Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics.
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
- ✓Overall, the reproduction was clean
- 🟡A deviation arose in the data or preprocessing
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
REPRODUCED 1:1. Applied the repo's shipped figGen R pipeline (SlavovLab/pSCoPE @27d5e7d) to its own Zenodo figure-generating data (record 7455405, sha256 8571b1b7...) on «our HPC» compute nodes (R 4.3.3). 9/10 quantitative pipeline-derived claims reproduced exactly or within rounding: Fig1b consistency 18->59%, Fig2a proteins/cell +106% (1431 vs 694) and peptides/run +103% (5549 vs 2729) and MS2-assigned 83.6%, Fig2c/d completeness +171% (peptides) and 93%/+34% (proteins), Fig3b spike-in slope 1.06/R^2 0.97, Fig4 BMDM 1123 proteins x 373 single macrophages. Only divergence: Fig2b median precursor-intensity fold = 2.79 reproduced vs 2.5 reported (partial, same direction; likely rounding/wording). Not attempted: wet-lab MS acquisition + MaxQuant raw search (out of scope, raw spectra not in deposit) and interpretive GO/PSEA biology chunks. No fabrication indicators - every checked value is derivable from shipped data.
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.
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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
- 2026-06-30
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-30no 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 multi-tiered 'prioritized' precursor-selection strategy (pSCoPE) can simultaneously increase the consistency, sensitivity, depth, and proteome coverage of single-cell proteomics beyond conventional topN shotgun MS, and whether this enables resolving continuous (rather than dichotomous) polarization states in primary macrophages.
- ★ pSCoPE (prioritized precursor selection via MaxQuant.Live) increases sensitivity, data completeness, and proteome coverage more than twofold over shotgun single-cell proteomics finding
- ★ Prioritization introduces priority levels that define the temporal order of peptide MS2 analysis, favoring high-priority precursors over the topN abundance-based heuristic method
- ★ Within LPS-treated and untreated macrophages, proteins covary in functional sets (e.g., phagosome maturation, proton transport) similarly across both conditions, linked to phenotypic variability in endocytic activity finding
- ★ pSCoPE enables quantification of proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition finding
- ★ pSCoPE quantification shows good accuracy and precision against known spike-in peptide standards across a 16-fold dynamic range finding
- Prioritization outperforms isobaric match between runs (iMBR) for recovering missing data, and unlike iMBR can be targeted to biologically selected peptides finding
- pSCoPE is freely available software, widely applicable for analyzing proteins of interest without sacrificing proteome coverage resource
- ★ Single-cell-derived LPS/untreated fold changes correlate positively with bulk-sample fold changes, supporting measurement accuracy finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Prioritized vs non-prioritized LC-MS/MS benchmarking (MaxQuant.Live real-time control) | bulk sample diluted to single-cell levels | none (prioritization on/off) | peptide/protein identification consistency, data completeness, MS2 analysis rate | MaxQuant.Live |
| Single-cell proteomics (shotgun vs pSCoPE), nPOP sample prep | HEK293 and melanoma single cells | none (cell type comparison) | MS2 spectra assigned to peptide, peptides/proteins quantified per cell, precursor intensity/dynamic range | 60-min active gradient LC-MS, 0.5-Th isolation windows |
| Spike-in peptide quantification accuracy assay | single-cell pSCoPE sets (BMDM background) | spiked-in peptide standards at known concentrations (5 levels, 16-fold range) | measured vs mixing ratio (regression slope, R2) | pSCoPE/MS |
| Single-cell proteomics (SCoPE2 then pSCoPE) | murine bone marrow-derived macrophages (BMDMs) | LPS treatment (24 h) vs untreated | protein abundance, PCA clustering, protein covariation (proton transport, phagosome maturation, IFN signaling) | nPOP + MS (SCoPE2/pSCoPE) |
| Bulk proteomic comparison | bulk macrophage samples (M-CSF differentiated) | LPS treatment vs untreated | protein fold change (LPS/untreated), projected onto single-cell PCA space | MS (bulk) |
- ▲ Data completeness for high-priority group of 4,000 peptides increased to 72% with prioritization vs 49% without
- ▲ Fraction of peptides identified in 100% of six runs at 1% FDR rose from 18% (no prioritization) to 59% (prioritization) 228% increased consistency
- ▲ pSCoPE increased fraction of MS2 spectra assigned to a confident peptide sequence to 84%, more than double shotgun over twofold
- ▲ Number of unique peptides per run and quantified proteins per single cell increased with pSCoPE vs shotgun 103% (peptides), 106% (proteins)
- ▼ Median precursor intensity of peptides quantified by pSCoPE was lower than shotgun, indicating wider dynamic range 2.5-fold lower
- ▲ Spike-in measured abundances showed linear dependence on spike-in levels slope=1.06, R2=0.97
- ▲ Single-cell and bulk LPS/untreated fold change estimates correlated positively Spearman ρ=0.91, P=2×10^-11
- – Proton-transport proteins covaried within a treatment condition despite not changing across conditions ρ=0.5, P<10^-12
- correlation ρ = 0.91, P = 2 × 10−11 (correlation of LPS/untreated protein fold changes between single cells and bulk samples)
- correlation ρ = 0.5, P < 10−12 (covariation of proton-transport proteins within a treatment condition)
- fold_change twofold increase in sensitivity, data completeness and proteome coverage (pSCoPE vs shotgun overall performance)
- fold_change 2.5-fold lower median precursor intensity (pSCoPE vs shotgun median precursor abundance)
- other regression slope = 1.06, R2 = 0.97 (spike-in peptide quantification accuracy)
- count 1,123 proteins across 373 single primary macrophage cells (pSCoPE macrophage dataset scale)
- mean 93% protein-level data completeness (34% gain over shotgun) (pSCoPE data completeness at protein level)
- count 97% of 4,000 high-priority peptides sent for MS2 analysis (prioritization efficiency for top priority tier)
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 is primarily a methods-development study comparing a new mass-spectrometry acquisition strategy (pSCoPE) to standard shotgun proteomics, and secondarily an application of that method to macrophage biology. Results are reported mainly through descriptive summaries (box plots with median/IQR, or mean/median with s.d. error bars) across replicate experiments and single cells, together with Spearman correlations and a linear regression to assess quantitative accuracy of spike-in standards. Multiplicity of correlations/covariation analyses is addressed via q-values, and peptide/protein identifications are filtered at a stated 1% FDR.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Simple linear regression (slope and R^2) | Fig. 3b, agreement between spike-in mixing ratios and measured reporter-ion intensities | eight prioritized single-cell experiments (pSCoPE sets), five spike-in levels | not stated |
| Spearman rank correlation | Fig. 4c, correlation between LPS-treated/untreated protein fold changes estimated from single cells vs. bulk samples | 28 proteins | not stated |
| Spearman rank correlation | Fig. 4d, covariation among proton-transport (V-ATPase) proteins within a treatment condition | 177 single cells | not stated |
| Descriptive box-plot summaries (median, 25th/75th percentile, 1.5xIQR whiskers) | Figs. 1b, 1c, 2a comparing prioritized vs. shotgun/non-prioritized acquisition | six experiments (Fig. 1b/c); eight experiments or 97 single cells (Fig. 2a) | na |
| Principal component analysis (PCA) with protein set enrichment analysis (PSEA) | Figs. 2e and 4a/4b, clustering of single cells by cell type or treatment and identification of enriched protein sets along PCs | 97, 194/206, and 373 single cells depending on panel | not stated |
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Comparisons between prioritized and shotgun/non-prioritized acquisition (Figs. 1b, 1c, 2a) are shown as box-plot summaries without an accompanying inferential test statistic or p-value.↳ Could also: A paired or unpaired nonparametric test (e.g., Wilcoxon signed-rank or Mann-Whitney U) or a mixed-effects model accounting for experiment-level clustering — This would let readers see, alongside the descriptive box plots, a formal estimate of how unlikely the observed difference in medians would be under a null of no difference, and a mixed-effects approach would also account for repeated measurements coming from the same experiments.
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Dispersion is reported as s.d. in some figures (Figs. 2b, 3b) and as IQR via box-plot whiskers in others (Figs. 1b, 1c, 2a).↳ Could also: A single consistent dispersion measure throughout, such as a 95% confidence interval or IQR everywhere — A uniform reporting convention (e.g., CIs) directly conveys the precision of an estimate and its likely range for replication, and can make cross-figure comparisons more direct for readers.
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Multiple correlation/covariation analyses (Fig. 4c, 4d, and related supplementary figures) are each associated with a q value, described as an FDR-type control.↳ Could also: An explicitly named and cited multiple-testing procedure, such as Benjamini-Hochberg FDR or Bonferroni correction, applied across the full family of correlation tests performed — Naming the specific method and the exact family of tests it was applied to would let readers reproduce the multiplicity adjustment and assess its scope.
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Quantitative accuracy of pSCoPE was assessed via a linear regression of measured vs. spike-in abundance (slope = 1.06, R^2 = 0.97), without reported uncertainty on the slope estimate.↳ Could also: Reporting a confidence interval around the regression slope/intercept, or using a Deming/orthogonal regression given that both axes carry measurement error — A CI on the slope would quantify how precisely the 1:1 relationship is estimated, and an errors-in-variables regression is often preferred when both the reference and the measured values are subject to experimental noise.
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Cluster separation by cell type or treatment condition (Figs. 2e, 4a) is shown via PCA visualization and projected bulk samples, without a formal statistical test of group separation.↳ Could also: A permutation-based multivariate test such as PERMANOVA, or a classification-based cross-validation accuracy, applied to the PCA or full proteomic profile — This would provide a quantitative, testable statement of how distinguishable the groups are in the high-dimensional protein space, complementing the visual PCA clustering.
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Associations between single-cell and bulk fold-change estimates, and between covarying proteins, are summarized using Spearman correlation only.↳ Could also: Reporting Pearson correlation alongside Spearman (when linearity is of interest), or a robust/percentage-bend correlation for additional sensitivity to outliers — Reporting both rank-based and linear correlation coefficients can clarify whether the relationship is monotonic versus linear, which is useful when readers want to model the reported fold-change relationship directly.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37012480 (pSCoPE, Nat Methods 2023)
Title: Prioritized mass spectrometry increases the depth, sensitivity and data
completeness of single-cell proteomics.
PMID 37012480 · PMCID PMC10172113 · DOI 10.1038/s41592-023-01830-1
Code: https://github.com/SlavovLab/pSCoPE (commit 27d5e7d9ae581c0d54cc851826151efc17d0094d, 2024-12-07)
Data (as cited): zenodo 10.5281/zenodo.7498141 (brief) / badge 10.5281/zenodo.7498171 (README)
Data ACTUALLY downloaded by the code: https://zenodo.org/record/7455405/files/pSCoPE_figureGeneratingData.zip
(1,086,912,266 bytes; this is the URL hard-coded in codeBlocks/pSCoPE_figGen_Zenodo_v1.rmd).
The three Zenodo IDs are versions of the same concept-record; the figure-generating data is record 7455405.
How the paper's computational results are produced
The repository's figure-generation notebook
codeBlocks/pSCoPE_figGen_Zenodo_v1.rmd (R, ~3,800 lines) downloads
pSCoPE_figureGeneratingData.zip from Zenodo, unzips it to datIn/, and
regenerates every main + extended-data figure from already-search-engine-processed
MaxQuant output tables (evidence.txt, msms.txt, msmsScans.txt, peptide/protein
matrices) plus MaxQuant.Live instrument log files. The heavy wet-lab + raw-MS
search steps (MaxQuant.Live acquisition on a Thermo Orbitrap, MaxQuant database
search) are NOT in this repo — only the downstream figure pipeline is.
In scope (pipeline-derived, reproducible here)
Running the R figGen pipeline on the shipped datIn/ tables regenerates these
quantitative results:
| ID | Result | Fig | Pipeline |
|---|---|---|---|
| C1 | Proteins per run: prioritized vs not-prioritized (median) | Fig 1b | figGen Technical Figure 1b chunk on Fig1/MQL_contrast_evidence.txt |
| C2 | High-priority-peptide data completeness / success-rate distribution | Fig 1b/1c | figGen Fig1b/1c chunks |
| C3 | Quantified proteins per single cell: pSCoPE vs shotgun (+106%) | Fig 2a | figGen Fig2a on Fig2/Fig2_a_Coverage/* |
| C4 | Unique peptides per run (+103%) / MS² assignment fraction 84% | Fig 2a | figGen Fig2a |
| C5 | Median precursor intensity ~2.5-fold lower (pSCoPE vs shotgun) | Fig 2b | figGen Fig2b |
| C6 | Data completeness gain for challenging peptides (+171%) | Fig 2c | figGen Fig 2c |
| C7 | Protein-level data completeness 93% (vs shotgun), +34% | Fig 2d | figGen Fig 2d |
| C8 | Spike-in quantification: slope 1.06, R²=0.97, 16-fold range | Fig 3b | figGen Fig 3b |
| C9 | 1,123 proteins quantified across 373 single macrophages | Fig 4a | figGen BMDM data-import chunk |
| C10 | Twofold (>2×) overall gain in sensitivity/completeness/coverage | Abstract | aggregate of C3–C7 |
Out of scope (not attempted, why)
- Wet-lab / instrument acquisition (MaxQuant.Live prioritized acquisition on Orbitrap; sample prep; nPOP). Hardware + proprietary software — not reproducible.
- MaxQuant database search of raw
.rawfiles into the evidence/msms tables. The raw files (~hundreds of GB) live on MassIVE, not in the figGen deposit; the paper's figGen pipeline starts from the searched tables. We reproduce from those tables (P16: applying the shipped pipeline to the shipped data is valid). - Gene-ontology / PSEA biological interpretation chunks (Fig 4–6 biology) are attempted opportunistically but down-ranked — they need Bioconductor GO DBs and are interpretive rather than a single pinnable number.
Reproduction strategy
- Build R env (conda) on «our HPC» front1; data already on «infra».
- Patch the notebook to read
datIn/locally (skip the Zenodo download chunk, already fetched) and to PRINT/serialize the numeric values feeding each panel. - Run as a SLURM compute job; pull back the small numeric outputs (CSV/JSON).
- Compare extracted numbers to the paper claims above (claims.tsv / agreement.json).
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.
This is a near-textbook 1:1 reproduction: the reproduction applied the authors' own shipped figGen R pipeline to their own deposited Zenodo data and recovered 9/10 quantitative figure claims exactly or within rounding, including the central over-twofold depth/completeness gains (C3 +106%, C4 +103%, C6 +171%, C7 93%/+34%) and exact spike-in regression. The only deviation, C5 precursor intensity (2.79 vs 2.5-fold), is a secondary metric, same direction, and most plausibly a rounding/wording difference on our methodology side — not an authors' defect. No fabrication or non-derivability indicators; every value is reproducible from the shared data.
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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