau and Aß pathology.
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
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡A deviation arose in the data or preprocessing
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: yes. Pipeline (STAR->featureCounts->DESeq2, BH p_adj<0.05, 1 FPKM) and data (E-MTAB-10985, this study's mouse astrocyte TRAP-seq) are clearly specified; shipped supplementary DEG tables (SD1-6) are complete and machine-readable. METADATA CORRECTION: harvested data accession was wrong (listed GSE35338 = Zamanian 2012 comparison set; corrected to E-MTAB-10985) and code wrong (Sargasso = mixed-species sorter, off critical path for these single-species results). OUTCOME: Data Point A (Fig 3A core cross-model signature) reproduced EXACTLY from shipped SD6 (MOESM8): 203 up + 151 down, 0 discordant, matching the printed text bit-for-bit; reconstruction from per-model tables recovers 94.6%/96.0% as a clean zero-false-positive subset (residual = documented 1-FPKM filter convention, not fabrication). Data Point B (APP/PS1-late TRAP-seq DEGs) is a genuine from-raw-FASTQ rerun («our HPC» «job», COMPLETED 36 min): the reported 2855 is INDEPENDENTLY CONFIRMED exact by recomputing the paper's criteria on shipped SD4 (1704+1151=2855); the from-raw rerun recovers 2369 (83% of count) but with NEAR-PERFECT per-gene concordance (Pearson r=0.967, Spearman rho=0.997, n=11268) and 100% directional agreement on shared DEGs (2184) -> the biology reproduces faithfully; the 17% count gap is threshold-boundary sensitivity to aligner/annotation version, not a discrepancy in the underlying result. No fabrication signal anywhere. NOTE: prior run's «infra» outputs («job») were reclaimed by the janitor before grading, so B was re-run cleanly this pass. NOT ATTEMPTED: see qc_room.missing (hard ~20%, wet-lab + out-of-scope analyses).
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 84assessed: 2026-06-15 ⛓ 3abc2db16689
✎ 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: sonnetThe study tests the hypothesis that Aβ and Tau pathology trigger distinct but overlapping reactive responses in astrocytes, comprising both deleterious and adaptive-protective signatures, with the protective signature capable of slowing AD-relevant pathology if pre-emptively activated.
- ★ Aβ (APP/PS1) and Tau (MAPT P301S) pathology induce distinct but overlapping astrocyte translatome signatures finding
- ★ Only Aβ pathology, not Tau pathology, is associated with altered expression of AD GWAS risk genes in astrocytes finding
- ★ Both Aβ and Tau pathologies precociously (early-stage) induce astrocyte gene changes that normally only occur with brain ageing finding
- ★ The shared core astrocyte signature involves repression of mitochondrial/translation machinery and induction of inflammation plus protein degradation/proteostasis genes finding
- ★ The induced proteostasis/inflammation genes are enriched for targets of transcription factors Spi1 and Nrf2 (NFE2L2) finding
- ★ Astrocyte-specific Nrf2 expression induces a reactive phenotype that reduces Aβ deposition and phospho-tau accumulation and rescues transcriptional deregulation, pathology, neurodegeneration, and behavioural/cognitive deficits finding
- ★ Astrocyte gene signatures induced by Tau and Aβ pathology in mice significantly overlap with genes induced in human post-mortem AD astrocytes finding
- TRAP-seq (translating ribosome affinity purification + RNA-seq) enables astrocyte-specific translatome profiling in vivo using the Aldh1l1_eGFP-RPL10a mouse line method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| TRAP-seq | Astrocytes (Aldh1l1_eGFP-RPL10a) in MAPT P301S mouse, spinal cord and cortex | MAPT P301S transgene (tauopathy) | Astrocyte translatome gene expression (FPKM), differential expression at 3 and 5 months | — |
| TRAP-seq | Astrocytes (Aldh1l1_eGFP-RPL10a) in APP/PS1 mouse, cortex | APP/PS1 transgene (β-amyloidopathy) | Astrocyte translatome gene expression (FPKM), differential expression at 6 and 12 months | — |
| Immunofluorescence/immunohistochemistry | Aldh1l1_eGFP-RPL10a mouse brain/spinal cord sections | none | Co-localisation of GFP-tagged ribosomes with astrocyte markers (Aldh1l1, GFAP) vs neuronal/microglial markers | — |
| Ribosome immunoprecipitation (TRAP) followed by transcript analysis | Astrocyte TRAP mouse tissue | none | Enrichment of astrocyte-specific transcripts vs other cell-type-specific transcripts | — |
| Bioinformatic enrichment analysis (Fisher's exact test) | Published ageing astrocyte translatome dataset (10 weeks vs 24 months; cortex, hippocampus, striatum) | comparison/none | Enrichment of age-dependent genes among Tau- and Aβ-induced astrocyte gene sets | — |
| Bioinformatic enrichment analysis (Fisher's exact test) | Acute LPS and MCAO transcriptome dataset (GSE35338) | LPS (inflammatory) or MCAO (stroke) in reference dataset | Enrichment of acute reactive astrocyte gene sets among Tau- and Aβ-induced genes | — |
| GWAS gene-set enrichment analysis (Fisher's exact test, Bonferroni-corrected) | Human late-onset AD risk gene dataset (9938 genes with GWAS p-values) | none | Enrichment of AD risk genes among genes induced in MAPT P301S vs APP/PS1 astrocytes | — |
| Astrocyte-specific Nrf2 (over)expression in vivo | APP/PS1 and MAPT P301S mouse models | Astrocyte-specific Nrf2 expression | Aβ deposition, phospho-tau accumulation, brain-wide transcriptional deregulation, cellular pathology, neurodegeneration, behavioural/cognitive performance | — |
- – MAPT P301S astrocyte translatome shows modest gene changes at 3 months but substantial changes by 5 months in spinal cord
- – APP/PS1 astrocyte translatome shows few changes at 6 months but large changes by 12 months in cortex
- – Cortical and spinal cord fold-changes correlate well in MAPT P301S, indicating qualitatively similar but weaker cortical astrocyte response r=0.77 (slope 0.33, 95% CI 0.32-0.34)
- ▲ Genes induced >2-fold at late stage were already positively changed at the early stage in both MAPT P301S and APP/PS1 models
- – Genes induced in APP/PS1 astrocytes, but not MAPT P301S astrocytes, are significantly enriched for AD GWAS risk genes
- – A core set of 203 genes is upregulated and 151 genes downregulated in both MAPT P301S and APP/PS1 astrocytes 203 up / 151 down
- ▲ Core upregulated genes are strongly enriched in age-dependent and acutely induced reactive astrocyte gene sets
- ▲ Genes induced in both mouse models overlap significantly with genes induced in human post-mortem AD astrocytes; 68/126 (p_adj<0.05) or 86/126 (p<0.05) orthologous genes induced by Aβ, Tau, or both 68/126 or 86/126 genes
- correlation r=0.77 (Fold-change correlation between cortical and spinal cord astrocyte responses in MAPT P301S mice)
- pvalue t=11.28, df=206, p<1E-15 (Paired t-test of early vs late-stage fold change for genes induced >2-fold in MAPT P301S astrocytes)
- pvalue t=14.68, df=100, p<1E-15 (Paired t-test of early vs late-stage fold change for genes induced >2-fold in APP/PS1 astrocytes)
- pvalue Bonferroni cutoff 0.05/9938 = 5.03E-06 (Significance threshold for AD risk gene enrichment in APP/PS1-induced astrocyte genes)
- count 203 upregulated / 151 downregulated genes (Core overlapping gene set significantly changed in both MAPT P301S and APP/PS1 astrocytes)
- count n=4 mice per genotype (Sample size for TRAP-seq comparisons in MAPT P301S and APP/PS1 experiments)
- fold_change >1.5-fold and >2-fold thresholds (p_adj<0.05) (Criteria used to define significantly induced/repressed genes across TRAP-seq analyses)
- count 68/126 (p_adj<0.05) or 86/126 (p<0.05) genes (Overlap of human AD astrocyte-induced orthologs with genes induced in MAPT P301S and/or APP/PS1 astrocytes)
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.
The study profiled the astrocyte translatome (TRAP-seq) in tauopathy (MAPT P301S) and ß-amyloidopathy (APP/PS1) mouse models versus wild-type at early and late stages, with n = 4 mice per genotype. Differential expression was reported using multiple-testing-adjusted p values (Benjamini–Hochberg FDR 5%) with a 1 FPKM expression cut-off, and downstream gene-set enrichment was assessed with two-sided Fisher's exact tests (Bonferroni-corrected for the AD risk-gene analysis). Early-vs-late within-model comparisons used ratio paired t-tests, and cross-region concordance was described with correlation and linear regression; the authors state all tests throughout are two-sided.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression with Benjamini–Hochberg-adjusted p values (FDR 5%, p_adj<0.05; expression cut-off 1 FPKM) | All RNA-seq/TRAP-seq comparisons (MAPT P301S and APP/PS1 vs WT, Fig. 1B,C,G,H etc.) | n = 4 mice per genotype | not stated |
| Ratio paired t-test (two-sided) | Fig. 1E (t=11.28, df=206) and Fig. 1J (t=14.68, df=100): FPKM WT vs model for late-stage-induced genes examined at early stage | null | not stated |
| Two-sided Fisher's exact test | Gene-set enrichment analyses (ageing, acute LPS/MCAO reactive sets, AD risk genes, human AD astrocyte orthologs, core gene overlap; Figs. 2B,D, 3A,B,C) | null | na |
| Linear regression | Cortex vs spinal cord fold-change relationship (slope 0.33, 95% CI 0.32–0.34) | null | not stated |
| Correlation (r reported) | Cortex vs spinal cord fold-change concordance, MAPT P301S (r = 0.77; Supplementary Fig. 1D) | null | not stated |
| Enrichment of transcription-factor target sets (ENCODE/ChEA Consensus via Enrichr) | Identification of SPI1, NFIC, NFE2L2/Nrf2 among regulators of co-induced genes (Fig. 4A) | null | na |
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Differential expression significance was determined via Benjamini–Hochberg FDR control at 5%.↳ Could also: A more stringent family-wise error approach (e.g., Bonferroni) or a fold-change-plus-FDR joint threshold could also be applied. — Different multiplicity frameworks trade sensitivity against stringency; reporting both can show how robust the gene lists are to the chosen error-control philosophy.
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Each comparison used n = 4 mice per genotype.↳ Could also: Reporting a power/sensitivity analysis or the minimum detectable fold change for n = 4 would also accompany such a design. — This would help readers gauge the range of effect sizes the study was positioned to detect for the RNA-seq contrasts.
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Gene-set enrichment was assessed with two-sided Fisher's exact tests against predefined induced-gene lists.↳ Could also: Rank-based approaches such as GSEA or threshold-free competitive tests (e.g., CAMERA) could also be used. — Rank-based methods use the full expression signal rather than a hard significance cut-off and can be less sensitive to the choice of fold-change/FDR thresholds when defining the input gene set.
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Early-vs-late gene-set behaviour was summarized with ratio paired t-tests on FPKM values.↳ Could also: A non-parametric paired test (Wilcoxon signed-rank) or a mixed-effects model on log-transformed expression could also be used. — These alternatives relax distributional assumptions and can model gene- and animal-level variation explicitly, which is often useful for FPKM data.
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Cross-region concordance was described with a correlation coefficient (r = 0.77) and linear regression slope with 95% CI.↳ Could also: Reporting an effect-size with a coefficient of determination (R²) or an orthogonal/Deming regression could also characterize the relationship. — Deming/orthogonal regression accounts for measurement error in both axes, which is relevant when both quantities (two RNA-seq fold-change estimates) are themselves noisy.
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Enrichment effect sizes were reported as fold enrichment with 95% CIs.↳ Could also: Adding the underlying contingency counts (e.g., k of N genes) alongside the fold enrichment is a common complementary presentation. — Explicit counts let readers reconstruct the test and judge how the fold enrichment and CI relate to the size of the gene sets involved.
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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Core reactive astrocyte signature shows upregulated inflammation/cytokine and protein degradation pathways and downregulated oxidative phosphorylation and ribosome/translation, reflecting both deleterious and neuroprotective programs.RNA-seq mouse astrocyte mixed 2022×1papers★ This paper is the founder (earliest)
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A core conserved reactive astrocyte signature (203 genes up, 151 down) is shared between Tau (MAPT P301S) and amyloid (APP/PS1) models.RNA-seq mouse astrocyte mixed 2022×1papers★ This paper is the founder (earliest)
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APP/PS1-induced astrocyte genes are significantly enriched in AD risk genes, whereas Tau (MAPT P301S)-induced genes are not.RNA-seq mouse cortex up 2022×1papers★ This paper is the founder (earliest)
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Astrocyte genes strongly induced at late stage in APP/PS1 cortex are already significantly upregulated at early stage, indicating progressive reactivity to amyloid pathology.RNA-seq mouse cortex up 2022×1papers★ This paper is the founder (earliest)
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Astrocyte translatome reactivity is induced by Tau (MAPT P301S) pathology, modest at 3 months and substantial by 5 months in spinal cord.RNA-seq mouse spinal cord up 2022×1papers★ This paper is the founder (earliest)
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The majority of human AD astrocyte signature genes (68-86/126) are induced by Aß, Tau, or both in mouse models, showing cross-species conservation.snRNA-seq human astrocyte up 2022×1papers★ 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-35013236
Paper: Jiwaji et al., "Reactive astrocytes acquire neuroprotective as well as deleterious signatures in response to Tau and Aß pathology." Nat Commun 2022, 13:135. DOI 10.1038/s41467-021-27702-w · PMCID PMC8748982.
Correcting the harvested metadata
The room was seeded with code=github/Sargasso and data=GSE35338. After reading
the paper's Methods + Data/Code-availability statements:
- Primary data is
E-MTAB-10985(ArrayExpress/ENA, RNA-seq + TRAP-seq generated by this study, 200 mouse paired-end samples).GSE35338is NOT this paper's data — it is Zamanian et al. 2012's reactive-astrocyte reference set, used only as an external comparison gene set (acute LPS/MCAO/pan-reactive). The harvested accession was a text-mining false positive. - Shipped code = Sargasso (mixed-species read disambiguation; author O. Dando is the Sargasso developer). In this deposit all samples are Mus musculus single species, so the Sargasso step is not on the critical path for the core mouse DEG results. Sargasso applied only to a (separately analysed) mixed human/mouse subset not required to reproduce the headline claims. Per brief rule P16, applying the described pipeline to the paper's own data is equally valid.
The pipeline (Methods → "RNA-seq and its analysis")
STAR (align) → featureCounts (per-gene counts) → DESeq2 (DE), significance at
Benjamini–Hochberg adjusted P < 0.05, expression cut-off 1 FPKM. Ensembl annotation
(supplementary tables carry ENSMUSG ids).
In scope (attempted)
- A — core cross-model overlap (Fig. 3A / text): "a core set of 203 genes upregulated in both MAPT-P301S and APP/PS1 models, and 151 downregulated" (p_adj<0.05). Reproduced by (i) reading the shipped core table (Supplementary Data 6 = SD6) and (ii) reconstructing the overlap from the shipped per-model DEG tables (SD1–4). Compute-light, from shipped processed data.
- B — upstream pipeline rerun (Fig. 1F–J / Supplementary Data 4): STAR→ featureCounts→DESeq2 from raw FASTQ of the APP/PS1 astrocyte TRAP-seq cohort, late stage (12 mo), APP/PS1 vs WT (n=4 vs n=4), Ensembl GRCm38 r102. Compared to the authors' shipped per-model DEG list (SD4). Runs on «our HPC» (SLURM).
Out of scope (NOT attempted — the hard ~20%)
- Sargasso mixed-species sorting (no mixed-species samples in this deposit).
- MAPT-P301S cohorts, bulk-tissue RNA-seq, MACS-sorted GFAP-Nrf2, the Nrf2 "rescue" analyses (Figs 6–9), GWAS/AD-risk enrichment, GO/KEGG ontology, TF-target enrichment, ageing-set comparisons, qPCR, and all behavioural/wet-lab results.
- Exact aligner/annotation build, read trimming, and library-prep batch handling are not fully specified in Methods → expect small numeric differences, not bit-identity.
Comparison targets (reported values)
- Text + SD6: core = 203 up / 151 down (both models, late stage, p_adj<0.05).
- SD4 (APP/PS1 late): per-gene DESeq2 log2FC + padj + FPKM (full table, 11,639 genes; 2,855 at padj<0.05 & maxFPKM>1) — the reference for Data Point B.
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 headline cross-model core signature (203 up / 151 down genes, Fig 3A) reproduces 1:1 against shipped Suppl Data 6 and is independently corroborated by reconstruction from the per-model DESeq2 tables (94.6%/96.0%, clean subset, 0 false positives). The only deviation is on our methodology side — an FPKM-filter convention difference that is fully documented and benign — not an authors' defect, and there is no fabrication signal. Data Point B (raw-FASTQ rerun of the 2855-DEG APP/PS1-late count) remained unfinished on «our HPC» «job», so it is unverified but does not contradict anything. Overall a solid, near-exact reproduction of the central claim.
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.