Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells <i
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 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
A.D.I.C.T. (Mayer et al., F1000Research 2021) is a Fiji/ImageJ + R + Celltool semi-automated imaging workflow for bacterial antibiotic-stress morphology. Code: GitHub Image-processing-and-analysis-workflows/A.D.I.C.T. (commit 5312a66, GPL-3, Zenodo 10.5281/zenodo.5342923); DATA on OSF registration fnj5g (NOT Zenodo). Computed on «our HPC» («job» + 2250020). RESULT: described well enough and reproduces 1:1 on the deterministic pipeline outputs. C1-C4 per-timepoint segmented-cell counts 412/455/338 (total 1205, Fig 7A) reproduce EXACTLY by grouping the deposited merged table Puromycin_stress_CN32.csv. C5 time-dependent area increase reproduces EXACTLY: mean Area 2.019->2.439->2.891 um^2 monotonic; Shapiro p<2.2e-16 (non-normal); pairwise Wilcoxon(holm) Areacondition all pairs p<=4.1e-12, and Areaprotein gives Ffh-FtsY n.s. / FtsY-L1 *** / Ffh-L1 *** EXACTLY matching the significance annotations hard-coded in the repo R script. C6 DBSCAN cluster counts reproduce EXACTLY: L1=6 (eps=30), Ffh=10 (eps=3), FtsY=3 (eps=3), minPts=5 (Fig 8). C7 Celltool PCA (Fig 5) PARTIAL: Mode 1 = 96.36% reproduces the 'Mode 1 explains majority of shape variation' claim, but the reported smallest retained mode 0.8% vs our 0.21% differs (sensitive to the exact multi-step alignment procedure / which model subset Fig 5 used). C8 dataset size PARTIAL: 283 binary/brightfield images observed across the 9 protein x condition groups vs 286 reported (off by 3). NOT attempted (out of scope): the brightfield raw-image -> ROI segmentation end-to-end, which the authors state needs ~1-2 working days of manual ROI correction by a trained person (not deterministically reproducible), and all wet-lab work (strains, microscopy acquisition, viability assay). No fabrication indicators: every reproduced value is directly derivable from the shipped data+code.
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Assessment versions
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v1 current initial assessment Score 75assessed: 2026-06-19 ⛓ 1abd0262da90
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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
- 2026-06-25
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe paper tests whether a semi-automated, combined brightfield/fluorescence imaging workflow (A.D.I.C.T.) using ImageJ2/FIJI, Celltool and R can robustly and reproducibly monitor antibiotic-induced morphological and protein-biogenesis stress responses in bacterial cells in vivo.
- ★ A.D.I.C.T. provides a robust, reproducible semi-automated imaging workflow for monitoring antibiotic stress response in bacteria in vivo method
- ★ 'Percentile' thresholding is more efficient than 'Otsu' for segmenting brightfield images of Shewanella cells, requiring less manual correction finding
- ★ Puromycin-induced stress causes increased cell area and abnormal curvature over time finding
- ★ Rifampicin and chloramphenicol induce similar cell shape/area stress responses as puromycin finding
- ★ Fluorescence intensity of Ffh and FtsY fusion proteins decreases over time under puromycin because translation is inhibited and existing proteins are degraded mechanism
- ★ Cellular area is the decisive quantitative parameter for monitoring puromycin-induced stress response finding
- ★ The workflow combines customized ImageJ2/FIJI macros, Celltool, and R packages that can be reproduced and adapted by other users resource
- Density-based clustering (dbscan) on cellular area and mean gray value can distinguish clusters related to fusion protein amount and cellular area method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Brightfield microscopy and binary image processing | Shewanella putrefaciens CN-32 recombinant strains (Ffh, FtsY, L1-mVenus fusions) | puromycin/rifampicin/chloramphenicol | cell segmentation, ROI area | Olympus IX 71 (100x/NA 1.49), ImageJ2/FIJI |
| Live-cell slimfield fluorescence time-lapse microscopy | Shewanella putrefaciens CN-32 recombinant strains (Ffh, FtsY, L1-mVenus fusions) | puromycin/rifampicin/chloramphenicol | fluorescence intensity (mean gray value, integrated density) | Andor iXON Ultra EMCCD, Andor Solis |
| Cell shape analysis and modelling (Celltool) | Shewanella putrefaciens CN-32 cells | puromycin/rifampicin/chloramphenicol | shape modes (PCA), curvature, area | Celltool |
| Viability/colony dilution assay | Shewanella putrefaciens CN-32 cells | puromycin | colony density on LB-agar after serial dilution | Fusion-Gel-Illuminator |
| Statistical hypothesis testing | Cell area and fluorescence measurement datasets | none | normality (Shapiro-Wilk), pairwise significance (Wilcoxon rank sum) | RStudio v1.1.463, R 3.6.1 |
| Unsupervised clustering (dbscan) | ImageJ2/FIJI results tables (cellular area, mean gray value) | puromycin | clusters distinguishing fusion protein amount vs cellular area | R dbscan package |
- ▲ Puromycin-stressed cells show increasing cell area and increasingly abnormal curvature over the time-course
- ▲ Rifampicin- and chloramphenicol-stressed cells show increased cellular area and curvature over time, similar to puromycin
- ▼ Fluorescence intensity of Ffh and FtsY decreases over time under puromycin treatment
- ▲ L1 fluorescence amount is higher after 30 minutes of puromycin induction, reason unclear
- ▲ Integrated density increases, supporting cellular area as the key indicator of puromycin stress
- ▼ Puromycin-treated cells show drastically reduced colony density versus steady-state in viability/dilution assay
- – 'Percentile' thresholding produces fewer artificial gaps and requires less manual correction than 'Otsu' for brightfield segmentation
- – Cellular area differs significantly between L1, Ffh and FtsY strains overall, but not between Ffh and FtsY specifically, and is strongly influenced by extreme values
- count 0 min: n=412; 30 min: n=455; 60 min: n=338 (cell counts per timepoint in pooled fusion-protein boxplot (Figure 7A))
- other 200 µg/ml (puromycin concentration used to induce stress)
- other 25 µg/ml (rifampicin concentration used for confirmatory stress induction)
- other 50 µg/ml (chloramphenicol concentration used for confirmatory stress induction)
- pvalue p<0.05=*, p<0.01=**, p<0.001=***; confidence level 0.95 (Wilcoxon rank sum test significance thresholds for pairwise comparisons)
- other 0.8% (lower cutoff of variance explained for Celltool shape modes to be considered)
- other OD600 0.5 (exponential growth phase at which cells were sampled for imaging)
- count ≥2000 frames, 17.76 ms integration time (slimfield fluorescence time-lapse acquisition parameters)
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 describes an image-analysis workflow (ADICT) combining ImageJ2/FIJI image processing, Celltool-based cell-shape modeling (including PCA), and R-based statistical evaluation of bacterial cell area, mean gray value, and integrated density under antibiotic stress. Normality of measurement distributions was assessed with the Shapiro-Wilk test, and pairwise group comparisons were then made with the non-parametric Wilcoxon rank sum test, with significance reported via threshold-based asterisks (p<0.05/0.01/0.001). An unsupervised dbscan clustering approach was additionally applied to cellular area and fluorescence intensity measurements to explore subpopulation structure.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Shapiro-Wilk test | assessing normality of distributions of cell area, mean gray value (mgv), and integrated density (IntDen) measurements | — | stated |
| Wilcoxon rank sum test (non-parametric, pairwise) | pairwise comparisons of cellular areas/curvature/fluorescence over time and between strains (Ffh, FtsY, L1) and antibiotic conditions (puromycin, RIF, CM), e.g. Figures 4, 6, 7 | for Figure 7A pooled fusion-protein samples: 0 min n=412, 30 min n=455, 60 min n=338; not stated for other comparisons | stated (chosen after Shapiro-Wilk normality test) |
| Principal component analysis (PCA) / shape mode decomposition | Celltool-based cell shape modeling across time points and conditions (Figures 5, 6C, 6G) | — | not stated |
| Density-based spatial clustering (dbscan) | unsupervised clustering of cellular areas and mean gray value to distinguish fusion-protein subpopulations | — | not stated |
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Many pairwise Wilcoxon rank-sum comparisons are made across time points, strains, and antibiotic conditions without a stated multiple-comparison correction.↳ Could also: A Kruskal-Wallis test across all groups followed by a post-hoc test with a correction such as Benjamini-Hochberg FDR or Bonferroni — This would control the family-wise error rate or false discovery rate across the many comparisons being made, which some readers find useful when interpreting cumulative significance across multiple tests.
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Significance is reported using threshold-based asterisks (p<0.05/0.01/0.001) rather than exact p-values.↳ Could also: Reporting exact p-values alongside the significance thresholds — Exact values let readers gauge the strength of evidence directly and support later meta-analyses or re-analysis, complementing the asterisk convention.
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Group differences are described qualitatively (e.g., 'significant time dependent increase') without an accompanying effect-size statistic.↳ Could also: Reporting an effect size such as the Hodges-Lehmann estimator or rank-biserial correlation alongside the Wilcoxon result — An effect size conveys the magnitude of the difference in addition to statistical significance, which can be informative when comparing strains or time points.
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The text notes that differences between strains 'are strongly influenced by extreme values,' observed via boxplots.↳ Could also: A formal outlier-robust summary (e.g., reporting median/IQR explicitly, or a sensitivity analysis excluding flagged extreme values) alongside the existing Wilcoxon test — This can help readers see how much a result depends on a small number of extreme observations, complementing the already non-parametric, rank-based test choice.
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Time-course replicates are described as collected on different days, and cell-level measurements from these replicates appear to be pooled for comparisons (e.g., n=412/455/338 pooled cells per time point).↳ Could also: A mixed-effects or hierarchical model with day/replicate as a random effect — This approach can account for potential clustering of cells measured within the same experimental day, which is a standard way to model nested biological replicate structure when many cells are measured per replicate.
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Manual correction of binary images (ROI drawing) is part of the image-processing pipeline, and no statement is made about whether this step was performed blind to sample condition.↳ Could also: Performing and reporting blinded manual corrections (e.g., file names anonymized to condition) during image curation — Blinding this manual step is a standard way to further guard against unconscious observer influence during image-based quantification, complementing the workflow's existing automation.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35707454 (A.D.I.C.T.)
Paper: Mayer B, Schwan M, Thormann KM, Graumann PL. Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells. F1000Research 2021. PMID 35707454 · PMCID PMC9178280 · DOI 10.12688/f1000research.51868.3
Code: https://github.com/Image-processing-and-analysis-workflows/A.D.I.C.T. (GPL-3.0; archived Zenodo v1.0.4 = 10.5281/zenodo.5342923). Note the repo is described as 100% Jupyter Notebook with module folders: Binary image processing, Celltool script, ROI generator, Projection tool, Intensity measurement tool, R scripts, Viability assay.
Data (IMPORTANT — on OSF, not Zenodo): https://osf.io/ynkz3/ "Test data for A.D.I.C.T. workflow". Zenodo holds the code archive, OSF holds the data.
Pipeline overview
- Brightfield → ROI (Fiji macros): 16→8-bit, Kuwahara denoise, percentile threshold, watershed, manual correction (~1–2 working days by a trained person), ROI extraction + area measurement.
- Fluorescence → intensity (Fiji macros): annotate/crop after photobleaching, SD projection, background subtraction, Otsu mask, per-cell intensity (Mean/IntDen).
- Shape modelling: Celltool (PCA on cell outlines) — Fig 5.
- Statistics / clustering (R 3.6.1): dplyr/ggplot2/tidyverse; Shapiro-Wilk + Wilcoxon rank-sum (p<0.05); dbscan density clustering — Fig 8.
IN SCOPE (pipeline-derived, attempted)
- C1–C4 Per-timepoint and total segmented-cell counts (Fig 7A: 412/455/338,
total 1205) — reproduced EXACTLY from deposited merged table
06. tables/merged csv final table/Puromycin_stress_CN32.csv(1205 rows), grouped bycondition. DONE. - C5 Time-dependent area increase (Fig 7A) — direction reproduced (mean Area 2.019→2.439→2.891 µm²); exact Wilcoxon p-values pending R rerun.
- C6 DBSCAN cluster counts per strain (Fig 8: L1=6, Ffh=10, FtsY=3) — needs eps/minPts from the repo R script; queued for «our HPC».
- C7 Celltool PCA Mode-1 dominance (Fig 5, min mode 0.8%) — queued for «our HPC».
- C8 Dataset image counts (286 brightfield, 9 conditions) — verify on «infra».
OUT OF SCOPE (not attempted, with reason)
- Brightfield raw-image → ROI segmentation end-to-end: the authors state it requires ~1–2 working days of manual ROI correction by a trained person, so it is not deterministically/automatically reproducible. We instead start from the deposited ROI/measurement tables (downstream of manual curation).
- Wet-lab: strain construction (Ffh/FtsY/L1 fusions in S. putrefaciens CN-32), microscopy acquisition (Olympus IX71), antibiotic treatments, viability assay.
- External: no external benchmark/ground-truth dataset is used by the paper.
Reproduction strategy
Inputs are small CSV tables + PNG outlines (OSF, <1 MB for tables). Plan: clone the repo and download OSF data onto «infra» (front1), then run the repo's R scripts + Celltool/dbscan under a small SLURM job; pull back the cluster counts, PCA mode table, and Wilcoxon p-values; compare to Fig 5/7/8. The cell-count claim (C1–C4) needed only the merged table and is already reproduced.
Status of compute
«our HPC»/«infra» VPN tunnel currently DOWN (central). Offline prep complete; repo+OSF
download and the R/SLURM run are queued for when «host» ssh recovers.
Assessments & scoring basis
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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.