Charting and probing the activity of ADARs in human development and cell-fate specification.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓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
- ✓Overall, the reproduction was clean
- Every checked point held up.
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 1:1. IN SCOPE = Figure 2 Cohen's d effect sizes of the Alu Editing Index (AEI) and ADAR/ADARB1/ADARB2 expression across 3 developmental windows, computed from the repo's shipped derived tables (AEI EditingIndex, ADAR TPM, organ-dev SuppTable). All 12 effect sizes quoted in the Results/Fig.2 reproduce exactly (max |delta d|=0.013; all round to the reported 2-3 sig figs) and 3/3 reported p-values match to 3 sig figs (1.24E-10, 9.94E-5, 2.5E-3). Status is 'partial' because this covers Fig.2 only, not the whole paper. NOTE ON METHOD: the heavy-compute rule routes work to «our HPC», but the Uni-HH VPN 2FA was not completed in time; since this target is trivial compute on KB-scale shipped tables, it was reproduced locally with an INDEPENDENT pure-Python reimplementation of Figure2/F02_03_OrganDev_Cohensd.R (pooled Cohen's d == effsize estimate; Welch t-test, t-CDF self-tested). An independent implementation landing on the authors' exact numbers is strong evidence the values are derivable from deposited data/code (no fabrication concern). NOT ATTEMPTED (hard ~20%): regenerating the AEI table from raw E-MTAB-6814 BAMs via RNAEditingIndexer, ADAR TPM via featureCounts, and the Fig.3-8 scRNA-seq analyses on GSE248941 (GSVA, site-specific editing, ADAR-KO guide calling, WGCNA/EMD, DEG, cell-type composition).
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 100assessed: 2026-06-15 ⛓ e69e36bfc24e
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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
- 2026-06-15
- 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: opusWhat are the spatiotemporal RNA editing profiles and functional roles of ADAR enzymes during early human development and cell-fate specification, a gap left unaddressed by lethal mouse models and adult-tissue-only studies?
- ★ RNA editing (AEI) and ADAR enzyme expression follow tissue-specific temporal dynamics across human organs from fetal to adult stages, with ADARB1 dynamics tracking AEI increase in hindbrain development. finding
- ★ Time-series teratomas faithfully recapitulate fetal developmental transcriptomic and epitranscriptomic trends, establishing the teratoma as a pan-tissue developmental model for RNA editing. resource
- ★ Knocking out ADAR leads to a global decrease in RNA editing across all germ layers in teratomas. finding
- ★ Knocking out ADAR leads to enrichment of adipogenic cells, revealing a role for ADAR in human adipogenesis and potential implication in obesity-related phenotypes. finding
- ★ A pan-tissue, single-cell CRISPR-KO screen of ADARs in teratomas can probe ADAR function across all three germ layers. method
- ★ Prenatal-to-postnatal differential RNA editing converges on innate immunity and DNA replication genes (e.g., EIF2AK2/PKR, MAVS) shared across organs. finding
- Global A-to-I editing (AEI) inversely correlates with viral infection and DNA replication pathway gene scores in forebrain, hindbrain, and liver during the fetal-to-adult transition. mechanism
- Muscle cells exhibit significantly lower AEI than the average teratoma cell type, consistent with adult human tissue reports. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk RNA-seq RNA editing analysis (Alu Editing Index) | Human organs (forebrain, hindbrain, heart, liver, kidney, testis) across developmental stages | none | Global A-to-I editing level (AEI) and ADAR/ADARB1/ADARB2 expression | — |
| Site-specific RNA editing analysis (REDIportal-based pipeline) | Human bulk organ time-series tissues (six organs) | none | Differential editing rate (delta editing) and gene region classification, correlation with gene expression log-fold change | — |
| Single-cell RNA-seq RNA editing analysis (pseudo-bulk AEI) | 8-week hPSC-derived cerebral organoids | none | AEI per cell type and correlation with ADAR/ADARB1 expression | 10X Chromium |
| Single-cell RNA-seq RNA editing analysis (pseudo-bulk AEI) | 8–10-week hPSC-derived teratomas (H1, H9, HUES62, PGP1 lines) | none | AEI per cell type, ADAR expression correlation, cell-type identity | 10X Chromium |
| Bulk RNA-seq RNA editing analysis | Bulk teratoma tissue | none | Editing rate validation vs single-cell | — |
| Single-cell CRISPR-KO screen (pan-tissue) | hPSC-derived teratomas | CRISPR knockout of ADAR genes | Editing levels and cell-type/germ-layer composition (adipogenic enrichment) | — |
| Gene Set Variation Analysis (GSVA) | Human bulk organ time-series tissues | none | Pathway gene scores (Viral Infection, DNA Replication) correlated with AEI | — |
- ▲ Significant rise in forebrain AEI during late gestation to newborn-teenager transition, with decrease in ADAR and increase in ADARB2 Cohen's D = 4.12 (p=1.24E-10)
- ▲ Concordant increases in hindbrain AEI and ADARB1 across development, suggesting ADARB1 drives AEI increase AEI Cohen's D = 1.50 (p=7.72E-04); ADARB1 Cohen's D = 2.04 (p=1.17E-06)
- ▼ Robust testis AEI reduction during newborn-teenager to adult transition concordant with ADAR expression decrease AEI Cohen's D = -5.21 (p=9.94E-05); ADAR Cohen's D = -2.19
- – 58 prenatal vs postnatal differentially edited sites shared across all organ datasets, enriched for innate immunity and DNA replication (EIF2AK2/PKR, MAVS) 58 sites
- ▼ Slightly negative correlation between change in editing levels and gene expression across differentially edited genes R = -0.133
- – ADAR and ADARB1 expression explain 31% and 10% of variance in teratoma cell-type AEI respectively 31% and 10% variance
- ▼ Muscle cells show significantly lower AEI than average teratoma AEI
- ▲ Liver shows robust AEI increase during late gestation to newborn-teenager transition Cohen's D = 6.00 (p=3.28E-05)
- pvalue p=1.24E-10, Cohen's D = 4.12 (Forebrain AEI rise, late gestation to newborn-teenager)
- pvalue p=9.94E-05, Cohen's D = -5.21 (Testis AEI reduction, newborn-teenager to adult)
- pvalue p=3.28E-05, Cohen's D = 6.00 (Liver AEI increase, late gestation to newborn-teenager)
- correlation R = -0.133 (Editing change vs gene expression log-fold change across organs)
- other 31% and 10% variance explained (ADAR and ADARB1 expression vs teratoma cell-type AEI (n=4 WT teratomas))
- count 58 shared differentially edited sites (Prenatal vs postnatal sites shared across all organ datasets)
- pvalue p=1.17E-06, Cohen's D = 2.04 (Hindbrain ADARB1 increase, early-to-late gestation)
- count 3 cerebral organoids; 4 WT teratomas; over 20 distinct cell-types (Sample sizes for single-cell editing analyses)
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 paper analyzes A-to-I RNA editing dynamics across human development using both bulk organ time-series data and hPSC-derived single-cell models (cerebral organoids and teratomas). Global editing levels are quantified via the Alu Editing Index (AEI), and temporal shifts across four developmental stages are compared using unpaired two-tailed t-tests with Cohen's d reported throughout. Pearson correlations characterize relationships between AEI and ADAR expression or pathway gene scores, and Gene Set Variation Analysis (GSVA) translates bulk expression profiles into pathway-level scores. Pseudo-bulk aggregation of single-cell data is used to compute per-cell-type AEI values before statistical comparison.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| unpaired two-tailed t-test | AEI comparisons across teratoma cell types (Fig. 4f) | n=4 WT teratomas (pseudo-bulk per cell type) | not stated |
| p-values with Cohen's d effect sizes (test family not explicitly named) | Sequential developmental stage comparisons of AEI and ADAR/ADARB1/ADARB2 expression in bulk organs (Fig. 2b–h) | derived from public time-series database; exact per-comparison n not stated | not stated |
| Pearson correlation coefficient | AEI vs. ADAR and ADARB1 expression per cell type (Fig. 4d, 4g); AEI vs. Viral Infection and DNA Replication gene set scores across developmental time (Fig. 3e–j) | number of cell types or time-point samples; exact n not stated per figure | not stated |
| Gene Set Variation Analysis (GSVA) | Pathway gene scores (Viral Infection, DNA Replication) over developmental time in bulk organs (Fig. 3e–j) | bulk organ samples per developmental stage; exact n not stated | na |
| differential editing analysis (customized R scripts, methodology not further specified) | Prenatal vs. postnatal site-specific editing levels across all bulk organs (Fig. 3a–d, Supplementary Fig. 1C) | prenatal and postnatal sample groups; exact n not stated | not stated |
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Multiple pairwise t-tests were used to compare AEI and ADAR expression across four sequential developmental stage transitions within each of six organ types, yielding a large family of simultaneous comparisons↳ Could also: A one-way or two-way ANOVA (organ × stage) followed by a post-hoc correction such as Tukey HSD or Benjamini-Hochberg FDR could also be applied across the same data — A single omnibus model with a post-hoc correction would explicitly account for the family-wise error rate across the many stage-by-organ combinations tested, and is a standard approach when the same hypothesis is tested repeatedly across related groups
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Effect sizes and spread for teratoma cell-type AEI comparisons are reported as SEM on a small number of biological replicates (n=4)↳ Could also: SD or a 95% bootstrap confidence interval could also be used to summarize spread — For small n, SEM compresses apparent variability because it scales with 1/√n; SD or a CI conveys the actual biological spread of the data and is often recommended for small-sample descriptive reporting
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Pearson correlation was used to relate AEI to ADAR/ADARB1 expression across cell types and to relate AEI to pathway gene scores across developmental time points↳ Could also: Spearman rank correlation could also be used for the same relationships — Pearson r assumes linearity and approximate normality; Spearman is distribution-free and robust to outliers or monotone-but-nonlinear associations, which may be relevant given the small number of cell-type or time-point observations per organ
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Pseudo-bulk AEI values were computed by pooling all cells of a given type from replicate samples before statistical comparison↳ Could also: A mixed-effects model treating sample identity as a random effect could also be applied directly to per-cell or per-sample AEI estimates — A mixed-effects framework explicitly models the nested structure (cells within samples) and propagates within-sample variability, complementing the pseudo-bulk approach and potentially providing better-calibrated uncertainty estimates with small numbers of biological replicates
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Global A-to-I editing was summarized by the Alu Editing Index (AEI) as a single scalar per sample or cell type↳ Could also: A site-specific differential editing analysis (e.g., using the Fisher's exact test or beta-binomial models as implemented in tools such as SAILOR or QNB) applied at the single-cell pseudo-bulk level could also complement the AEI — The AEI captures average global editing but cannot distinguish which individual sites drive stage- or cell-type-specific changes; site-level analysis would add resolution to identify biologically specific editing events alongside the aggregate metric
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The differential editing pipeline identified prenatal-vs-postnatal differentially edited sites using customized R scripts with methodology described as analogous to established methods↳ Could also: Established dedicated tools such as RADAR, DESeq2 applied to editing counts, or a beta-binomial regression framework could also be used for differential editing analysis — Dedicated tools provide explicit statistical models for the count-ratio nature of editing data, built-in multiple-testing correction, and documented assumptions, which can facilitate reproducibility and comparison with other studies
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-39537590
Paper: Dailamy, Lyu et al. "Charting and probing the activity of ADARs in human
development and cell-fate specification." Nat Commun 2024. DOI 10.1038/s41467-024-53973-0.
Code: https://github.com/SammiLyu/scScreens_ADARs @ commit 0ad2e54a7d0e7a526b3930e0ccdef5a56511fc39 (HEAD, 2024-10-01).
Data: GEO GSE248941 (the study's own scRNA-seq: teratoma, cortical organoid, H1
timeseries, ADAR-KO). The human organ-development bulk RNA-seq is EXTERNAL:
ArrayExpress E-MTAB-6814 (Cardoso-Moreira et al., Nature 2019).
How the repo is organized
Scripts are grouped by figure (Figure2/ … Figure8/, Supp/). The heavy upstream
(cellranger / featureCounts / samtools / RNAEditingIndexer) was already run by the
authors, and the derived output tables are shipped in the repo (small, KB-scale):
Figure2/ADARexp_OrganDev.txt— ADAR/ADARB1/ADARB2 TPM per organ-dev sample (313 samples × 3 genes).Figure2/AEI_OrganDev_EditingIndex.csv— RNAEditingIndexer output, A2GEditingIndex (Alu Editing Index) per sample (307 samples).Figure2/OrganDevelopment_Human_SuppTable.csv— sample metadata (library ID, Organ, Developmental stage, Sex). The downstream R statistical analyses consume these shipped tables and produce the reported figure quantities.
IN SCOPE (pipeline-derived, deterministic, reproduced)
RU-claim group C1 — Figure 2 Cohen's d effect sizes of AEI & ADAR expression across
developmental time (script Figure2/F02_03_OrganDev_Cohensd.R).
- Pipeline: shipped AEI + log2(ADAR TPM) per sample → group by organ × {early,late}
developmental window →
effsize::cohen.d(late, early, pooled=T, na.rm=T)effect size, 95% CI, Welch t-test p-value. Three windows:- W1 early→late gestation (4–10 wpc vs 11–20 wpc)
- W2 late gestation→newborn-teenager (11–20 wpc vs newborn–oldTeenager)
- W3 newborn-teenager→adult (newborn–youngTeenager vs oldTeenager–senior)
- Fully deterministic given the shipped inputs; tiny env (R + effsize). This directly regenerates the numbers quoted for Fig. 2b–g.
- Reported anchors (paper Results / Fig. 2): Forebrain W2 AEI d=4.12 (p=1.24e-10), ADAR1 d=−1.35 (p=2.54e-3); Hindbrain W1 AEI d=1.50, ADARB1 d=2.04; Hindbrain W2 AEI d=1.42, ADARB1 d=0.99; Heart W1 AEI d=−1.03, W2 AEI d=1.43; Liver W1 AEI d=−1.11, W2 AEI d=6.00, W3 AEI d=1.26; Testis W3 AEI d=−5.21 (p=9.94e-5).
OUT OF SCOPE (not attempted — why)
- Regenerating the AEI table from raw reads (
F02_02_OrganDev_AEI.sh): requires the full E-MTAB-6814 BAMs (~TB), the RNAEditingIndexer tool + hg19 genome/Alu/SNP/RefSeq resources, and days of compute. The shippedAEI_OrganDev_EditingIndex.csvis the output of exactly this step; we take it as given (per P16, reproducing the downstream pipeline on the authors' shipped intermediate is valid). - Regenerating ADAR TPM (
F02_01,fc_OD_v1/*): needs featureCounts over the same BAMs; shippedADARexp_OrganDev.txtis taken as given. - scRNA-seq analyses (Fig 3–8): GSVA, site-specific editing (Breen Quantify-RNA-editing), label transfer, ADAR-KO guide calling, WGCNA/EMD, DEG/volcano, cell-type composition. These need the GSE248941 raw/processed scRNA objects (large), cellranger, Seurat, and in several cases proprietary inputs (F07_05 .xlsx fitness). Deferred as the hard ~20%.
- Wet-lab / manual results (perturbation phenotypes, microscopy): out of scope by design.
Reproduction plan
Clone repo on «infra» («our HPC»), build a minimal conda R env (r-base r-effsize r-dplyr r-stringr r-jsonlite), run a faithful port of the F02_03 computation against the three
shipped tables, emit the three Cohen's d tables as CSV/JSON, pull back to «host», compare
the 12 anchored cells to the paper.
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Reproduction footprint
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