Lamin C is required to establish genome organization after mitosis.
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
- ✓Reported values were directly comparable
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡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 paper's LAD-calling pipeline (pyLAD/LADetector, the authors' own CBS/DNAcopy tool) was reproduced 1:1-ish: feeding the deposited per-GATC-fragment log2(Dam-Lamin/Dam) score tracks (GSE97095 GPL13112, mm9) into pyLAD with the paper's exact parameters (merge LADs <25kb, post-filter >100kb) regenerates the authors' deposited LAD calls at 94-99% reciprocal base coverage, with #LADs within ~6% and %genome within ~1% for all three knockdown conditions (shA/shLmnA, shC/shLmnC, shB1/shLmnB1). Not byte-identical because CBS permutation testing has no fixed segmentation seed and the authors' pyLAD version is unpinned -> within-tol. The cross-condition '>90% base-coverage preservation of WT LADs' claim (C2) was partially reproduced via a cross-knockdown pairwise proxy (most directions >90%; reproduced pattern matches deposited); the literal WT-vs-KD comparison was not done because the WT MEF track is bundled only in GSE97095_RAW.tar. NOT attempted (the hard 20%): full pipeline from raw SRA reads (FASTQ->bowtie1 mm9->count->ratio; deposited scores taken as given), the WT-vs-KD comparison, and all wet-lab/imaging results (out of scope, non-pipeline). Notable engineering: pyLAD is py2-era + uses numpy.distutils f2py over legacy DNAcopy Fortran; getting it to build/run on a modern «our HPC» toolchain needed 5 patches (BOZ-in-transfer, two py3 str/bytes fixes, version.py, TMPDIR). No fabrication indicated: every compared value is derivable from the shipped GEO data via the shipped tool.
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
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v1 current initial assessment Score 76assessed: 2026-06-15 ⛓ 55e17aeb6b47
✎ 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
- 2026-06-15
- 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 whether lamin C, as distinct from lamin A, is uniquely required to establish and maintain the 3D organization of lamina-associated domains (LADs) and overall chromosome architecture, particularly during the re-establishment of genome organization as cells exit mitosis.
- ★ Lamin C, and not lamin A or lamin B1, is required for LAD:LAD cohesion, retention of LADs near the nuclear envelope, and overall chromosome territory organization. finding
- ★ Depletion of lamin C alone fully recapitulates the genome disorganization caused by depleting both A-type lamins (lamin A and C). finding
- ★ Lamin C remains nucleoplasmic during telophase/early G1 and is significantly delayed in associating with the reforming nuclear envelope relative to lamin A, with this timing correlating with post-mitotic LAD re-association with the NE. mechanism
- ★ The lamin C-dependent loss of LAD/LAS peripheral localization can be rescued by re-expression of mCherry-lamin C but not mCherry-lamin A. finding
- DamID-seq (population-averaged) detects no significant differences in LAD positioning or boundaries upon lamin depletion, whereas single-cell imaging reveals strong disruption, highlighting a limitation of population-aggregated methods. method
- shRNA tools (shA, shC, shAC, shB1) specifically and individually deplete lamin isotypes in MEFs. resource
- Differential post-mitotic NE association of lamin A versus lamin C is linked to differential phosphorylation. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Western blot | mouse embryonic fibroblasts (MEFs) | shRNA knockdown of lamin A (shA), lamin C (shC), both (shAC), lamin B1 (shB1), control (shLacZ) | lamin isotype protein levels / knockdown specificity | — |
| Cell growth/proliferation assay | MEFs | shRNA knockdown (shA, shC, shAC, shB1, shLacZ) | growth rate / doubling time over 4 days | — |
| DamID-seq | MEFs | shRNA knockdown (shAC, shA, shC, shB1, control) | genome-wide LAD positioning and boundaries (log2 DamID signal) | Dam-lamin B1 |
| 3D-immunoFISH with Chromosome Conformation Paints (CCP) | primary MEFs, chromosome 11 | shRNA knockdown (shAC, shA, shC, shB1, control) | LAD vs non-LAD spatial localization, LAD object volumes, % disrupted nuclei, NE proximity (lamin B1/A-C marker) | — |
| 3D-immunoFISH of TCIS lacO array (eGFP-LacI visualization) | clonal TCIS MEF lines (clone Y, clone 12) bearing Ikzf1 LAS I | shRNA knockdown (shA, shC, shAC, shB1) and rescue with mCherry-lamin A or mCherry-lamin C | peripheral (NE) association of lacO locus by co-localization with lamin B1 (or lamin A/C) | — |
| Live/fixed fluorescence imaging of lamin localization during mitotic exit | MEFs co-expressing mCherry-lamin A and eYFP-lamin C | none (anti-lamin B1 antibody used) | post-mitotic NE incorporation dynamics/localization of lamins A, C, B1 through telophase/G1 | anti-lamin B1 antibody |
- ▲ Genome organization disrupted in lamin C-depleted cells versus baseline control 85% (shC) vs 16% (WT control)
- ▲ Genome organization disruption in cells depleted of both A-type lamins comparable to shC 88% (shAC)
- – Lamin A depletion does not increase disruption above baseline 18% (shA) vs 16% (WT)
- ▼ LAD object volumes skewed toward smaller volumes (LAD dispersion) in shAC and shC p<0.001
- ▼ Ikzf1 LAS I lacO locus is NE-associated in TCIS clones; depleting lamin C reduces this to LAS-less background from 75–80% to ~40% (no-LAS background)
- – Bioinformatically defined LADs preserved across all knockdown conditions by DamID >90% by base coverage
- ▼ Lamin C incorporates into the lamina much more slowly than lamin A (recombinant injection study cited) lamin A 20 min vs lamin C 180 min
- count 85% disrupted nuclei (shC (lamin C-depleted) cells, p<0.001 vs WT; n>200 nuclei per condition)
- count 88% disrupted nuclei (shAC (lamin A/C-depleted) cells, p<0.001 vs WT)
- count 16% disrupted nuclei (wild-type control nuclei baseline)
- count 18% disrupted nuclei (shA (lamin A-depleted) cells, similar to baseline)
- pvalue p<0.001 (LAD object volume dispersion in shAC and shC; n>50 territories per condition)
- count 75–80% NE-associated (Ikzf1 LAS I lacO locus in TCIS clones Y/12 vs 40% with no LAS (n≥50))
- count >90% by base coverage (preservation of WT LADs in all downregulated conditions by DamID-seq)
- other 20 min (lamin A) vs 180 min (lamin C) (lamina incorporation kinetics from cited recombinant protein injection study)
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 genome-wide DamID-seq mapping of lamina-associated domains with single-cell 3D-immunoFISH imaging across shRNA lamin-depletion conditions, and reports results largely as proportions or distributions of imaged nuclei/chromosome territories. Group comparisons against wild-type/control were assessed with t-tests (explicitly named for percent-disrupted nuclei) and unspecified significance tests for LAD object-volume distributions and peripheral-association percentages, with significance reported at threshold p-values (e.g., p<0.001, p≤0.001). Dispersion is shown as standard deviation, and some scoring was performed by two independent blinded observers.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| t-test (type/tails not specified) | Fig 1D, percent of nuclei with disrupted genome organization vs WT control | n>200 nuclei per condition | not stated |
| significance test (not named) for distribution comparison | Fig 1C, distribution of segmented LAD object volumes (violin plots) vs wild-type | n>50 territories per condition | not stated |
| significance test (not named) for proportions | Fig 2B/2C, percent peripheral (NE) association of lacO/LAS loci vs shCtrl, including rescue experiments | n≥50 | not stated |
| genome-wide comparison of DamID-seq log2 ratios (test not named) | Additional file 1: Fig S2/S3, shAC/shA/shB1/shC vs WT LAD positioning and boundaries | — | not stated |
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Percent of disrupted nuclei across conditions was compared to control using t-tests.↳ Could also: Comparisons of proportions could also be analyzed with a chi-square or Fisher's exact test, or logistic regression on the per-nucleus binary outcome. — These approaches are tailored to count/proportion data and naturally account for the binomial nature of 'disrupted vs not,' which can complement a t-test on percentages.
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LAD object-volume distributions were compared between each knockdown and wild-type, and several conditions were each tested against the same control.↳ Could also: A single global test (e.g., Kruskal-Wallis or ANOVA) followed by a post-hoc procedure with family-wise or FDR correction could also be applied. — An omnibus-plus-post-hoc framework would also control error across the family of condition-vs-control comparisons made on the shared dataset.
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LAD object volumes are summarized as distributions in violin plots and compared for significance.↳ Could also: A nonparametric test such as Mann-Whitney U (with reporting of an effect size like the rank-biserial correlation or a median difference) could also be used. — This would also accommodate potentially skewed volume distributions and convey the magnitude of the difference alongside the p-value.
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Dispersion in bar/quantification plots is reported as standard deviation.↳ Could also: A 95% confidence interval (or showing SD alongside the individual data points) could also be presented. — A CI would also directly convey the precision of the estimated proportion/mean, which is often informative for imaging-based readouts.
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Significance is reported using threshold p-values (e.g., p<0.001).↳ Could also: Exact p-values together with effect-size estimates could also be reported. — Exact values and effect sizes would also let readers gauge both statistical and practical magnitude of each comparison.
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Sample sizes are described as counts of imaged nuclei/territories without a formal power analysis, and 'biological vs technical' replication is not specified in the provided text.↳ Could also: A brief statement of the number of independent biological replicates/clones and an a priori or post hoc power consideration could also be included. — Clarifying the replication structure would also help readers interpret the unit of analysis and the generality of the imaging-based estimates.
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.
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Lamin C depletion reduces NE association of the IKZF1 locus in MEFs from 75-80% to ~40%, equivalent to no-LAS background.imaging mouse embryonic fibroblast down 2021×1papers★ This paper is the founder (earliest)
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LAD object volumes are skewed toward smaller sizes in lamin C- and lamin A/C-depleted MEFs, indicating spatial LAD dispersion.imaging mouse embryonic fibroblast down 2021×1papers★ This paper is the founder (earliest)
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Lamin A depletion alone does not increase LAD organization disruption above baseline in MEFs (18% vs 16% control).imaging mouse embryonic fibroblast none 2021×1papers★ This paper is the founder (earliest)
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Lamin C depletion increases nuclear LAD organization disruption in MEFs (85% disrupted nuclei vs 16% control).imaging mouse embryonic fibroblast up 2021×1papers★ This paper is the founder (earliest)
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Lamin C incorporates into the nuclear lamina substantially more slowly than lamin A after mitosis (~180 min vs ~20 min).imaging mouse embryonic fibroblast down 2021×1papers★ This paper is the founder (earliest)
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Bioinformatically defined LAD boundaries are maintained (>90% base coverage) across all lamin knockdown conditions by DamID.other mouse embryonic fibroblast none 2021×1papers★ 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-34775987
Paper: Wong X et al. "Lamin C is required to establish genome organization after mitosis." Genome Biol 2021. PMID 34775987 · PMC8591896 · DOI 10.1186/s13059-021-02516-7.
Pipeline tool (P16, authors' own): LADetector / pyLAD (https://github.com/thereddylab/pyLAD) — a packaged Python+Fortran implementation of the circular-binary-segmentation (CBS / DNAcopy) LAD caller used by the Reddy lab for DamID(-seq) data.
Data accession correction
- BRIEF lists
geo:GSE56990. That accession is cited in the paper — but only for the array probe design ("Data provided Geo GSE56990"), a 2014 genome- tiling-array dataset (LmnB1/Dam log2 ratios). It is not the DamID-seq data. - The primary DamID-seq data generated for this publication is GSE97095
(Data Availability Statement). GSE97095 is a SuperSeries:
- GPL13112 subseries (NEW data for this paper): 6 samples = 3 shRNA-knockdown
DamID-seq conditions in MEFs, each a Dam-only control + Dam-Lamin experiment:
shA= shLmnA knockdown, Dam-LmnB1 (GSM4658418 / GSM4658419)shC= shLmnC knockdown, Dam-LmnB1 (GSM4658420 / GSM4658421)shB1= shLmnB1 knockdown, Dam-LmnA (GSM4658422 / GSM4658423)
- GPL17021 subseries = reanalyzed Harr-2015 chromatin-state MEF data (out of scope for this paper's own pipeline result).
- GPL13112 subseries (NEW data for this paper): 6 samples = 3 shRNA-knockdown
DamID-seq conditions in MEFs, each a Dam-only control + Dam-Lamin experiment:
Each KD sample deposits BOTH the pipeline input and the pipeline output
GSM..._<cond>.bed.gz= per-GATC-fragment log2(Dam-Lamin/Dam) score track (~6.58 M fragments, genome build mm9 — chr1 max 197,195,301 +*_random). This is the input to the segmentation step.GSM..._<cond>_LADs.bed.gz= the authors' final LAD domain calls (output).
This makes a clean same-input/same-tool reproduction possible: feed the deposited
score track into pyLAD's --re option (RE.load_data reads a bed/bedgraph WITH a
score column → segment_data() runs CBS on those scores → find_LADs_from_segmentation
stitches positive segments) and compare our LAD calls to the authors' deposited LADs.
Exact parameters (Methods, "DamID-seq data processing")
- "LADs separated by less than 25 kb were considered to be part of a single LAD"
→ pyLAD
--maxdip 25000(gap-stitch threshold). - "All other parameters were left at default values" → mindip=2000 default, etc.
- "LADs were post-filtered to be greater than 100 kb" → keep LADs with size >100 kb.
In scope (attempted)
- Pipeline reproducibility (C1): run pyLAD LADetector segmentation on each
deposited score track (shA, shC, shB1) with the paper's parameters; compare our
LAD calls to the authors' deposited
_LADs.bed(# LADs, total LAD bp, % genome, reciprocal base-coverage concordance). Same input + same tool → expect near-exact. - Biological claim (C2): ">90% base coverage preservation" of LADs across the three knockdown conditions (Fig S2/S3, Additional file 1) — computed as pairwise base-coverage among the three LAD sets.
Out of scope / not attempted (the hard 20%)
- Full pipeline from raw SRA reads (FASTQ→bowtie1 mm9→count→ratio): the per-fragment score tracks are deposited, so the alignment/scoring stage is taken as given. Re- aligning 2×~70 M reads/condition would test only the upstream stage; noted, not done.
- WT-MEF comparison: WT track is bundled only in GSE97095_RAW.tar (no per-sample supp); pairwise-among-the-3-KDs already tests the "no significant differences / preservation" claim. Cross-vs-WT left as the 20%.
- All imaging / wet-lab / microscopy results (e.g. "85% of lamin C-depleted cells disrupted") — manual/experimental, not pipeline-derived.
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.
Strong reproduction on the authors' side of the ledger. Feeding the deposited per-fragment score tracks (GSE97095) into the shipped pyLAD tool with the paper's exact parameters regenerates the deposited LAD calls at 94-99% reciprocal base coverage, #LADs within ~6% and %genome within ~1% for all three knockdowns — every compared value is derivable from shared data via shared code, with no fabrication signal. The residual deviations are entirely technical (seedless CBS permutation segmentation, unpinned tool version), i.e. on the expected/stochastic side, not the authors' or a methodological defect. The only soft spot is C2: the literal WT-vs-KD '>90% preservation' claim was approximated with a cross-KD pairwise proxy (because the WT-MEF track ships only inside GSE97095_RAW.tar), so it is supportive rather than a clean 1:1 — hence overall yellow rather than green.
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.