Proteomics
Per-cell protein matrices cluster into states and contrast tables when bulk LC-MS/MS averages heterogeneity away, with pinned preprocessing and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Thermo Orbitrap (Exploris, Fusion Lumos, Eclipse); Bruker timsTOF (dia-PASEF, Ultra when scoped); TMT/TMTpro isobaric carrier multiplexing (SCoPE2-style; [1]); plexDIA and nPOP; label-free DIA single-cell; Proteome Discoverer, MaxQuant, and DIA-NN exports. Scoped at kickoff. |
| Input requirements | PSM or protein quantification tables plus colData with cell ID, MS batch/run, condition, and carrier channel; often hundreds of cells per condition for clustering [2]; diluted bulk reference sample per batch recommended [2] |
| Reference builds supported | UniProt Swiss-Prot reviewed proteomes (human UP000005640, mouse UP000000589); CPTAC-style contaminant sequences; organism-specific FASTA on request |
| Primary tools (with versions) | scp 1.22+; QFeatures; SingleCellExperiment; QuantQC; DIA-NN 2.0+; MaxQuant 2.8+; proDA 1.0; limma 3.60, pinned per project |
| Typical turnaround time | 4-8 weeks (standard SCP cohort, one primary contrast). Longer for multi-batch integration, matched scRNA-seq, or atlas-scale cell counts. Confirmed at kickoff. |
| Deliverable formats | Protein matrices (.csv, .tsv); SingleCellExperiment .rds objects; PDF/SVG figures; HTML QC report; commented R scripts; Methods draft |
| Key cited best-practice reference | Deutsch et al. [2], Nature Methods; Specht et al. [1], Genome Biology (SCoPE2) |
| Custom / bespoke analysis | Non-standard matrices, cross-modality integration, client-specified QC thresholds, trajectory analysis, or phosphoproteomics extensions scoped at kickoff |
Single-cell proteomics (SCP) converts PSM tables into a cell-by-protein matrix, then applies per-cell QC, batch correction, clustering, and differential testing. That measures which proteins define cell states rather than tissue averages [2]. Unlike bulk DDA/DIA proteomics, SCP can resolve subpopulations and continuous proteome gradients [1]. Unlike Olink PEA, it is discovery-scale MS without a fixed antibody panel. SCoPE2 quantified about 1,000 proteins per cell across 1,490 monocytes and macrophages [1]. Pepkio starts from client PSM or protein matrices with documented QC. Custom workflows are agreed at kickoff. See the single-cell proteomics glossary.
Single-cell proteomics fits when you need protein-level cell-state heterogeneity, such as differentiation trajectories, rare subpopulations, or post-transcriptional regulation that bulk or RNA measurements often miss [1, 2].
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Single-cell MS proteomics (SCoPE2 / DIA / nPOP) | Cell-state discovery, proteome gradients, low-input heterogeneity, matched multi-omics | Sparse matrices; carrier or library design affects depth; MS time per cell limits throughput | Higher per-cell MS and sample-prep cost than bulk; moderate bioinformatics cost |
| Bulk DDA/DIA proteomics | Cohort-scale differential abundance when tissue is homogeneous | Averages across cell types; rare states below detection | Lower per-sample MS cost than SCP for modest cohorts |
| scRNA-seq | Cell-type atlases, trajectory inference, large cell counts | mRNA proxy; post-transcriptional regulation and protein stability are not measured directly ([1]: about 20-fold more protein than RNA copies per gene) | Library prep + sequencing; bioinformatics varies by cell count |
| Olink PEA | Targeted plasma or CSF panels, clinical-scale validation | Fixed panel size; not discovery-scale MS depth | Panel and plate cost; efficient for targeted biomarker testing |
Pepkio confirms PSM or protein matrix integrity, MS batch structure, carrier channels, and contrast definitions in sample_manifest.csv. Confounded batch designs or very few cells per group are flagged before processing [2].
Tools used: Custom validation scripts; scp annotation schema check
Output: sample_manifest.csv with cell IDs, MS batch, condition labels, carrier design notes, and QC flags
PSM quantification tables from MaxQuant, Proteome Discoverer, or DIA-NN are imported per MS batch using readSCP() into a QFeatures object with a PSM to peptide to protein hierarchy [4, 5, 6].
Tools used: scp 1.22+; QFeatures; SingleCellExperiment
Output: Per-batch QFeatures object; import log with row and column counts per set
Run-level and per-cell metrics include reporter intensity (RI), CV, peptide counts, and sample-to-carrier ratio (SCR). QuantQC is applied when nPOP exports include it [2, 7].
Tools used: scp 1.22+; QuantQC; custom R diagnostics
Output: cell_qc_summary.csv; SCR distribution plots; RI and peptide-count histograms per batch
PSMs are filtered at 1% peptide FDR. SCR-based filtering removes PSMs with unreliable single-cell quantification. Carrier-only PSMs (missing SCR) are excluded [2, 4].
Tools used: scp 1.22+
Output: Filtered PSM assay; psm_filter_log.csv documenting FDR and SCR thresholds
PSM intensities are aggregated to peptide and protein levels using median aggregation per scp conventions, preserving links between assay layers for audit and re-aggregation [4].
Tools used: scp 1.22+; QFeatures aggregation functions
Output: Peptide and protein assays within QFeatures; aggregation_log.csv
Zero intensities are converted to NA before normalization, treating zeros as missing rather than true absence [2, 4]. Protein matrices are log-transformed. MNAR versus MCAR missing-value patterns are profiled before any imputation [8].
Tools used: scp 1.22+
Output: protein_matrix_raw.csv; missing_value_profile.csv; normalization parameters in analysis_parameters.yaml
Cells with low RI, high CV, or very few quantified peptides are removed using batch-adaptive thresholds. Retained and excluded counts are documented per filter rule [2].
Tools used: scp 1.22+
Output: Filtered protein matrix; updated cell_qc_summary.csv; cell-exclusion plots
SCP acquires few multiplexed cells per MS run, so batch structure is typical. scplainer removes technical variation when shared cell states span batches. Batch is modeled as a covariate when correction is not possible [2].
Tools used: scp 1.22+ (scplainer); limma 3.60 when batch is modeled
Output: Batch-corrected protein matrix; before/after PCA by batch and condition
Pepkio builds a neighbor graph on the corrected protein matrix, runs Leiden community detection at data-driven resolution, and computes PCA/UMAP embeddings. Cell states receive provisional labels from top marker proteins per cluster.
Tools used: scp 1.22+; leidenalg 0.10+; custom R visualization
Output: Cluster assignments in cell_metadata.csv; PCA/UMAP plots; marker_protein_table.csv
Cluster-wise or condition-wise differential testing uses proDA or limma with Benjamini-Hochberg FDR control, accounting for missing values without blind imputation [9, 10]. Optional GO/KEGG enrichment is scoped at kickoff. Figures, scripts, README, and a Methods draft are bundled.
Tools used: proDA 1.0; limma 3.60
Output: dep_by_cluster.csv or dep_<contrast>.csv; volcano plots; final .rds object; scripts; Methods draft
Often hundreds of cells per condition support clustering. Deutsch et al. [2] recommend reporting counts per group, and Specht et al. [1] analyzed 1,490 cells. SCoPE2 reports about 1,000 proteins per cell [1]. nPOP with plexDIA reports about 3,000-3,700 proteins per human cell [7]. Smaller cohorts are accepted with reduced-power notes in the QC report. Exact targets are confirmed at kickoff.
Yes, with caveats documented in the QC report. Cells with very low reporter intensity, high CV, or very few quantified peptides are flagged and typically excluded before clustering [2]. Empty carrier channels and carrier-only PSMs are removed during SCR filtering. Re-acquisition or pooling strategies are discussed when cell yield threatens the study question.
Yes, for formats we can load after kickoff review. Thermo Orbitrap .raw files from Exploris, Fusion Lumos, and Eclipse support TMT/TMTpro carrier multiplexing and DIA workflows when scoped. Bruker timsTOF .d files support dia-PASEF and Ultra single-cell acquisitions when scoped. MaxQuant, Proteome Discoverer, and DIA-NN PSM or protein exports are the primary computational entry points. nPOP and plexDIA exports are supported when scoped at kickoff.
A standard SCP project (roughly 100-500 cells, one primary contrast, one or two MS batch groups, and clustering plus DE) typically completes in 4-8 weeks from data receipt. Multi-batch integration, matched scRNA-seq correlation, or cohorts exceeding 1,000 cells may take longer. Milestone check-ins occur during the project. Exact timelines are confirmed at kickoff.
When batch is known and not fully confounded with condition, Pepkio applies scplainer batch correction or includes batch as a covariate in limma/proDA models [2]. PCA and RI/CV diagnostics are reviewed before modeling. Designs without batch randomization are flagged at kickoff. Biological and technical factors may be inseparable when randomization was not performed.
Yes. You retain full ownership of all code, scripts, and results delivered under the project agreement. Pepkio provides commented R scripts with environment lock files so you can rerun analyses when the execution environment matches the pinned setup. Matrices use standard .csv and .tsv formats. SingleCellExperiment objects are delivered as .rds files readable in R/Bioconductor.
Yes. Checkpoint reviews occur after cell-level QC, after batch correction, and before final delivery when scoped at kickoff. You can review cluster annotations, SCR filtering thresholds, contrast definitions, and marker-protein tables within agreed scope. A dedicated scientific contact leads the project, coordinates milestone feedback, and documents decisions in the QC report and Methods draft.
Clarification of methods, QC thresholds, SCR filtering, and minor figure or table revisions within agreed scope (typically ≤20% of project scope), consistent with Pepkio's standard post-delivery policy. It does not include open-ended reanalysis or new biological contrasts. Substantial new work such as additional contrasts, alternate imputation, or scRNA-seq integration is scoped as separate milestones.
No. Pepkio is fee-for-service unless co-authorship is agreed in advance. You retain ownership of results and code. Billing is separate from publication credit.
Carrier amount should reflect project priorities. Isobaric carriers about 200-fold larger than single-cell samples enhance peptide identification with a limited adverse effect on quantification [11]. Larger carrier ratios can increase identification depth but can also compress single-cell dynamic range and worsen TMT channel impurity effects [12]. Pepkio documents carrier design assumptions and SCR thresholds in the QC report. Channel exclusion is applied when impurity correction is insufficient.
TMT with an isobaric carrier (SCoPE2-style) quantified about 1,000 proteins per cell in published benchmarks [1]. Label-free DIA and plexDIA can increase depth. nPOP reports about 3,000-3,700 proteins per cell with plexDIA [7], but those workflows need workflow-specific informatics [13]. Pepkio documents trade-offs at kickoff based on your export format and biological question.
Yes, when a feasibility review confirms inputs, outputs, and timeline. Custom matrices, client QC thresholds, scRNA-seq integration, trajectory inference, phosphoproteomics, or non-standard deliverables are scoped at kickoff. Raw .raw reprocessing can be scoped via the DDA/DIA proteomics service when needed.
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