Single-cell analysis of testicular bacterial microbiome changes during aging and effect on reproductive capacity in mice.
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
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡The deviation was non-trivial in magnitude
- 🟡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 the pipeline-derived microbiome figures 1:1 at the genus level. The paper reports NO original code and uses the third-party INVADEseq pipeline (FredHutch/invadeseq, commit 43f6d94: Cell Ranger -> GATK PathSeq -> Kraken2) on 10x 5'+16S testis libraries; GEO GSE303193 ships that pipeline's OUTPUT as two small per-cell genus matrices (43779 bacterial-positive cells x 193 genera, 6 mice). I recomputed the Figure-3 summary statistics from those deposited matrices on «our HPC» (pure-python, «job») and compared to the paper. RESULT: the headline taxonomy (Achromobacter most abundant, Ensifer second; Fig 3E) reproduces EXACTLY; UMIs/mouse (18294 vs ~17000) and UMIs/positive-cell (2.51 vs ~2) are within tolerance of the figure read-offs; reads/mouse (64755 vs ~50000) and the aging up-trend (old 1.6x young) reproduce in direction/order; the ~50% positive-cell ratio is partial because the shipped matrix contains only positive cells (per-sample GEX denominator absent) but 43779/72326 = 60.5% overall is consistent. Every graded value is derivable from the shipped data -> no fabrication observed at the figure level. NOT ATTEMPTED (the hard 20%, per 80/20): the full INVADEseq re-run from raw FASTQ (PRJNA1294316, 12 libraries) requiring the licensed Cell Ranger binary, a ~30GB GATK PathSeq DB, Kraken2, and Nextflow -> so the matrices were verified for internal consistency vs the figures, not independently re-derived from reads. Also out of scope: wet-lab assays, CellChat/AUCell downstream, M1/M2 macrophage percentages, steroidogenic gene activation (Fig 7).
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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 ⛓ f091fbefbb9f
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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
- 2026-06-14
- 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 how the testicular bacterial microbiome is composed and spatially distributed across testicular cell types, how it changes with aging, and how bacteria-host cellular interactions impact the testicular microenvironment and reproductive capacity in mice.
- ★ INVADE-seq simultaneously captures host and bacterial transcripts to map bacteria-host interactions across diverse testicular cell types at single-cell resolution method
- ★ A sparse but widespread bacterial presence exists across multiple testicular cell types finding
- ★ Somatic cells and early germ cells (spermatogonia) located outside the blood-testis barrier show relatively higher bacterial abundance finding
- ★ Testicular bacterial load increases with age, coinciding with transcriptional signatures of reduced BTB function finding
- ★ Bacterial-positive Leydig cells exhibit activation of steroidogenic genes while macrophages upregulate autophagy and immune modulation (M2) pathways mechanism
- ★ Spatial localization relative to the BTB is a crucial determinant shaping bacterial distribution within the testicular microenvironment finding
- Aging causes molecular reprogramming of testicular somatic and germ cells, including weakened BMP/GPR signaling and activated inflammatory pathways (MIF, GDF, COMPLEMENT) finding
- The dataset serves as a resource for discovering diagnostic biomarkers and therapeutic targets for BM- and age-associated male subfertility resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| INVADE-seq (single-cell RNA-seq with 16S rRNA bacterial enrichment primer) | whole testes of young (5-month) and old (20-month) male mice, n=3 each | aging (young vs old) | host and bacterial transcripts, cell type composition, bacterial UMI counts per cell | 10x Genomics Chromium 5' platform |
| Histology (H&E staining) | testis sections from young and old mice | aging | seminiferous tubule morphology, wall thickness, germ cell layers | — |
| Sperm count quantification | young and old mice | aging | sperm count | — |
| 16S rRNA bacterial sequencing / microbial annotation | testicular cells from mice | none | bacterial reads, UMIs, genus-level composition | — |
| Differential expression and GO enrichment analysis | annotated testicular cell types (young vs old) | aging | DEGs, enriched biological processes/pathways | Seurat v4.0; simplifyEnrichment package |
| Cell-cell communication analysis (CellChat) | testicular cell types, young vs old mice | aging | number and strength of ligand-receptor interactions | CellChat package |
| Gene set activity scoring (AUCell) | single testicular cells, young vs old | aging | pathway activity scores (ROS response, inflammation, DNA damage response) | AUCell |
- – 72,326 single cells passed QC (33,738 young; 38,588 old), resolving 10 distinct testicular cell populations 72,326 cells
- – Roughly 50% of cells in each sample were bacterial-positive (bacterial UMI ≥1) ~50%
- – Bacterial-positive cells averaged ~2 microbial UMIs per cell, with nearly half harboring a single bacterial UMI ~2 UMIs/cell
- ▲ Somatic cells and spermatogonia (outside BTB) showed significantly higher bacterial abundance than spermatogenic cells (SPCs, RSs, ESs inside BTB)
- – Achromobacter was the most abundant bacterial genus, followed by Ensifer
- ▼ Aged mice exhibited significant reduction in spermatogonia proportion; LCs and macrophages showed most pronounced transcriptomic divergence
- – BMP and GPR signaling showed weakened ligand-receptor interactions in aged testes (reduced Bmp7 and Nmb), while MIF, GDF, COMPLEMENT pathways were activated
- – Sperm count in aging mice did not show a significant decrease
- count 72,326 single cells (total cells passing QC (33,738 young, 38,588 old))
- count ~50,000 reads and ~17,000 bacterial UMIs per mouse (16S library bacterial sequences detected)
- count ~25,000 high-confidence reads per mouse (after filtering non-target reads)
- count ~2 microbial UMIs per bacterial-positive cell (bacterial transcriptional load)
- count ~50% (proportion of bacterial-positive cells per sample)
- pvalue *p<0.05, **p<0.01 by Student's t test (bacterial-positive cell ratio across cell types)
- fold_change fold change >1.5 cutoff (stricter DEG cutoff for LCs and macrophages in aged testes)
- count 10 distinct cellular populations (cell types resolved in mouse testis)
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 study applied INVADE-seq (10× Genomics 5′ scRNA-seq augmented with a 16S rRNA primer) to 72,326 single cells from 3 young and 3 old mice to characterize testicular bacterial microbiome composition and host-microbial interactions. Differential gene expression between age groups was assessed per annotated cell type, and bacterial abundance across cell types and BTB compartments was compared using Student's t-tests. Cell-cell communication was inferred via CellChat, gene set activity was scored with AUCell, and results were visualized as UMAP projections, GO enrichment bubble plots, and violin/box plots.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Student's t-test (one-tailed direction unspecified in Figure 3F legend) | Comparison of bacterial-positive cell ratio across testicular cell types (Figure 3F) | n=3 young, n=3 old mice (6 biological replicates pooled) | not stated |
| Two-tailed t-test | Comparison of bacterial UMI counts per bacterial-positive cell across testicular cell types (Figure 3G) | n=3 young, n=3 old mice | not stated |
| Sample-paired differential analysis (method not further specified) | Comparison of bacterial abundance between cells located inside vs. outside the blood-testis barrier (Figure 3H) | 6 samples (n=3 per age group) | not stated |
| Louvain graph-based clustering (Seurat v4.0) | Unsupervised cell-type clustering from top 50 PCs of the scRNA-seq data | 72,326 single cells | na |
| Differential gene expression analysis (specific statistical test not stated; Seurat v4.0 default is Wilcoxon rank-sum) | Age-associated DEGs per annotated cell type (Table S1, Figures 2B–2E) | 33,738 young cells vs. 38,588 old cells | not stated |
| Gene Ontology (GO) over-representation/enrichment analysis (specific method/package not stated) | Pathway enrichment of top 30 marker genes per cluster (Figure 1E) and age-associated DEGs per cell type (Figures 2C–2E) | null | not stated |
| AUCell gene-set activity scoring | Per-cell activity scores for ROS response, inflammatory response, DNA damage response pathways (Figures 2F, 2G, S2E) | 72,326 single cells | na |
| CellChat ligand-receptor interaction inference | Age-dependent rewiring of cell-cell communication (Figures 2H, 2I, 2J) | n=3 young, n=3 old mice | na |
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Bacterial abundance across cell types and between BTB compartments was compared with Student's t-tests based on n=3 mice per group↳ Could also: A non-parametric test such as the Wilcoxon rank-sum (Mann-Whitney U) test could also be used for these comparisons — With only three observations per group, normality is difficult to verify; non-parametric tests make no distributional assumption, which is a common preference when n is small
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Multiple t-tests were conducted separately across several testicular cell types (Figures 3F, 3G) without a stated multiplicity correction↳ Could also: A single mixed-effects model or a non-parametric test with Benjamini-Hochberg FDR correction across cell types could also be used — Correcting for the family of cell-type comparisons explicitly controls the expected false discovery rate, a practice that is increasingly expected when many simultaneous group comparisons are made
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Dispersion around means is reported as SEM in Figure 3F and as SD in Figure 3G, resulting in mixed within-paper reporting↳ Could also: A consistent choice of SD or 95% CI across all figures could also be applied — SD and 95% CI communicate the spread of the underlying data or estimation uncertainty, respectively, whereas SEM describes precision of the mean estimate; standardizing the measure avoids reader re-interpretation when comparing panels
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The differential gene expression test used by Seurat is not explicitly named (Seurat v4.0 defaults to Wilcoxon rank-sum)↳ Could also: Pseudobulk approaches — aggregating counts per sample and applying DESeq2 or edgeR — could also be used to compare young vs. old expression — Pseudobulk methods treat biological replicates (here n=3 per group) as the unit of inference, which better controls type-I error inflation that can arise when thousands of cells from the same animal are treated as independent observations
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Significance of cell-type proportional changes (e.g., reduced spermatogonia fraction in aged mice) is described qualitatively↳ Could also: Compositional methods such as scCODA or a Dirichlet-multinomial regression could also be used to formally test changes in cell-type proportions — Cell-type proportions are compositional data (they sum to 1); dedicated models account for this constraint and propagate uncertainty across the full composition rather than testing each proportion independently
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No formal sample-size or power justification is provided for the n=3 per group design↳ Could also: A pilot-data-based power calculation or a statement of the minimum detectable effect size at the chosen n could also be included — With n=3 per group, statistical power is limited for detecting moderate effect sizes; pre-specifying the expected effect or reporting the detectable effect helps readers contextualize the sensitivity of the study
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-41438041
Paper: Zhou et al. 2025, iScience. "Single-cell analysis of testicular bacterial microbiome changes during aging and effect on reproductive capacity in mice." DOI 10.1016/j.isci.2025.114174 · GEO GSE303193 · SRA PRJNA1294316.
Pipeline(s) named per result
- Microbiome quantification (Fig 3, Fig 4): INVADEseq pipeline (https://github.com/FredHutch/invadeseq, commit 43f6d94; Galeano Niño et al., Nat Protoc 2023) = Cell Ranger v6.1.2 → GATK PathSeq → Kraken2 v2.1.2, on 10x 5' GEX + 16S-enriched libraries. Paper ships NO original code — third-party tool on the paper's own data (brief P16: equally valid).
- scRNA-seq atlas: Cell Ranger (mm10) → Seurat v4 / Louvain → DoubletFinder.
- Downstream: CellChat (cell-cell comms), AUCell (gene-set activity).
IN SCOPE (attempted)
Recompute Figure-3 genus-level summary statistics from the deposited INVADEseq
output (GSE303193_combined.genus.{umi,read}.matrix.csv.gz, 43779 positive cells
× 193 genera, 6 mice) and compare to the paper:
- C1/C2 dominant + 2nd genus (Fig 3E) → exact
- C3 UMIs/mouse, C4 UMIs/positive-cell (Fig 3A/D) → within-tol
- C5 reads/mouse, C6 aging trend (Fig 3A / 4A) → partial (order/direction)
- C7 positive-cell ratio (Fig 3C) → partial (denominator absent)
- C8 study design → exact
OUT OF SCOPE / NOT ATTEMPTED
- Full INVADEseq re-run from raw FASTQ (the hard 20%): licensed Cell Ranger binary + ~30GB GATK PathSeq DB + Kraken2 + Nextflow on 12 libraries. Skipped per 80/20; the shipped matrices were checked for internal consistency instead.
- Seurat cell-type atlas / UMAP / cluster counts (Fig 1–2): regenerable but param-sensitive, not the microbiome claim; not attempted.
- CellChat / AUCell / steroidogenesis / autophagy / M1-M2 macrophage % (Fig 5–7).
- All wet-lab results (sperm counts, IHC, fertility, BTB assays) — non-pipeline.
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 result reproduces cleanly: summing the deposited GSE303193 genus matrices yields Achromobacter (#1, 104,725 UMIs) and Ensifer (#2, 3,680), matching Fig 3E exactly, with UMIs/mouse (18,294 vs ~17k) and UMIs/positive-cell (2.51 vs ~2) within figure read-off tolerance and the aging up-trend confirmed in direction (1.6×). The notable gaps — reads/mouse (64,755 vs ~50k, ~30%) and positive-cell ratio (60.5% vs ~50%) — sit on data/definition (eyeballed bar-chart values; shipped matrix lacks a per-sample denominator), not on the authors' analysis or any fabrication. The main caveat is on our methodology: this is an internal-consistency check of the deposited pipeline output, not an independent raw-FASTQ re-run (deliberately out of scope). Overall a solid, explainable partial/yellow reproduction with no derivability or core-claim concerns.
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.