Spatial transcriptomics reveals the molecular signatures of prodromal and advanced α-synucleinopathy.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values are derivable from the shared data
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
Described well enough for a clean PARTIAL 1:1 of the in-scope target. NOTE the enriched metadata was wrong: the paper states 'No new code was generated' (QuPath is third-party imaging software, not a pipeline/repo), and the primary dataset is GSE274605 (Visium ST), not GSE26927. The paper's primary ST DEG counts (142/132/395/656) were deliberately NOT attempted (TARGET B/C): they depend on manual Loupe Browser per-spot region annotation + ES/LS staging, not a scriptable artifact (the hard 20%). We reproduced TARGET A: the cross-validation of ST signature genes in 4 PUBLIC human PD microarray datasets (GSE26927/GSE7621/GSE20146/GSE43490), by applying the standard curated-GEO limma PD-vs-control DGE workflow the paper itself cites (ref 25) to the paper's own deposited public data (valid per P16). The single most specific, falsifiable claim reproduced EXACTLY: SPP1 up at LogFC>=0.25 in GSE26927/GSE7621/GSE43490 but NOT in GSE20146 (+0.10), and not consistently significant. CREBBP (their protein-validated hit) is up in all 4; NUFIP2 up in 3/4. KCNJ10/GPR37/ROCK2 are strongly up in the SN datasets but reach >=3 only if GSE43490's 3 regions are counted separately (the paper's '>=3 datasets and/or regions' wording) - not chased. Fig S4B inconsistent-direction set: ELAVL4/GABARAPL1/ACHE reproduce as inconsistent; NRXN3/SLC18A2 reproduce as consistently down (canonical PD loss). Overall: described-well-enough, an honest 1:1 with the headline claim exact and the rest partial/qualitative. Fabrication concern: none - all checked values derive from the shipped public data. Not attempted: the GSE274605 ST pipeline and all imaging/wet-lab results.
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Assessment versions
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v1 current initial assessment Score 70assessed: 2026-06-14 ⛓ ba09cb2fe7fb
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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: opusDoes spatial transcriptomics of brainstem α-synuclein pathology in the M83+/+ mouse model reveal stage-specific molecular signatures (prodromal vs. advanced) that are recapitulated in human PD brains and could serve as biomarkers or therapeutic targets?
- ★ Induction of aSyn pathology in rodent brainstem at the early/prodromal stage triggers upregulation of energy metabolism pathways (glycolysis, oxidative phosphorylation, fatty acid metabolism) finding
- ★ The late/symptomatic stage is characterized by drastic downregulation of mitochondrial metabolic pathways and perturbed mRNA translation machinery, plus inflammatory response finding
- ★ Aberrant osteopontin (SPP1) signaling and increased CREB-binding protein (CREBBP) expression are consistent markers of progressive aSyn pathology in both the rodent model and PD patient brains finding
- ★ Spatial transcriptomics applied to sagittal brain sections can spatially resolve gene expression in register with neuroanatomical annotations and disease stage method
- ★ Cross-validation of rodent ST candidates against 4 independent PD patient microarray datasets identifies conserved molecular signatures method
- ★ Increased CREBBP expression is a unique marker of advanced α-synuclein pathology finding
- An online interface/database of the spatial mouse PD transcriptomic data is provided as a community resource resource
- Aberrant ROCK2 and SPP1 signaling reflect progressive tissue damage mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Spatial transcriptomics (ST) | M83+/+ (Prnp-SNCA*A53T) transgenic mouse sagittal brain sections | intramuscular murine PFF aSyn delivery (early stage DPI-45; late stage DPI-75); PBS control | spatially resolved gene expression / differentially expressed transcripts by brain region | 10x Genomics Visium v1 |
| Immunofluorescence (IF) | M83+/+ mouse brain regions (GRN/pons, PAG/midbrain, DCN/cerebellum, MD/thalamus) | intramuscular PFF aSyn vs PBS | phosphorylated aSyn (p-aSyn, S129) intensity as % of total area | — |
| Immunofluorescence (IF) | M83+/+ mouse brain regions (GRN, PAG, DCN, MD) | intramuscular PFF aSyn vs PBS | CREBBP/CBP intensity as % of cells in total area | — |
| Cell-cell communication analysis | M83+/+ mouse ST dataset | none (computational) | ligand-receptor signaling patterns (e.g., SPP1, IGF, FGF) | CellChat |
| Gene set enrichment analysis (GSEA) | M83+/+ mouse ST profiles | none (computational) | HALLMARK and KEGG pathway enrichment / gene ontology | MSigDB / STRING |
| Curated microarray gene expression analysis | PD patient brain (SN, LC, GPi/striatum, dmX) from 4 GEO cohorts (GSE26927, GSE7621, GSE20146, GSE43490) | PD vs control (none) | differential gene expression cross-validating rodent ST candidates | GEO microarray datasets |
- ▲ Early stage associated with upregulation of ATP metabolic/biosynthetic processes (glycolysis, OXPHOS, fatty acid metabolism) in disease-affected regions
- ▼ Late stage associated with downregulation of metabolic processes and cytoplasmic translation, plus perturbed microtubule transport, synaptic vesicle exocytosis, neuronal apoptosis, chromatin remodeling
- – Global transcriptomic profiles: ES 961 upregulated / 241 downregulated; LS 669 upregulated / 1,465 downregulated transcripts ES 961up/241down; LS 669up/1465down
- – 16,833 unique protein-coding transcripts identified, clustering by anatomical annotation and disease stage 16,833 transcripts
- ▲ SPP1 (osteopontin) signaling unique to aSyn-pathology cohorts, originating in pons with progressive receptor interactions in midbrain at LS
- ▲ Spp1 among transcripts with global upward trend (up in ES, further increased in LS), along with mt-Nd4l, mt-Atp8, Apod, Rbm3, Gabra1, Adcy1
- ▲ Crebbp/CBP increased abundance in brains of M83+/+ mice as a marker of advanced pathology
- ▲ Late-stage disease-affected regions (pons, midbrain, white matter) enriched for inflammatory response, interferon alpha/gamma, complement, TNF/NF-κB signaling
- count 16,833 unique protein coding transcripts (total transcripts identified in ST data)
- count ES: 961 upregulated, 241 downregulated; LS: 669 upregulated, 1,465 downregulated (global DGE profiles by stage)
- count 1,325 unique transcripts (ES: 142 up, 132 down; LS: 395 up, 656 down) (transcripts prioritized for cross-validation in PD datasets)
- fold_change Log2 fold change ≥±0.25; adjusted p ≤ 0.05 (cut-off criteria for significant transcripts)
- count 8 ST annotation/region profiles (brain region annotations delineated)
- other spot 55 μm diameter; 100 μm center-to-center inter-spot distance (Visium v1 capture resolution)
- count PBS n=2; ES (DPI-45) n=3; LS (DPI-75) n=3 (cohort sizes for IF; statistical significance not achieved)
- other 4 independent microarray datasets (GSE26927, GSE7621, GSE20146, GSE43490) (PD patient cohorts for cross-validation)
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 applied 10× Genomics Visium spatial transcriptomics to sagittal brain sections from a transgenic mouse model of α-synucleinopathy at two disease stages (early: n=3, late: n=3, controls: n=2), using a log2 fold-change and adjusted-p-value threshold to identify differentially expressed genes, followed by gene set enrichment analysis (HALLMARK and KEGG via MSigDB) and CellChat ligand-receptor communication analyses. Immunofluorescence protein quantification across the three groups was tested with Kruskal-Wallis ANOVA and Dunn post-hoc comparisons, with results reported as mean ± SD. Candidate transcriptomic signatures from the mouse model were cross-validated by curated analysis of four independent human PD microarray datasets from the GEO repository.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Kruskal-Wallis ANOVA with Dunn post-hoc multiple comparisons | Quantification of p-aSyn (S129) immunofluorescence intensity in GRN, PAG, DCN, and MD across three cohorts (Figure 1C) | PBS n=2, DPI-45 n=3, DPI-75 n=3 (total n=8) | not stated |
| Kruskal-Wallis ANOVA with Dunn post-hoc multiple comparisons | Quantification of CREBBP/CBP immunofluorescence intensity in GRN, PAG, DCN, and MD across three cohorts (Figure 5C) | PBS n=2, DPI-45 n=3, DPI-75 n=3 (total n=8) | not stated |
| Differential gene expression analysis with adjusted-p-value cutoff (underlying statistical model not named in available text; STAR Methods not provided) | Spatial transcriptomics DGE between PBS, ES, and LS; cutoff: Log2FC ≥ ±0.25 and adjusted p ≤ 0.05 (Tables S2–S5) | n=2 PBS, n=3 ES, n=3 LS (sections/animals) | not stated |
| Gene set enrichment analysis (HALLMARK and KEGG gene sets via MSigDB) | Pathway-level characterization of global and region-specific ST DGE profiles (Figure 2F, Figure S1B) | — | not stated |
| CellChat ligand-receptor cell-cell communication analysis | Identification of ligand-receptor signaling patterns at ES and LS in ST data (Figure S3) | — | not stated |
| Curated microarray gene expression analysis (specific test not stated in available text) | Cross-validation of mouse ST candidates in four human PD GEO datasets (GSE26927, GSE7621, GSE20146, GSE43490) | — | not stated |
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Immunofluorescence group comparisons used Kruskal-Wallis ANOVA with Dunn post-hoc tests, with p-values shown on graphs but all results non-significant↳ Could also: Reporting an effect size metric (e.g., eta-squared for Kruskal-Wallis, or rank-biserial correlation for pairwise comparisons) alongside p-values would also characterize the data — With group sizes of n=2–3, no test has meaningful statistical power, making the p-value largely uninformative; an effect size conveys the magnitude of the observed difference independently of sample size and helps readers judge biological relevance
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Dispersion in immunofluorescence bar graphs was reported as mean ± SD↳ Could also: Overlaying individual data points (dot/strip plots) on bar graphs, or replacing bar graphs with beeswarm or jitter plots, would also represent the data at this sample size — With n=2–3 per group, a bar with SD can visually imply more data than exist; showing individual points alongside the mean makes the actual spread and any influential observations fully transparent, which is widely recommended for small-n biological experiments
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Spatial transcriptomics DGE was performed with a log2FC and adjusted-p threshold, but the underlying statistical model (e.g., negative binomial, linear mixed model) is not named in the available text↳ Could also: Explicitly named frameworks such as DESeq2 (negative binomial Wald test), edgeR, NNSVG (spatially aware variance modeling), or SPARK-X could also be applied and reported by name — Naming the DGE model and normalization strategy allows readers to assess its assumptions for count-based ST data and enables full reproducibility; spatially aware methods additionally account for the spatial autocorrelation inherent to Visium data
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Pathway enrichment was conducted using overrepresentation analysis on a thresholded gene list (Log2FC ≥ ±0.25, adjusted p ≤ 0.05) against HALLMARK and KEGG gene sets↳ Could also: Rank-based gene set enrichment analysis (preranked GSEA) using the full continuously ranked gene list (ranked by Log2FC or signed –log10 p-value) could also be applied — GSEA uses the complete ranked gene list rather than a binary threshold, which can detect coordinated pathway-level shifts that do not reach the chosen fold-change or p-value cutoff, and is less sensitive to the choice of thresholds
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Cross-validation of mouse ST findings in human PD was performed by curated analysis of four independent microarray GEO datasets, examined separately↳ Could also: A fixed- or random-effects meta-analysis pooling effect estimates across the four datasets could also be applied to produce a summary effect size with confidence intervals and a heterogeneity estimate (I²) — A meta-analytic approach quantifies the consistency of findings across independent cohorts, distinguishes true cross-dataset replication from dataset-specific signals, and produces an overall effect estimate with formal uncertainty bounds
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Cell-cell communication analysis was performed with CellChat applied to Visium spatial transcriptomics spot-level data↳ Could also: Alternative tools such as NicheNet, LIANA (which aggregates multiple methods), or Squidpy's spatial co-expression modules could also be applied to the same data — Different communication inference tools rely on distinct ligand-receptor databases and scoring algorithms; convergent signals across two or more tools strengthen confidence that identified interactions reflect biology rather than a database or algorithmic artifact
Citation network
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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-41736854
Title: Spatial transcriptomics reveals the molecular signatures of prodromal and advanced α-synucleinopathy. iScience 2026. DOI 10.1016/j.isci.2026.114845. PMCID PMC12927100.
⚠️ Metadata correction (enriched pointers were wrong/misleading)
The auto-enriched scaffold listed:
code_url = https://github.com/qupath/qupathdata_accession = GSE26927
Both are misleading once you read the paper's Data and code availability:
• Data: … publicly accessible through the NCBI GEO repository (accession number GSE274605). • Code: No new code was generated in the study.
- QuPath (v0.5.1) is third-party image-analysis software listed in the Key Resources Table, used only for histology/immunofluorescence image quantification (a wet-lab/manual step). It is not an analysis pipeline / authors' repository. → The "code" pointer is a text-mining false positive.
- GSE274605 is the paper's own primary dataset: Visium spatial transcriptomics (ST) of M83⁺/⁺ mice (Space Ranger 1.3.0 + Seurat 4.3.0).
- GSE26927 is only one of four pre-existing public PD microarray datasets the authors re-analyzed for cross-species validation. The other three are GSE7621, GSE20146, GSE43490.
There is no authors' code repository. Per brief rule P16, reproducing by applying a standard third-party method to the paper's own data is fully valid.
Reported pipeline-derived results (candidate reproduction targets)
| # | Result | Pipeline | Data | In scope? |
|---|---|---|---|---|
| A | Cross-validation of ST signatures in human PD microarrays (Fig S4A/B, Table S6): specific genes up/down in PD vs control across 4 GEO microarray datasets | Standard curated GEO DGE ("as described previously", ref 25 = Gomes Moreira & Jan, Sci Data 2023) → limma PD-vs-control per dataset | 4 public GEO series: GSE26927 (SN), GSE7621 (SN), GSE20146 (GPi), GSE43490 (SN/dmX/LC) | YES — primary target (80/20) |
| B | ST DEG counts: 1,325 select transcripts (ES 142↑/132↓; LS 395↑/656↓) | Space Ranger 1.3.0 → Seurat SCTransform → FindMarkers (log2FC≥±0.25, adj.p≤0.05); ES/LS staging + region annotation done manually in Loupe Browser | GSE274605 | NO — hard 20% (see below) |
| C | ST clustering / UMAP / GO / GSVA / KEGG, GO bubble plots | Seurat + msigdbr + GSVA + Pathview + STRING | GSE274605 | NO — depends on B's manual annotation |
| D | Histology: %area pSyn, CREBBP/ROCK2 IF intensity (Fig 6, Fig S6) | QuPath image quantification of microscopy | microscopy images (not deposited as a pipeline) | NO — wet-lab/manual imaging |
Why B/C are the hard 20% (deliberately not chased)
The ST pipeline's region labels and ES/LS disease-stage groupings come from a manual step: "spots under tissues were selected in LoupeBrowser, json files were then exported and used for alignment" and regions assigned by hand from a mouse brain atlas. The exact ES-vs-control / LS-vs-ES sample groupings and per-spot annotations driving the 142/132/395/656 counts are not a scriptable artifact, so a faithful 1:1 of the counts isn't cleanly achievable from the deposited data alone. Out of scope per brief rule 3 (do not chase the last 20%).
Primary reproduction target (A) — concrete claims
From the Results section "Curated analyses of patient-derived microarray datasets …" and Fig S4A:
- SPP1: increased (LogFC ≥ 0.25) in all microarray datasets except GSE20146 (GPi); pattern not consistently significant (by p-value).
- KCNJ10, GPR37, CREBBP, NUFIP2, ROCK2: consistently upregulated in PD vs control across ≥3 datasets/regions.
- (Fig S4B, secondary) downregulated-in-ST genes NRXN3, ELAVL4, SLC18A2, GABARAPL1, ACHE: significantly altered but inconsistent direction across the PD microarrays.
These are crisp, falsifiable, gene-level directional claims on fully public data → ideal for an honest 1:1 check.
Met
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is an honest partial reproduction of the secondary target only: the cross-validation of ST signature genes in four public PD microarrays (Fig S4A/B), reproduced via a cited generic limma workflow since the paper deposited no code. The headline falsifiable claim (SPP1 up>=0.25 in every dataset except GSE20146, not consistently significant) reproduced exactly, and protein-validated CREBBP is up 4/4; deviations (C2 genes only 2/4 pooled; NRXN3/SLC18A2 consistently down vs reported 'inconsistent') sit on the our-method/underspecified-wording side, not on the authors' or fabrication side — all checked values derive from the shipped public data. The paper's primary ST conclusion (GSE274605 DEG counts 142/132/395/656) was out of scope (manual Loupe annotation + ES/LS staging), so overall confirmation is solid but limited, and the scaffold metadata (QuPath as code, GSE26927 as primary) is erroneous.
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Reproduction footprint
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