Deep transcriptomics reveals cell-specific isoforms of pan-neuronal genes.
Part of the results reproduced; minor but material deviations remained.
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- Reproduced
- 2026-06-16
- Rubric version
- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no human curator yet
- Last updated
- 2026-07-29
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Deep full-text extraction
Model: opusCan deep, replicate single-neuron-type transcriptomes from C. elegans reveal cell-specific alternative splicing patterns (including in pan-neuronal genes) and the regulatory factors that establish them, overcoming the low capture/sensitivity limits of conventional single-cell RNA-seq?
- ★ Pan-neuronal genes (expressed in many/all neurons) harbor highly cell-specific splice variants/isoforms restricted to single or few neuron types. finding
- ★ Differential intron retention is widespread across neuron types, accounting for ~52% of all differential splicing — an under-appreciated source of cell-specific gene regulation. finding
- ★ A 'uniqueness index' algorithm aggregating significant differential splicing across all pairwise cell comparisons identifies cell-specific isoforms and candidate regulatory factors. method
- ★ Gene expression and alternative splicing are globally distinct and regulated orthogonally across neuronal cell types (poor correlation between the two). finding
- ★ Three distinct splicing factors (RNA binding proteins) are employed in vivo to control splicing in a single neuron. mechanism
- ★ Deep CeNGEN transcriptomes with biological replicates enable high-confidence splicing analysis even for lowly-expressed genes due to uniform gene-body coverage. resource
- A user-friendly platform was developed for spatial transcriptomic visualization of splicing patterns at single-neuron resolution. resource
- Different types of alternative splicing are correlated with each other across cell types (concerted regulation), except intron retention which correlates less. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Short-read bulk RNA-seq of sorted neuron-type populations (rRNA-depleted) | C. elegans isolated neuron types (46 of 118 anatomically-distinct types, CeNGEN consortium) | none (neuron-type-specific fluorescent transgene labeling/sorting) | differential gene expression and alternative splicing (PSI/ΔPSI) across neuron types | Illumina short-read sequencing |
| Whole-animal polyA-selected RNA-seq (validation) | C. elegans wild-type whole animals | none | intron retention levels to confirm cell-type observations | — |
| RT-PCR (orthogonal validation of intron retention) | C. elegans whole-animal population RNA | none | presence of intron-skipped and intron-retained products for 5 retained introns | — |
| Transgenic fluorescent splicing reporter (GFP/RFP/BFP imaging) | C. elegans neurons (pan-neuronal rgef-1 promoter; cholinergic unc-17 promoter BFP) | transgene/overexpression (dgk-1 GFP/RFP splicing reporter) | in vivo cell-specific splice-site selection (upstream RFP vs downstream GFP) in motor neurons | — |
| In silico differential splicing analysis (JUM) and differential expression (DESeq2) | C. elegans neuron-type transcriptomes (all pairwise comparisons) | none | cassette exons, 5'/3' splice sites, intron retention, composite events; uniqueness index | JUM, DESeq2 |
- – Intron retention accounts for over half of all differential splicing across neuron types 52%
- – Differential intron retention is >7-fold more prevalent than differential cassette exon splicing 7-fold
- – Total alternative splicing events detected across genes 15,515 events in 5779 genes
- – AVM vs AVL comparison yields differential cassette exons; 2070 total pairwise comparisons performed 75 cassette exons
- – unc-31 exon 3 inclusion varies continuously across neurons (AVL 78%, AVM 53%, AVG 1%); uniqueness index 23 in AVL 78% vs 53% vs 1%; index=23
- ▲ pct-1 intron consistently spliced in most neurons but retained in CAN neuron 69% retained in CAN vs ~0%
- – mec-2/Stoml3 has highest 3' splice-site uniqueness; touch neurons (AVM/PVM) select downstream 3' splice site, others upstream
- – dgk-1 alternative 3' splice site uniquely used in excitatory motor neurons (DA/VB) but not inhibitory (VD/DD), confirmed in vivo by reporter isoforms of 67 vs 31 aa
- count 15,515 alternative splicing events (total AS events in distinct genes)
- count 2070 pairwise comparisons (all pairwise neuron-type comparisons)
- count 75 differential cassette exons (AVM vs AVL comparison at |ΔPSI|>10%, q<0.05)
- other 52% (fraction of differential splicing that is intron retention (5' SS 27%, 3' SS 11%))
- fold_change 7-fold (intron retention more prevalent than cassette exon splicing)
- other 69% retained in CAN vs ~0% elsewhere (pct-1 intron retention; n=3,4,4,3,4,4,4 replicates)
- other ASK 72% retained vs RIC 10% (pqn-53 intron retention across neuron types)
- other uniqueness index range -45 to +45 (=23 for unc-31 in AVL) (single neuron compared against 45 others)
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 performed pairwise RNA-seq comparisons across 46 C. elegans neuron types using deep bulk transcriptomes with biological replicates generated by the CeNGEN consortium. Differential gene expression was assessed with DESeq2 and differential alternative splicing with JUM, applying ΔPSI > 10% and FDR q < 0.05 thresholds across all 2070 pairwise cell-type combinations. A custom 'uniqueness index' aggregated significant ΔPSI values across pairwise comparisons to identify cell-specific splicing events, and relationships between regulatory layers were summarized with adjusted R-squared from linear regression.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 (negative binomial Wald test with internal FDR correction) | Pairwise differential gene expression across all 2070 combinations of 46 neuron types | 46 neuron types; n=3–4 biological replicates per neuron type (exact per-comparison n not uniformly stated in text) | not stated |
| JUM (Junction Usage Model) PSI-based differential splicing with FDR q-value | Pairwise differential alternative splicing (cassette exons, intron retention, 5′ and 3′ splice sites, composite events) across all 2070 neuron-type pairs; thresholds: |ΔPSI| > 10%, q < 0.05 | Minimum 5 junction-spanning reads per biological replicate; n=3–4 replicates per neuron type | not stated |
| Custom uniqueness index (sum of all significant ΔPSI values from pairwise comparisons for one cell vs. all others) | Ranking cell-specific splicing events for each of the 46 neuron types across all alternative splicing classes | 45 pairwise comparisons per focal neuron (maximum index value ±45) | na |
| Linear regression / correlation (adjusted R-squared with 95% CI) | Correlating magnitude of differential splicing across types (e.g., cassette exons vs. 5′ splice sites) and gene expression vs. splicing differences, one point per cell-type pair | 2070 pairwise comparisons as data points | not stated |
| RT-PCR (qualitative band detection, orthogonal validation) | Validation of five selected retained introns in whole-animal RNA | null | na |
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Differential alternative splicing was quantified pairwise using JUM with PSI-based statistics↳ Could also: rMATS, SUPPA2, or LeafCutter could also be applied for differential splicing from short-read RNA-seq — These tools implement distinct statistical models (e.g., likelihood-ratio tests, Dirichlet-multinomial) that handle read-count variability differently; using a second tool in parallel is a common robustness check and can increase confidence in shared findings
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Replicate spread for PSI values was displayed as SEM↳ Could also: SD or 95% CI could also be used to summarize biological variability across replicates — With n=3–4 replicates, SD directly conveys biological variability without scaling by sample size; 95% CI communicates estimation uncertainty; both are frequently preferred over SEM for small n because SEM shrinks with n in a way that can visually understate true spread
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Relationships between splicing-type magnitudes and between expression and splicing were summarized with adjusted R-squared from linear regression↳ Could also: Spearman rank correlation could also be used to characterize monotonic association between these quantities — Spearman correlation makes no linearity or normality assumption and is more robust to outliers, which may be relevant when ΔPSI distributions are bounded (0–100%) and potentially skewed across 2070 pairwise comparisons
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A custom uniqueness index (summed significant ΔPSI) was used to rank cell-specific splicing events↳ Could also: Published cell-type specificity metrics such as the tau index, a Jensen-Shannon divergence-based score, or a one-vs-rest linear model contrast could also rank specificity — Established metrics have known statistical properties and prior benchmarks, which can facilitate cross-study comparison and help calibrate threshold selection; they also handle missing cell-type data in defined ways
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Intron retention events were orthogonally validated with RT-PCR on five selected events from whole-animal RNA↳ Could also: Long-read RNA-seq (Nanopore or PacBio) on sorted neuronal populations could also validate isoform structure — Long reads capture complete isoform structure in a single read, which is particularly informative for composite splicing events where multiple choices occur on the same transcript; this would complement short-read PSI estimates with direct isoform-level evidence
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No formal power analysis or sample-size justification for n=3–4 biological replicates was reported↳ Could also: A simulation-based sensitivity analysis or post-hoc estimation of minimum detectable ΔPSI at the observed read depths and replicate variance could also be reported — Reporting the detectable effect size at the observed n and variance helps readers interpret negative results and calibrate confidence in the completeness of the differential splicing catalog
Citation network
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40379625
Paper: Wolfe Z, Liska D, Norris A. Deep transcriptomics reveals cell-specific
isoforms of pan-neuronal genes. Nat Commun 16, 2025. DOI 10.1038/s41467-025-58296-2.
Repo: https://github.com/xcwolfe/Differential-Expression-in-C-elegans @ 4528b8c (own code, R-markdown pipeline scripts 1–13).
Data: CeNGEN bulk neuron RNA-seq, SRA PRJNA952691 (Barrett et al. 2022).
What the repo actually ships (decides reproducibility)
Barrett_et_al_2022_CeNGEN_bulk_RNAseq_data.csv— a count matrix (genes × replicates). VERIFIED: 31173 genes × 160 replicates spanning 41 neuron types — NOT the 46 types / 180 replicates the paper analyzed. Missing 5 types: AIM, AVL, DVC, LUA, SMB. README calls it a "sample count matrix … for all 46 cell types," but it is a 41-type/160-rep subset.genenames.csv(31173 WBGene IDs),simplemine_results.csv(484 RBPs),experimental_conditions_46_neurons.csv(180 rows, the full design table), GTF + refFlat annotations.- R-markdown scripts 1–13 (DESeq2/PCA/UMAP, RBP search, JUM/DCC splicing analysis).
- Does NOT ship: the full 46-type matrix, any JUM/STAR output, BAM/fastq, single-cell data. Splicing results require regenerating alignments from SRA.
In scope (pipeline-derived, reproducible from shipped data)
| # | Result | Pipeline | Script | Feasibility |
|---|---|---|---|---|
| C1 | Count matrix dimensions (genes, replicates, neuron types) | data inventory | repo files | trivial, done |
| C2 | RBP gene list size (484) | WormBase list | simplemine_results.csv |
trivial, done |
| C3 | PCA separates neuron classes; PC1/PC2 variance | prcomp/DESeq2 vst+plotPCA |
1, 3 | runs on shipped matrix |
| C4 | DESeq2 type-vs-type differential expression → DE-gene-count matrix ( | log2FC | >2, padj<0.01) | DESeq2 ~type |
| C5 | # pairwise comparisons = n·(n−1) | arithmetic | 2 | trivial |
Heavy tier (gradeable headline numbers, require full SRA re-processing)
| # | Result (paper) | Pipeline | Why heavy |
|---|---|---|---|
| H1 | 15,515 AS events in 5779 genes | STAR → JUM over 2070 pairwise comparisons | author's own "~48 h / ~3300 files megaloop"; needs all 180 deep transcriptomes from SRA + JUM tool |
| H2 | 52% intron retention / 27% A5S / 11% A3S | JUM ΔPSI categorisation | aggregate over all comparisons |
| H3 | 352 pan-neuronal genes at median cutoff | single-cell (PRJEB22693) + JUM | needs non-shipped single-cell data |
Out of scope (wet-lab / manual / external — not attempted)
RT-PCR validation of 5 introns, transgenic two-color splicing reporters, VISTA-SPLICE Tableau dashboard, amino-acid/reading-frame manual annotation, PhyloP conservation (author states script 13 was not in the final manuscript).
Strategy
Phase 1 (core): run the authors' DESeq2/PCA code on the shipped 41-type matrix; grade structural facts (C1,C2,C5) and the DE/PCA pipeline (C3,C4); honestly record the 41/160 vs 46/180 data-scope gap. Phase 2 (stretch): assess/attempt a scoped STAR+JUM splicing run from SRA. No fabrication; honesty over coverage.
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 authors' DESeq2+PCA count-matrix pipeline reproduces cleanly on the shipped data, with C4=31173 genes and C5=484 RBPs exact and PCA/DE confirming the qualitative 'neurons separate by type' premise. The main deviation is a data-scope gap: the repo ships a 41-type/160-replicate subset (README claims all 46), so 46/180/2070 become 41/160/1640 — an input/availability issue, partly the authors' incomplete deposit. The title-level splicing claims (15515 AS events, 52/27/11%, 352 pan-neuronal genes) were not reproduced because they require ~20.4 TB SRA regeneration and non-shipped single-cell data — a feasibility/availability blocker, not a contradiction. Net: solid where computable, incomplete where data/compute were unavailable; no fabrication signal.
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