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Updated: Jun 23, 2025

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Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp

Samuel Grégoire1, Christophe Vanderaa1, Sébastien Pyr Dit Ruys2

  • 1Computational Biology and Bioinformatics Unit, de Duve Institute, UCLouvain, Brussels, Belgium.

Methods in Molecular Biology (Clifton, N.J.)
|June 21, 2024
PubMed
Summary
This summary is machine-generated.

Analyzing single-cell proteomics (SCP) data is challenging due to varied workflows. We present the scp R package for standardized SCP data analysis, improving reproducibility and enabling robust quality control and batch correction.

Keywords:
BioconductorData processingMass spectrometryQuantitative data analysisRSingle-cell proteomics

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Area of Science:

  • Proteomics
  • Single-cell analysis
  • Bioinformatics

Background:

  • Mass-spectrometry (MS)-based single-cell proteomics (SCP) is crucial for understanding cellular heterogeneity.
  • Current SCP data analysis workflows lack standardization, hindering reproducibility and evaluation.
  • The absence of ground truth datasets complicates pipeline assessment.

Purpose of the Study:

  • To introduce the scp R/Bioconductor package as a standardized framework for SCP data analysis.
  • To provide a flexible and comprehensive data analysis protocol for SCP data.
  • To benchmark SCP analysis pipelines using controlled experimental designs.

Main Methods:

  • Development of the scp R package utilizing QFeatures and SingleCellExperiment data structures.
  • Creation of a benchmark dataset using known proportions of cell lines for controlled variability.
  • Implementation of a data analysis protocol including quality control, data aggregation, normalization, and batch correction.

Main Results:

  • The scp package offers a modular and standardized framework for SCP data analysis.
  • The developed protocol was validated using a ground truth dataset, demonstrating its effectiveness.
  • The workflow facilitates crucial steps in SCP data processing, including quality control and batch correction.

Conclusions:

  • The scp package provides a standardized and reproducible approach to single-cell proteomics data analysis.
  • This framework aids in overcoming the challenges associated with analyzing complex SCP datasets.
  • The validated protocol enhances the reliability and interpretability of findings from single-cell proteomics studies.