ArticleMethods in molecular biology (Clifton, N.J.)2024
Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Potential of Proteomics in Forensic Phenotyping: A Focus on Biological Sex Estimation.Journal of proteome research · 2025Article
- Single-cell proteomics using mass spectrometry.Cell genomics · 2025Review
- scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.Genome biology · 2025Article
- Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.Journal of proteome research · 2025Review
- The hepatocellular model of fatty liver disease: from current imaging diagnostics to innovative proteomics technologies.Frontiers in medicine · 2025Review
- Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.Nature protocols · 2024Article
- Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.
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Registered trials
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