Single-nucleus mRNA-sequencing reveals dynamics of lipogenic and thermogenic adipocyte populations in murine brown adipose tissue in response to cold exposure.
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
1:1 REPRODUCED (authors' own R/Seurat v5 pipeline applied to the authors' deposited processed object). Anchor = ChREBP_SeuratObject_final.rds (doi:10.25592/uhhfdm.18248, MD5 verified). 10/10 attempted claims across 8 result groups graded: 7 exact, 2 within-tol, 1 partial. Cluster count (21), adipocyte subtypes (7), sample structure (6), Fig3A WT adipocyte frequencies (basal 58.88%~59%, lipogenic 14.44%~14.4%, white-like 11.56%->7.01% vs 11.6%->7%), Ttc25 as #1 lipogenic marker, and the Fig2A Lundgren-vs-Behrens correlation (r=0.93 overall, 0.99 signature) all reproduce. The shipped lipogenic-vs-basal DE table (848 genes) is regenerated to full numerical precision from the deposited object (Pearson=Spearman=1.0, max log2FC diff=0.000) -- a strong fabrication-negative result. ONE honest discrepancy: the deposited object holds 34,665 nuclei, not the 36,611 stated by both the paper (Fig1C) and the uhhfdm record; most plausibly 36,611 is the pre-RBC-removal total and the object is post-removal, but the record's N is inaccurate for the file. KEY DATA NOTE: the brief's accession GSE218710 is NOT this paper's data -- it is the external Lundgren et al. 2023 spatial-transcriptomics dataset used only as the Fig2A comparator; the paper's data are uhhfdm.18248 (processed) + E-MTAB-15819 (raw). NOT attempted: wet-lab qPCR/WB panels and the PROGENy/LIANA/scVelo panels whose outputs are pre-shipped.
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 93assessed: 2026-06-19 ⛓ 4f93e3d2a25e
✎ 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
- 2026-06-19
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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 investigates how the cellular composition of murine brown adipose tissue (BAT), particularly its adipocyte subtypes, responds to acute and chronic cold exposure, and whether the lipogenic transcription factor ChREBP is required for the identity/maintenance of a distinct lipogenic brown adipocyte subpopulation.
- ★ snRNA-seq of murine BAT identifies seven distinct brown adipocyte subtypes with distinct metabolic profiles, part of a 21-cell-type atlas. finding
- ★ Lipogenic adipocytes are highly sensitive to acute cold exposure, showing marked depletion that is compensated by other brown adipocyte subtypes maintaining de novo lipogenesis. finding
- ★ Chronic cold exposure expands basal brown adipocytes and adipocytes putatively derived from stromal and endothelial precursors, while white-like adipocytes decrease. finding
- ★ ChREBP-deficient mice almost completely lack lipogenic adipocytes under all housing/cold conditions, identifying ChREBP as a key determinant of this adipocyte subtype. finding
- ★ Ttc25 is a specific marker gene of lipogenic brown adipocytes and acts as a downstream target of ChREBP. finding
- ★ Pathway and cell-cell interaction analyses implicate a Wnt-ChREBP axis, with Wnt ligands from stromal and muscle cells providing instructive cues for lipogenic adipocyte maintenance. mechanism
- ★ The study provides a comprehensive snRNA-seq atlas of BAT cellular heterogeneity across genotypes and cold-exposure conditions. resource
- Loss of lipogenic adipocytes/DNL in ChREBP knockout mice does not significantly affect energy homeostasis under cold stress, indicating compensation by the BAT organ. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-nucleus mRNA-sequencing (snRNA-seq) | interscapular BAT, mouse (Cre- and ChREBP flox/flox Ucp1-Cre+ knockout) | cold exposure (RT, 1 day acute cold, 10 days chronic cold at 6°C) and ChREBP knockout | cell cluster identity and frequency, gene expression profiles | 10X Genomics Chromium |
| qPCR | iBAT, mouse | ChREBP knockout (Cre+) vs control (Cre-) across RT/acute/chronic cold | mRNA expression of ChREBPα and ChREBPβ (Mlxipl isoforms) | — |
| Western blot | iBAT, mouse | ChREBP knockout (Cre+) vs control (Cre-) across RT/acute/chronic cold | ChREBPα and γ-tubulin protein levels | — |
| histology | BAT, mouse | ChREBP knockout vs control | BAT tissue morphology | — |
| re-analysis of spatial transcriptomics data | murine BAT (published dataset, Lundgren et al.) | none | co-expression of Mlxipl and Ttc25 in tissue spots | — |
| snRNA-seq re-analysis | human BAT (published dataset, Sun et al.) | none | correlation of TTC25 with MLXIPL and DNL enzyme expression across adipocyte clusters | — |
| gene expression analysis (qPCR/RNA-seq) | BAT, mouse (WT, heterozygous, homozygous ChREBP total-body knockout; also LSL-ROSA-ChREBPβ overexpression mice) | ChREBP knockout dose or ChREBPβ overexpression | Ttc25 gene expression | — |
| snRNA-seq re-analysis with GO term analysis | murine BAT (published aged-mouse dataset) | aging (young vs aged, up to 16 months) | expression of thermogenesis markers, Ttc25, DNL genes, and lipogenic adipocyte percentage | — |
- – 36,611 nuclei from mouse iBAT were retrieved and 21 cellular clusters identified, including 7 adipocyte subtypes. 21 clusters; 36,611 nuclei
- ▲ Gene expression in lipogenic adipocytes strongly correlated with lipogenic adipocytes identified by Lundgren et al. Pearson's r = 0.93; p < 2.2e-16
- ▲ Basal brown adipocytes increased after chronic cold, constituting the majority of adipocytes. 59% of all adipocytes after chronic cold
- ▼ White-like adipocyte proportion dropped during cold adaptation. from 11.6% (RT) to 7% (chronic cold)
- ▲ Lipogenic adipocyte number increased moderately after chronic cold. 14.4% of adipocytes at chronic cold
- ▲ Stroma- and endothelium-derived adipocytes increased after chronic cold. from 7.2% (RT) to 9.7% (chronic cold)
- ▼ Ttc25 expression was strongly diminished in BAT of ChREBP total-body knockout mice, with intermediate expression in heterozygotes. more than 90% reduction
- ▲ Ttc25 expression was increased in mice overexpressing ChREBPβ in BAT. 50% increase
- correlation Pearson's r = 0.93, p < 2.2e-16 (correlation of lipogenic adipocyte gene expression with Lundgren et al. dataset)
- pvalue p adj. = 5.8e-40 (KEGG OXPHOS pathway enrichment in OXPHOS-high adipocytes)
- count 36,611 nuclei (total nuclei retrieved from iBAT across genotypes/conditions (n = 3-4 mice per sample, pooled))
- count 21 cellular clusters (total distinct cell clusters identified by snRNA-seq)
- fold_change >90% reduction (Ttc25 expression in BAT of ChREBP total body knockout mice)
- fold_change 50% increase (Ttc25 expression in LSL-ROSA-ChREBPβ overexpression mice vs. control littermates)
- mean 59% (basal brown adipocytes as proportion of all adipocytes after chronic cold)
- mean 11.6% to 7% (white-like adipocyte proportion from RT to chronic cold)
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 paper combines conventional parametric group-comparison tests (Student's t-test, one-way ANOVA with Tukey's post-hoc test) for validating individual gene/protein expression measurements (qPCR, Western blot quantification) with computational single-nucleus RNA-sequencing (snRNA-seq) analyses (UMAP clustering, marker-gene identification, pathway enrichment, and correlation analyses) for the main transcriptomic dataset. Results from validation experiments are reported as mean ± SEM with significance denoted by asterisks or thresholds, while some -omics analyses (pathway enrichment, gene correlation) report adjusted p-values. A detailed statistical methods section (e.g., normality checks, exact snRNA-seq analysis software/pipeline) is not included in the provided excerpt.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| one-way ANOVA with Tukey's post-hoc test | Figure 1A, ChREBPα/β mRNA expression across genotypes/conditions | n = 4 | not stated |
| one-way ANOVA | Figure 2E, Ttc25 expression in WT/heterozygous/homozygous ChREBP knockout BAT | n = 2–7 | not stated |
| Student's t-test | Figure 2F, Ttc25 expression in ChREBPβ-overexpressing vs. control mice | n = 6 | not stated |
| Pearson correlation | Figure 2A, cross-study comparison of lipogenic adipocyte gene expression vs. Lundgren et al. | — | not stated |
| Pathway/gene-set enrichment test (KEGG) | Fig. S1D, OXPHOS pathway enrichment in OXPHOS-high adipocyte marker genes | — | not stated |
| Correlation-based gene ranking with GO term enrichment | Figure 2G–H, genes correlating with Ttc25 in lipogenic adipocytes | — | not stated |
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Group differences across genotype and cold-exposure conditions were analyzed with separate one-way ANOVAs (with Tukey's post-hoc test) within individual figures.↳ Could also: A two-way ANOVA (genotype × cold-exposure duration) with post-hoc correction — This would formally test for an interaction between genotype and cold exposure within a single model, which can increase statistical power and directly quantify whether the effect of ChREBP knockout differs by housing condition.
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Bar graphs (e.g., Figure 1A, 2E, 2F) report dispersion as mean ± SEM.↳ Could also: Reporting SD or a 95% confidence interval alongside or instead of SEM — SEM decreases with sample size and can visually understate variability at small n; SD conveys the spread of the observed data directly, and a CI additionally communicates the precision of the estimated mean, which some readers find more informative for small-n biological experiments.
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snRNA-seq libraries were generated by pooling nuclei from 3–4 mice per condition/genotype, yielding one sequencing library per group rather than multiple independent biological replicates at the single-cell level.↳ Could also: A pseudobulk differential expression/abundance approach (e.g., DESeq2 or edgeR on pseudobulk counts) or mixed-effects models with several independent snRNA-seq libraries per group — Using multiple independent libraries per condition allows inter-animal biological variability to be estimated directly and enables formal statistical testing (rather than descriptive comparison) of expression and cluster-abundance differences between conditions.
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Changes in adipocyte subtype frequency across cold-exposure conditions (Figure 3A) are presented as percentages without a stated formal statistical test for the shift in proportions.↳ Could also: A compositional data analysis method (e.g., scCODA, Milo, or a Dirichlet-multinomial/beta-binomial test) or a chi-square/Fisher's exact test on cell counts — Cell-type proportions from single-cell data are compositional (they sum to 100% and are not independent across clusters); dedicated compositional-analysis methods can provide a formal significance estimate and account for this dependency, which a simple percentage comparison does not.
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Cross-study similarity of lipogenic adipocyte gene expression was assessed using Pearson's correlation (r = 0.93).↳ Could also: Spearman's rank correlation — Spearman's correlation does not assume a linear relationship or normally distributed data, which can be a useful complement when comparing gene-expression values that may be skewed or contain outliers.
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Pathway (KEGG) and GO term enrichment results are reported with adjusted p-values without specifying the correction method used in this excerpt.↳ Could also: Explicitly naming and reporting the multiple-testing correction method (e.g., Benjamini-Hochberg FDR) — Explicitly stating the correction procedure allows readers to know precisely how the false discovery rate was controlled and to compare enrichment results directly with other studies using the same or a different correction approach.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40945691
Paper: Behrens et al. 2025, Mol Metab 101:102252. "Single-nucleus mRNA-sequencing reveals dynamics of lipogenic and thermogenic adipocyte populations in murine brown adipose tissue in response to cold exposure."
Code: https://github.com/AdlungLab/ChREBP @ commit 53fa669017053a94e795cc278395d4f74a97aa61
(authors' own R/Seurat code — P16 own-repo).
Data accessions (IMPORTANT correction to the brief)
The brief lists geo:GSE218710 as the data accession. That is NOT this paper's
own data. GSE218710 = "A subpopulation of lipogenic brown adipocytes drives
thermogenic memory [Spatial Transcriptomics]", Lundgren & Thaiss, UPenn,
PMID 37783943 (2023). It is the external dataset this paper integrates with
(repo file data/mergedLundgrenBehrensLipogenic.rds, Fig 2A comparison).
This paper's actual data:
- Raw snRNA-seq: ArrayExpress E-MTAB-15819 (6 samples, Mus musculus, 10X).
- Processed Seurat object: doi:10.25592/uhhfdm.18248 →
ChREBP_SeuratObject_final.rds(471.5 MB, MD5 cb9de37b76ff37961165e403bdb2184c, 36,611 nuclei, 6 samples, 21 clusters). This is the reproduction anchor. - GSE218710 (Lundgren spatial, integration partner) — profiled but secondary.
In scope (pipeline-derived, reproducible from shipped object + repo)
| ID | Result | Fig | Pipeline | Approach |
|---|---|---|---|---|
| C1 | 36,611 nuclei total after QC | 1C | Seurat QC/merge/Harmony | ncol(obj) |
| C2 | 21 cellular clusters | 1C | Harmony+Louvain clustering | count celltype levels (excl RBC) |
| C3 | 7 adipocyte clusters | 1D-E | annotation | count adipocyte celltypes |
| C4 | 6 samples (2 genotype × 3 treatment) | 1 | metadata | table(sample) |
| C5 | Fig 3A WT adipocyte frequencies: basal 59% (CC), lipogenic 14.4% (CC), white-like 11.6%→7% | 3A | freq table | recompute per treatment |
| C6 | Ttc25 = most enriched gene in lipogenic adipocytes | 2B | FindMarkers lipo vs basal (WT RT) | rank shipped + recomputed DE |
| C7 | Lipo_vs_basal_WTRT.csv DE table derivable from object | 2B | FindMarkers | recompute, compare values (fabrication check) |
| C8 | Fig 2A Pearson r (Lundgren vs Behrens lipogenic signature) | 2A | cor.test on merged.plot | recompute from shipped rds |
| C9 | Ttc25 expression diminished >90% in ChREBP-KO | 2E | mean expr WT vs KO | recompute |
Out of scope / lower priority (require extra non-conda tools or wet-lab)
- Fig 1A qPCR, Fig 1B Western blot — wet-lab, OUT.
- Fig 2H GO enrichment (clusterProfiler/enrichGO) — attempt if env allows.
- Fig 3G/H RNA velocity (scVelo, Python) — uses shipped CSV exports; magnitude violin reproducible from CSV but velocity itself precomputed externally.
- Fig 4F/G PROGENy pathway, Fig 4I/J LIANA — progeny/liana (liana is GitHub-only); LIANA output shipped (LIANAout.rds), attempt visualization if env allows.
Reproduction strategy
Download processed object (done, MD5 verified) → run R/Seurat v5 on «our HPC» SLURM → compute C1-C9 → compare to paper + shipped CSVs. The shipped CSVs (Lipo_vs_basal, velocity exports) let us check whether reported values are derivable from the shipped data (fabrication guard).
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.
This is a near-textbook 1:1 reproduction run on the authors' own deposited Seurat object: cluster/adipocyte/sample counts, all Fig3A frequencies, the Ttc25 marker rank, and the Fig2A Lundgren-vs-Behrens correlations all reproduce, and the one tested shipped derived table (848-gene lipogenic-vs-basal DE) is byte-for-byte regenerable (fabrication-negative). The single deviation is the reported nuclei count (36,611 vs 34,665, ~5.3%), which sits at a preprocessing boundary (pre- vs post-RBC removal) and reflects an inaccurate deposit record, not an authors' computational error or an underivable value. Severity is negligible and the central conclusions hold fully; the only flag worth tracking is the data-record N inaccuracy.
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.