Single-cell transcriptome maps of myeloid blood cell lineages in Drosophila.
Part of the results reproduced; minor but material deviations remained.
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- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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.
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- Reproduced
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator headless) · v1.0 · run #1 2026-06-18no human curator yet
- Last updated
- 2026-07-29
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Deep full-text extraction
Model: opusHow Drosophila lymph gland hemocytes develop and are regulated at single-cell resolution is unclear; the authors test whether single-cell RNA sequencing can comprehensively resolve the heterogeneity, developmental trajectories, and immune responses of developing myeloid-like hemocytes and distinguish embryonic- versus lymph gland-derived lineages.
- ★ Single-cell RNA-seq of developing Drosophila lymph glands resolves heterogeneity of hemocytes and identifies major and sub cell types. resource
- ★ Previously undescribed hemocyte types exist, including adipohemocytes, stem-like prohemocytes, and intermediate prohemocytes. finding
- ★ A GST-rich hemocyte cluster and an adipohemocyte cluster (lipid-metabolism/phagocytosis gene-enriched) are present in wild-type lymph glands. finding
- ★ Developmental trajectories of hemocytes can be reconstructed, with PH1 as the start point and emergence of the lamellocyte lineage upon wasp infestation. finding
- ★ Crystal cells and lamellocytes each split into premature (CC1/LM1) and mature (CC2/LM2) states. finding
- SCENIC analysis delineates cell-type-specific transcriptional regulators (e.g., DREAM/Dpp factors in prohemocytes; ecdysone pathway factors in plasmatocytes). mechanism
- New genetic reporter tools/markers were generated and validated (e.g., Ance-MiMiC, dome-LexA; PSC markers Ilp6, tau, mthl7, chrb; crystal cell markers Men, Numb). resource
- ★ Embryonically derived and larval lymph gland hemocytes share similarities and differences. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-seq (Drop-seq) | Drosophila larval lymph gland hemocytes at 72, 96, 120 h AEL | none (normal development) | single-cell transcriptomes; cell-type clustering and proportions | Drop-seq; Seurat3 integration; Scrublet QC |
| single-cell RNA-seq (Drop-seq) | Drosophila lymph gland hemocytes after parasitic wasp infestation | wasp (Leptopilina) infestation / active cellular immunity | emergence of lamellocyte lineage; transcriptomes | Drop-seq |
| bulk RNA-seq | Drosophila wild-type lymph glands | none | validation of gene detection and cluster signature gene expression | — |
| gene regulatory network inference (SCENIC) | lymph gland scRNA-seq dataset | none | transcription factor regulons per cell type | SCENIC |
| trajectory/pseudotime analysis (Monocle3) | lymph gland hemocyte scRNA-seq (PSC excluded) | none | developmental trajectory/pseudotime ordering | Monocle3 |
| fluorescence in situ hybridization / immunostaining (in vivo validation) | Drosophila lymph gland; reporter lines (gal4, MiMiC, LexA, GTRACE) | none | marker gene/protein localization (e.g., CG18547, CG3397, Sirup, Lsd-2, vir-1, Antp) | — |
| lipid droplet staining | Drosophila lymph gland hemocytes (cortical zone) | none | neutral lipid droplet detection in adipohemocytes | BODIPY; Nile Red; Phalloidin |
| DAPI cell counting / clustering (Louvain) | single lymph gland lobe | none | DAPI-positive cell counts and cluster identity | — |
- – 22,645 high-quality cells retained across three timepoints (72h:2321; 96h:9400; 120h:10,924) 22,645 cells
- – Eight major cell types identified including prohemocytes, plasmatocytes, crystal cells, lamellocytes, PSC, GST-rich, adipohemocyte, dorsal vessel PH 36.2%, PM 57.6%, CC 1.3%, LM 1.5%, PSC 0.9%, DV 0.1%, GST-rich 1.0%, adipohemocyte 1.4%
- ▼ At 72h AEL prohemocytes and plasmatocytes are nearly equal; by 120h AEL plasmatocytes dominate and only ~30% retain prohemocyte signature 49.8% PH and 46.1% PM at 72h; ~30% PH at 120h
- – Crystal cells and GST-rich cells first appear at 96h AEL; lamellocytes and adipohemocytes appear at 120h AEL
- – 17 transcriptionally distinct hemocyte subclusters identified (6 PH, 4 PM, 2 LM, 2 CC subclusters) 17 subtypes
- – Crystal cells split into CC1 (early, low lz with MZ/CZ markers) and CC2 (mature, high PPO1/PPO2)
- – Adipohemocytes contain neutral lipid droplets and express Sirup/Lsd-2 within Hml+ cortical zone
- – Cell coverage of one lymph gland lobe was 5.5X, 6.8X, and 2.4X at 72, 96, 120h AEL respectively 5.5X / 6.8X / 2.4X
- count 22,645 cells total (high-quality cells retained after QC)
- count median 6361 transcripts (UMIs) and 1477 genes per cell (per-cell complexity)
- count median 397, 1392, 4557 (DAPI+ cell counts per lobe at 72, 96, 120h AEL (n=30 each))
- count plasmatocytes ~95% of hemocytes (plasmatocyte proportion of total hemocytes (background))
- count crystal cells ~5% (crystal cell proportion of blood population (background))
- count 49.8% and 46.1% (prohemocyte and plasmatocyte proportions at 72h AEL)
- count 14 libraries (5 for 72h, 5 for 96h, 4 for 120h AEL) (independent sequencing libraries)
- count ~30% (fraction retaining prohemocyte signature at 120h AEL)
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.
This study used single-cell RNA sequencing (Drop-seq) of Drosophila lymph gland hemocytes across three developmental timepoints (72, 96, and 120 h AEL), integrating 14 libraries with Seurat3 batch correction and Louvain-based unsupervised clustering. Subclusters were characterised by Wilcoxon Rank-Sum tests for differentially expressed marker genes. Developmental trajectories were reconstructed with Monocle3, and transcriptional regulatory networks were inferred with SCENIC. Results were primarily reported as cell counts, proportions, and median values, with in vivo validation of key marker genes by fluorescence imaging.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon Rank-Sum test | Identification of significant marker genes across 17 hemocyte subclusters (Fig. 2b dot plot) | 22,645 cells across subclusters (cell counts per subcluster listed in Supplementary Table 4) | not stated |
| Louvain community-detection algorithm | Unsupervised clustering of all cells into major cell types and subsequent subclusters (Fig. 1c, Fig. 2a) | 22,645 cells | not stated |
| Scrublet doublet prediction | Quality-control pipeline to remove predicted doublets prior to downstream analysis | — | not stated |
| SCENIC regulon activity scoring | Transcription factor regulatory network inference per cell type (Supplementary Fig. 1k) | 22,645 cells | not stated |
| Monocle3 pseudotime trajectory reconstruction | Developmental ordering of hemocyte subclusters (Supplementary Fig. 3a onward) | 22,645 cells minus PSC | not stated |
| Seurat3 integration / batch correction (canonical correlation + mutual nearest neighbours) | Integration of 14 Drop-seq libraries across timepoints prior to clustering | 14 libraries; 22,645 cells retained post-QC | not stated |
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The Louvain algorithm was used for graph-based clustering of single cells↳ Could also: The Leiden algorithm (Traag et al. 2019) is an alternative graph-based clustering method that avoids the disconnected-community artefact inherent to Louvain and is now the default in many scRNA-seq workflows — Leiden guarantees well-connected communities and is generally considered a methodological refinement of Louvain; reporting which algorithm and resolution parameter was used also aids reproducibility
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t-SNE was used for two-dimensional visualisation of cell type relationships (Figs. 1c, 2a)↳ Could also: UMAP (McInnes et al. 2018) is a widely adopted alternative for single-cell visualisation — UMAP tends to better preserve global structure (inter-cluster distances) while remaining competitive on local structure, and has become the de facto standard in scRNA-seq publications; showing both would allow readers to assess robustness of cluster separation
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Wilcoxon Rank-Sum tests were applied across many genes and all 17 subclusters without a stated multiple-testing correction↳ Could also: Applying Benjamini–Hochberg FDR correction across all gene–subcluster comparisons is a standard companion to Wilcoxon tests in differential-expression analyses (and is an option within Seurat's FindMarkers function) — With thousands of genes tested across 17 groups simultaneously, FDR correction explicitly controls the expected proportion of false discoveries, making the threshold for 'significant' markers more interpretable to readers
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Monocle3 was used to reconstruct developmental pseudotime trajectories↳ Could also: RNA velocity (scVelo; Bergen et al. 2020) or PAGA (Wolf et al. 2019) are complementary trajectory methods; RNA velocity uses spliced/unspliced RNA ratios to infer directionality independently of pseudotime ordering — RNA velocity provides an orthogonal, data-driven estimate of differentiation direction that can corroborate or refine Monocle3 trajectories, particularly for identifying branch points
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Cell count distributions (Fig. 1b) were summarised with median values only↳ Could also: Reporting an IQR or range alongside the median, or showing individual data points as an overlay, would also convey the spread of counts across the n = 30 lymph gland lobes per timepoint — With n = 30 per group, variability metrics help readers judge biological consistency and are useful for power estimation in follow-up experiments
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Batch effects across 14 libraries were corrected using Seurat3's integration (CCA + MNN anchors)↳ Could also: Harmony (Korsunsky et al. 2019) or scVI (Lopez et al. 2018) are alternative batch-correction approaches that operate in latent space and can be applied to the same data — Benchmarking studies suggest that no single integration method outperforms all others across dataset types; cross-method concordance (e.g., consistent cluster assignments after Harmony integration) would strengthen confidence that observed cell types are not integration artefacts
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
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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.
This is a strong, honest reproduction: deterministic outputs (15,540-gene union, 14 libraries, per-cell QC) match 1:1 and the C3 anti-fabrication check passes decisively (deposited matrices regenerate the authors' per-cell metrics exactly, pearson 1.0, max|diff|=0), so there is no fabrication concern. The substantive deviation is the headline cell count (22,645 vs 19,439, with 120h dropping 10,924->7,829) plus minor Fig 1c proportion shifts (within 1-3.5 pts, rank preserved) and subcluster count (17->20). These all trace to the deposit being a later revised re-analysis rather than the published version — an authors'-side versioning/data-availability gap (the original 22,645 set is not recoverable from shared data), not a methodological or computational error on our side. The central conclusion — the 8-cell-type myeloid blood lineage atlas — holds robustly, so overall quality is solid with cleanly explainable deviations.
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