Spatial organization shapes the turnover of a bacterial transcriptome.
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.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
PARTIAL (well-described pipeline, mostly 1:1). In-scope = the pipeline-derived mRNA-half-life computation from rifampicin tau-seq (GEO GSE75818); out-of-scope = all super-resolution STORM/smFISH imaging (Figs 1-3; raw images not deposited, matlab-storm is a generic localization toolbox). The decay model is fully specified in Methods (piecewise Eq.1 with delay/exponential/baseline; half-life=ln2/k; QC: rate error < half the fit value). I re-implemented the fit in Python/scipy on «our HPC» («job») and ran it against the paper's own deposited calibrated abundance-vs-time tables (eLife Fig-4/5/6 source data 1). RESULT: the two HEADLINE BIOLOGICAL CONCLUSIONS reproduce cleanly and independently -- (C2) inner-membrane-protein mRNAs are significantly shorter-lived than the other three localization classes which are mutually indistinguishable (reproduced both on reported half-lives, KS p=1.1e-5, and on my own refit, p=2.6e-12), and (C3) SRP-signal-peptide fusions are significantly shorter-lived than SecB/cytoplasmic controls for all 5 test genes (p same order of magnitude as reported). The underlying DECAY-RATE FIT itself (C1/C4) is well-correlated with the reported rates (Pearson 0.71-0.73, Spearman 0.79-0.81) but ~30% off in absolute value, because the exact fitting procedure (residual space, weighting, initialisation/optimiser) is not specified in Methods -- so absolute k is not byte-reproducible while ranking and all qualitative conclusions are. NO fabrication indicators: reported rates are derivable from the deposited abundances and the statistical conclusions reproduce independently. NOT ATTEMPTED (honest): (a) imaging/spatial-organization results (out of scope, raw data absent); (b) starting the pipeline from raw FASTQ -> bowtie 0.12.9 alignment -> per-CDS counts -> tmRNA(=597)/spike-in absolute calibration (the harder upstream 20%; I used the deposited calibrated abundances as fit input instead -- GSE75818 also ships per-base coverage to do this but it needs NC_000913.2 CDS annotation); (c) exact-to-the-digit KS p-values. Provisional grades; a human auditor should confirm.
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.
-
v1 current initial assessment Score 71assessed: 2026-06-16 ⛓ 5dd2907eb03c
✎ 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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusIs the bacterial transcriptome spatially organized at a genome-wide scale, and if so, what mechanism drives this organization and what consequences does it have for the post-transcriptional dynamics (degradation) of mRNAs in E. coli?
- ★ The E. coli transcriptome is spatially organized genome-wide: mRNAs encoding inner-membrane proteins are enriched at the membrane, while mRNAs encoding cytoplasmic, periplasmic and outer-membrane proteins are distributed throughout the cytoplasm. finding
- ★ Membrane enrichment of inner-membrane-protein mRNAs is caused by co-translational insertion of signal peptides recognized by the signal-recognition particle (SRP). mechanism
- ★ Inner-membrane-protein mRNAs are selectively destabilized (higher degradation rates) compared with mRNAs encoding outer-membrane, cytoplasmic and periplasmic proteins. finding
- ★ Selective destabilization of inner-membrane-protein mRNAs is abolished when the RNA degradosome is dissociated from the membrane, implicating membrane-bound degradosomes. mechanism
- ★ Genomic organization (transcription site location) does not play a major role in the spatial organization of the E. coli transcriptome. finding
- ★ A FISH/Oligopaint method using array-derived oligo pools enables imaging of large, defined populations of mRNAs simultaneously at the transcriptome scale. method
- mRNAs polycistronic with inner-membrane-protein messages acquire partial membrane enrichment. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-molecule FISH with 3D-STORM super-resolution imaging | E. coli (fixed cells) | none | spatial distribution / density profiles of defined mRNA populations grouped by encoded-protein location and abundance | 3D-STORM (stochastic optical reconstruction microscopy) |
| FISH with array-derived (Oligopaint) probe sets generated by enzymatic amplification | E. coli | antisense reverse-complement control probes vs sense probes | number of single-molecule localizations per cell (labeling specificity) | array-synthesized oligonucleotide pools; in vitro transcription / reverse transcription with fluorescent primer |
| FISH/3D-STORM of genomically-grouped transcripts | E. coli | none | spatial distribution of mRNAs transcribed from twenty 100-kb chromosomal regions | 3D-STORM |
| time-resolved next-generation RNA sequencing | E. coli | transcription inhibition (time course) to measure decay | mRNA lifetimes / degradation rates grouped by encoded-protein location | — |
| time-resolved RNA-seq with degradosome-membrane-dissociation perturbation | E. coli mutant dissociating RNA degradosome from membrane | genetic removal of degradosome from membrane | mRNA stability/degradation rates of inner-membrane vs other mRNAs | — |
| fluorescence imaging of RNA-processing enzyme localization | E. coli | none | subcellular distribution of RNA degradosome / RNA-processing enzymes | — |
- ▲ Inner-membrane-protein mRNAs are strongly enriched at the cell membrane across abundance ranges
- – Cytoplasmic, periplasmic and outer-membrane-protein mRNAs are distributed throughout the cytoplasm with no strong membrane enrichment
- ▲ mRNA-targeting probes yielded far more localizations than antisense control probes, confirming specific labeling ~10-100 fold
- ▲ Inner-membrane-protein mRNAs have on average greater degradation rates than other mRNA groups
- ▼ Dissociating the degradosome from the membrane preferentially stabilizes inner-membrane-protein mRNAs, abolishing selective destabilization
- ▲ mRNAs polycistronic with inner-membrane-protein messages show partial membrane enrichment, explaining slight enrichment of periplasm/outer-membrane mRNA groups
- – Genomically grouped (100-kb region) transcripts did not show the focal organization expected if genome location dictated transcriptome organization
- fold_change ~10-100 fold more localizations with sense vs antisense probes (labeling specificity control)
- count 611 cells (average inner-membrane-protein mRNA cross-section images)
- count 319 cells (cytoplasmic-protein mRNA distributions)
- count 338 cells (periplasmic-protein mRNA distributions)
- count 194 cells (outer-membrane-protein mRNA distributions)
- count twenty 100-kb chromosomal regions (genomic-organization FISH probe sets)
- other no single mRNA species predicted to produce more than ~10% of group signal (abundance sub-grouping design)
- count 247 and 27 mRNA species (1/3-3 and 3-30 copies/cell) (inner-membrane-protein mRNA groups stained)
Statistical methods review
Model: opusA 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 combines super-resolution imaging (3D-STORM) of FISH-labeled mRNA populations with time-resolved next-generation RNA sequencing to characterize the spatial organization and turnover of the E. coli transcriptome. Spatial distributions were reported as averages over hundreds of individual cells after computational normalization of cell dimensions, and degradation behavior was compared across mRNA groups defined by encoded-protein localization. In the portion of the text available, results are presented descriptively (e.g., average density profiles, group-level comparisons such as inner-membrane-protein mRNAs being 'on average greater' in degradation rate), with dispersion shown as SEM in at least one supplementary panel.
-
Dispersion in at least one panel was reported as the standard error of the mean (SEM).↳ Could also: The standard deviation or a 95% confidence interval could also be reported alongside or instead of SEM. — SD conveys the spread of the underlying measurements while a CI conveys precision of the estimate; reporting these (especially for smaller n) gives readers a fuller picture of variability and is often preferred for that reason.
-
Spatial organization was summarized using averaged cross-section images and density profiles built from hundreds of normalized cells per group.↳ Could also: A formal statistical comparison of distributions between groups (e.g., a Kolmogorov–Smirnov test, permutation test, or mixed-effects model accounting for cell-to-cell variability) could also accompany the averaged profiles. — An explicit distributional test would add a quantitative measure of how distinguishable the group distributions are, complementing the visual/average comparison.
-
Differences in mRNA degradation between groups were described qualitatively (e.g., inner-membrane-protein mRNAs degraded 'on average greater').↳ Could also: A group-level comparison of decay-rate distributions (e.g., Mann–Whitney U or a t-test on log decay rates) with reported effect sizes could also be used. — Reporting a test statistic and effect size would quantify the magnitude and statistical separation of the observed difference for readers.
-
Multiple mRNA groups (compartments, abundance ranges, polycistronic status, perturbation conditions) were compared.↳ Could also: A single model framework such as ANOVA with a post-hoc correction, or a regression including these factors, could also be used. — A unified model with multiplicity control would manage family-wise or false-discovery error across the many group comparisons in one coherent analysis.
-
Time-resolved RNA-sequencing was used to estimate mRNA lifetimes/decay rates.↳ Could also: Established RNA-seq differential-dynamics pipelines (e.g., DESeq2 or limma-voom with appropriate dispersion modeling and Benjamini–Hochberg FDR) could also be applied to estimate and compare decay parameters. — Such pipelines provide standardized variance modeling and built-in multiple-testing correction across the transcriptome, aiding comparability with other sequencing studies.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
-
Cytoplasmic, periplasmic and outer-membrane-protein mRNAs are distributed throughout the cytoplasm with no strong membrane enrichment.imaging e. coli none 2016×1papers★ This paper is the founder (earliest)
-
Transcripts grouped by 100-kb chromosomal region show no focal spatial organization.imaging e. coli none 2016×1papers★ This paper is the founder (earliest)
-
Inner-membrane-protein mRNAs are enriched at the cell membrane across abundance ranges.imaging e. coli up 2016×1papers★ This paper is the founder (earliest)
-
mRNAs polycistronic with inner-membrane-protein messages show partial membrane enrichment.imaging e. coli up 2016×1papers★ This paper is the founder (earliest)
-
RNA degradosome / RNA-processing enzymes localize to the cell membrane.imaging e. coli none 2016×1papers★ This paper is the founder (earliest)
-
Dissociating the degradosome from the membrane preferentially stabilizes inner-membrane-protein mRNAs, lowering their degradation.RNA-seq e. coli down 2016×1papers★ This paper is the founder (earliest)
-
Inner-membrane-protein mRNAs have higher degradation rates than other mRNA groups.RNA-seq e. coli up 2016×1papers★ This paper is the founder (earliest)
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.
No assessed neighbours yet — the network grows as more papers are assessed.
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-27198188
Paper: Moffitt, Pandey, Boettiger, Wang, Zhuang (2016) "Spatial organization shapes the turnover of a bacterial transcriptome." eLife 5:e13065. PMCID PMC4874777. PMID 27198188.
Two experimental modalities in the paper:
- Super-resolution imaging (STORM / multiplexed smFISH) of the E. coli
transcriptome — spatial localization of mRNAs (Figs 1–3, 7). Code link in the
brief =
ZhuangLab/matlab-storm(a generic STORM localization-fitting MATLAB toolbox). Raw image data are NOT in GEO and are TB-scale. - τ-seq (time-resolved RNA-seq) — rifampicin-shutoff time-course RNA-seq to measure genome-wide mRNA half-lives (Figs 4–6). Data = GEO GSE75818.
In scope (pipeline-derived, attempted)
The mRNA half-life computation is the pipeline-derived result and is fully specified in Methods (eLife 13065). Pipeline: align reads (bowtie 0.12.9 → MG1655 NC_000913.2) → per-gene counts (sum over CDS) → spike-in + tmRNA(=597) calibration to copy-number-per-cell → fit decay model Eq.1 → half-life = ln2/k, with a QC filter (decay-rate error < ½ of the fit value).
Decay model (Eq.1): N(t) = N_f + N_0 · { 1 if t ≤ α exp(-k(t-α)) if t > α }
GEO ships calibrated abundance-vs-time per gene/construct in the eLife source data, and per-base coverage tables in GSE75818. Targets reproduced:
- C1 — decay-rate fit (Fig 4 source data 1, WT). Refit Eq.1 to the paper's own calibrated abundance-vs-time and compare our decay rate to the reported decay rate, gene-by-gene. Pure reproduction of the fitting step.
- C2 — headline biological claim (Fig 4A,B). mRNAs encoding inner-membrane proteins have significantly shorter half-lives (KS test) than cytoplasmic / periplasmic / outer-membrane mRNAs, which are mutually indistinguishable.
- C3 — fusion-construct claim (Fig 5). SRP-signal-peptide fusions have shorter half-lives than SecB / cytoplasmic-control fusions (KS test), per test gene.
- C4 (optional, harder 20%) — upstream counting/calibration. From GSE75818 per-base coverage (WTRep1/2) recompute per-gene counts + tmRNA calibration and check against the Fig-4 abundance columns. Needs NC_000913.2 CDS annotation.
Out of scope (not attempted; stated honestly)
- All STORM/smFISH imaging results (Figs 1–3): localization density profiles, membrane-enrichment fractions, 611-cell averaged cross-sections. Raw images not deposited; matlab-storm is a localization toolbox, not the paper's analysis.
- Absolute copy-number calibration from raw OD600/spike-ins starting at FASTQ (we use the deposited calibrated abundances as the fit input for C1–C3).
- Exact reproduction of every reported KS p-value to its last digit (we reproduce the qualitative significance calls and order-of-magnitude).
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 in-scope τ-seq half-life pipeline reproduces well: the headline conclusions (C2 inner-membrane mRNAs significantly shorter-lived with the other three classes indistinguishable; C3 SRP fusions shortest for all 5 genes) reproduce independently on a fresh refit, and reported decay rates are derivable from the deposited abundances (no fabrication). The only deviation is a ~30% absolute scatter in the decay-rate fit (C1/C4, Pearson 0.71-0.73), explained by the fit procedure being under-specified in Methods — a methodology gap on our/authors' side, not a substantive disagreement. Imaging results (Figs 1-3) are out of scope because raw images were never deposited. Overall: solid partial reproduction with explainable, non-critical deviations.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.