Lipopolysaccharide distinctively alters human microglia transcriptomes to resemble microglia from Alzheimer's disease mouse models.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡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
EXACT 1:1 REPRODUCTION. Ran the authors' own PokeMicro Seurat pipeline (Seurat 4.3.0.1) on the paper's own deposited GSE186301 count matrix on «our HPC». All 9 paper claims + 1 code-comment cross-check reproduced EXACTLY: 20231 cells, 12335 genes (min.cells=100), 8 clusters, fibroblast/proliferating clusters of 469/302 cells, 19460 microglia, and the three headline DEG counts 904 (LPS+IFNg 24h) / 802 (ATPgS 24h) / 152 (PGE2 24h) via FindConservedMarkers+metap Tippett — all to the single gene; integration features=1620 matched too. No discrepancies, no fabrication concerns. NOT attempted (out of scope): wet-lab, Cell Ranger/STAR from raw FASTQ (deposited matrix is the documented start), GO enrichment cosmetics, and the GSE109329 external mouse comparator dataset.
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 100assessed: 2026-06-20 ⛓ 06a30517f784
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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
- 2026-06-20
- 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 paper tests whether combinatory inflammatory stimulation (ATPγS, LPS+IFN-γ, PGE2, alone or combined) of human iPSC-derived microglia produces transcriptional changes relevant to modeling Alzheimer's disease, compared against transcriptomic changes in AD mouse models and post-mortem AD patient microglia.
- ★ iPSC-microglia show a shared core transcriptional response to ATPγS and to LPS+IFN-γ, suggesting a convergent mechanism of action finding
- ★ DEGs from all iPSC-microglia treatment conditions significantly overlap with genes altered in microglia from AD patients, though often with inconsistent direction finding
- ★ A common transcriptomic disease axis exists across AD genetic mouse models that separates wild-type and transgenic AD mouse microglia finding
- ★ Only LPS(+IFN-γ) shifts microglial transcriptional profiles along the identified disease axis in both mouse and human iPSC-microglia, making it the best modeled stimulus for AD among those tested finding
- PGE2 produces a milder, more temporally complex/delayed transcriptional response compared to ATPγS and LPS+IFN-γ finding
- Combining ATPγS with LPS+IFN-γ or PGE2 produces little additional (synergistic) transcriptional effect beyond ATPγS alone finding
- DEG protein products form protein-protein interaction networks with more interactions than expected by chance, supporting functional convergence of the ATPγS and LPS+IFN-γ responses finding
- CITE-seq single-cell transcriptomics was used to simultaneously measure iPSC-microglia responses to multiple stimuli and timepoints method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-seq (CITE-seq) | human iPSC-derived microglia | ATPγS (1 mM) | single-cell transcriptome / differential gene expression | CITE-seq |
| single-cell RNA-seq (CITE-seq) | human iPSC-derived microglia | LPS+IFN-γ (10 ng/ml) | single-cell transcriptome / differential gene expression | CITE-seq |
| single-cell RNA-seq (CITE-seq) | human iPSC-derived microglia | PGE2 (500 nM) | single-cell transcriptome / differential gene expression | CITE-seq |
| single-cell RNA-seq (CITE-seq) | human iPSC-derived microglia | combined treatment (PGE2 or LPS+IFN-γ for 24 h, then ATPγS added for a further 24 h) | single-cell transcriptome / differential gene expression | CITE-seq |
| Ca2+ imaging | human iPSC-derived microglia | pre-treatment with PGE2 or LPS+IFN-γ followed by ATPγS | calcium response magnitude | — |
| protein-protein interaction network analysis | protein products of iPSC-microglia DEGs | none (computational analysis of DEG sets) | number of network interactions vs randomized control | — |
| transcriptomic meta-analysis (single-cell/snRNA-seq) | mouse microglia (wild-type and transgenic AD genetic models) | genetic (AD mouse model genotype) | common disease axis of transcriptomic change | — |
| single-nucleus/single-cell RNA-seq (published datasets) | human post-mortem microglia (entorhinal cortex and prefrontal cortex, AD patients vs controls) | none (AD disease state vs control) | cell-type-specific differentially expressed genes | — |
- – Largest number of DEGs detected after 24 h LPS+IFN-γ exposure n=904 (combined P<0.05)
- – 24 h ATPγS exposure produced the second-largest DEG set n=802 (combined P<0.05)
- – 24 h PGE2 exposure produced fewer DEGs than ATPγS or LPS+IFN-γ n=152 (combined P<0.05)
- – 73 DEGs overlap across all three treatments at 24 h; strong pairwise overlaps between LPS+IFN-γ and ATPγS n=514 overlap, P≈0 (log(P)=-1007.81)
- ▲ Gene expression fold changes at 24 h correlate strongly between LPS+IFN-γ and ATPγS treatments r=0.625, P<2.2×10^-16
- – Only 20 unique DEGs found for LPS+IFN-γ(48h)+ATPγS(24h) combined vs ATPγS alone; only 2 unique DEGs for PGE2(48h)+ATPγS(24h) vs ATPγS alone 20 and 2 unique DEGs respectively
- – Overlap of AD-associated microglial DEGs between two independent post-mortem AD datasets (Grubman and Mathys) higher than expected by chance 12 overlapping genes, P=4.525×10^-12
- – iPSC-microglia DEGs show higher-than-expected overlap with AD patient microglia DEGs, but with some divergent directionality (e.g., SPP1)
- correlation r=0.625, P<2.2×10^-16 (Correlation of 24h gene expression fold changes between LPS+IFN-γ and ATPγS treatments)
- pvalue P≈0, log(P)=-1007.81 (Hypergeometric overlap of DEGs between LPS+IFN-γ and ATPγS at 24h)
- pvalue P=8.23×10^-62 (Hypergeometric overlap of DEGs between LPS+IFN-γ and PGE2 at 24h)
- pvalue P=4.51258×10^-101 (Hypergeometric overlap of DEGs between ATPγS and PGE2 at 24h)
- count 904 DEGs (DEGs after 24h LPS+IFN-γ exposure, combined P<0.05)
- count 802 DEGs (DEGs after 24h ATPγS exposure, combined P<0.05)
- count 152 DEGs (DEGs after 24h PGE2 exposure, combined P<0.05)
- pvalue P=4.525×10^-12 (Overlap of AD microglial DEGs between Grubman et al. and Mathys et al. post-mortem datasets)
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 used single-cell RNA sequencing (CITE-seq) to compare iPSC-derived microglia treated with various stimuli (ATPγS, LPS+IFN-γ, PGE2, and combinations) at 24h and 48h against untreated controls, across four biological replicates (donors). Differential expression was assessed per gene using a 'combined P-value' integrated across donors, gene set overlaps between conditions/datasets were tested with the hypergeometric test, fold-change relationships were assessed with Pearson correlation, Gene Ontology enrichment was reported with adjusted P-values, and protein-protein interaction network enrichment was assessed via randomization-based empirical P-values controlling for node degree and gene length.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Hypergeometric test | Overlap of DEG sets between treatments (Fig. 2A), between AD post-mortem datasets, and between iPSC-microglia DEGs and AD patient microglia DEGs (Fig. 4) | Overlap counts among DEG sets as stated (e.g. n=514, n=89, n=112, n=605, n=66) | not stated |
| Combined P-value (integrated across biological replicates/donors) | Differential expression analysis between each treatment and 0h control cells, per gene | Four biological replicates (donors); DEG counts reported (e.g. n=904 for LPS+IFN-γ 24h, n=802 for ATPγS 24h, n=152 for PGE2 24h) | not stated |
| Pearson correlation | Correlation between gene expression fold changes at 24h for LPS+IFN-γ vs ATPγS (Fig. S7) | Not explicitly stated (gene-level fold changes) | not stated |
| Gene Ontology enrichment analysis (adjusted P-value) | Enriched biological processes among up/downregulated DEGs per treatment and time point (Fig. 2B) | DEG lists per condition | not stated |
| Randomization-based empirical P-value test | Enrichment of protein-protein interactions among DEG products relative to chance, controlling for node degree and gene length (Fig. S10A) | DEG-derived PPI network nodes; P<0.0001 based on randomizations | not stated |
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Overlap between gene sets (e.g. DEGs across treatments, or DEGs vs AD patient microglia genes) was assessed with the hypergeometric test.↳ Could also: A permutation-based or GSEA-style enrichment test could also be used. — Permutation approaches can account for gene-level covariates such as expression level or gene length that a simple hypergeometric test assumes are uniform across the background set.
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Differential expression across donors was summarized using a 'combined P-value' per gene, integrating replicate information.↳ Could also: A mixed-effects/pseudobulk approach (e.g. treating donor as a random effect in a model such as MAST, or aggregating to pseudobulk counts for analysis in DESeq2/edgeR/limma) could also be used. — Since donor-to-donor clustering was observed even among untreated controls, explicitly modeling donor as a random effect can help partition biological replicate variance from treatment effects.
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Gene-level differential expression significance was thresholded at combined P-value<0.05 without a stated multiple-testing correction across the many genes tested.↳ Could also: A false discovery rate correction (e.g. Benjamini-Hochberg) applied across all genes tested could also be reported alongside or instead of raw P-values. — FDR correction is standard practice in transcriptome-wide differential expression analysis to control the expected proportion of false positives when thousands of genes are tested simultaneously.
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Gene Ontology enrichment results were reported using an adjusted P-value without the specific correction method named in the text.↳ Could also: Explicitly stating the correction method (e.g. Benjamini-Hochberg FDR, commonly used by tools such as topGO or clusterProfiler) could also be reported. — Naming the specific multiple-testing correction improves reproducibility and lets readers judge the stringency of the enrichment threshold used.
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The relationship between fold changes across treatments (e.g. LPS+IFN-γ vs ATPγS) was quantified using Pearson correlation.↳ Could also: Spearman's rank correlation could also be used to assess this relationship. — Fold-change distributions from differential expression analyses are often skewed or contain outliers, and a rank-based correlation can capture monotonic relationships without assuming linearity or normality.
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Enrichment of protein-protein interactions among DEG products was tested using empirical P-values from randomizations controlling for node degree and gene length.↳ Could also: Additional network-based methods such as edge-permutation null models or pathway topology-based approaches (e.g. SPIA) could also be used to characterize network-level convergence. — These complementary approaches can further probe whether enrichment reflects specific pathway topology rather than degree/length-matched connectivity alone.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36254682
Paper: Monzón-Sandoval et al. 2022, Dis Model Mech — "Lipopolysaccharide distinctively alters human microglia transcriptomes to resemble microglia from Alzheimer's disease mouse models." PMID 36254682 / PMC9612871.
Repo: https://github.com/jmonzon87/PokeMicro @ commit
ad29f257428709a1237093d02bc83ee2ca5866e1 (2022-03-09). Authors' own code (R /
RMarkdown, Seurat). Two scripts: iPSCmicro.Rmd (clustering + pooled-Wilcoxon
DEA) and iPSCmicro_Integrated.Rmd (per-donor integration + FindConservedMarkers
DEA — this is the source of the paper's headline DEG counts).
Primary data: GEO GSE186301 — scRNA-seq (10x + CITE-seq HTO) of iPSC-derived microglia, 4 donors × 8 stimulation conditions. Repo starts from the deposited protein-coding count matrix + metadata (NOT raw FASTQ), so Cell Ranger / STAR alignment is out of scope (no FASTQ needed; we begin at the count matrix — this is exactly the pipeline the authors documented).
Secondary data: GEO GSE109329 — external mouse microglia stimulus data used only for a cross-species comparison figure. Out of scope for the computational re-run (comparison/illustrative, not pipeline-derived from our matrix).
In scope (pipeline-derived, attempted)
Starting from GSE186301_Counts_ProteinCodingGenes.txt + GSE186301_Metadata.txt,
run the documented Seurat workflow and regenerate these reported numbers:
| id | reported value | paper location | pipeline |
|---|---|---|---|
| C1 | 20,231 single cells | Results, opening | matrix dim / CreateSeuratObject |
| C6 | 12,335 genes after min.cells=100 |
code comment (iPSCmicro.Rmd L62) | CreateSeuratObject |
| C2 | 8 clusters | Results | FindClusters res=0.1, dims 1:20 |
| C3 | cluster 6 (fibroblast-like) = 469 cells | Results | cluster table |
| C4 | cluster 7 (proliferating) = 302 cells | Results | cluster table |
| C5 | 19,460 iPSC-microglia after removing clusters 6,7 | Results | subset idents 0–5 |
| C7 | LPS+IFNγ 24h DEGs = 904 (combined P<0.05) | Results | integration + FindConservedMarkers (Tippett minimump) |
| C8 | ATPγS 24h DEGs = 802 | Results | "" |
| C9 | PGE2 24h DEGs = 152 | Results | "" |
Stage A (iPSCmicro.Rmd core): C1, C6, C2, C3, C4, C5.
Stage B (iPSCmicro_Integrated.Rmd core): C7, C8, C9 — donor-split integration
(SelectIntegrationFeatures → FindIntegrationAnchors → IntegrateData) then
FindConservedMarkers per condition vs Control, combine per-donor P with
metap::minimump (Tippett), count rows with combined P < 0.05.
Out of scope (not attempted)
- Wet-lab: iPSC differentiation, CITE-seq hashing, FACS (manual/experimental).
- Cell Ranger / STAR alignment + HTO demultiplexing (raw FASTQ; deposited matrix is the documented entry point).
- GO/rrvgo enrichment heatmaps, all PDF figure cosmetics (visual, not a reported scalar; expensive, no headline number).
- GSE109329 mouse cross-species comparison (illustrative external dataset).
Reproducibility notes / risks
- Cluster counts + DEG counts are Seurat-version sensitive (method card: Seurat 71% clean — pin version + seed). Using r-seurat 4.3.0 (authors' era was Seurat v4, 2022). Seurat sets deterministic seeds by default (FindClusters random.seed, RunUMAP seed.use=42).
- Code comment says 19,448 microglia cells while paper text says 19,460 (=20,231−469−302). We grade against the paper's 19,460 and report both.
Assessments & scoring basis
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Reproduction footprint
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