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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.

Christophe Vanderaa1, Laurent Gatto2

  • 1Computational Biology and Bioinformatics Unit (CBIO), de Duve Institute, UCLouvain, Av. Hippocrate 75, 1200, Brussels, Belgium.

Genome Biology
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

We introduce scplainer, a new method for analyzing mass spectrometry-based single-cell proteomics data. This approach simplifies complex data, corrects technical variability, and facilitates biological discovery.

Keywords:
Batch correctionData analysisData interpretationLinear modelingMass spectrometryMissing valuesProteomicsReproducible researchSingle-cell

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Area of Science:

  • Proteomics
  • Single-cell biology
  • Bioinformatics

Background:

  • Mass spectrometry (MS)-based single-cell proteomics (SCP) data analysis is challenging due to inherent technical limitations and the complexity of single-cell experiments.
  • Existing methods often require extensive data processing, hindering the extraction of meaningful biological insights.

Purpose of the Study:

  • To present scplainer, a standardized and principled computational approach for analyzing SCP data.
  • To enable robust extraction of biological insights from SCP datasets with minimal data processing.

Main Methods:

  • scplainer utilizes linear modeling for variance analysis, differential abundance analysis, and component analysis.
  • The approach incorporates streamlined result visualization.
  • It effectively corrects for technical variability, allowing integration of data from diverse SCP experiments.

Main Results:

  • scplainer provides a standardized framework for SCP data analysis.
  • The method successfully corrects for technical variability, improving data integration.
  • It shifts focus from technical data handling to biological question answering.

Conclusions:

  • scplainer reshapes the analysis of single-cell proteomics data.
  • This approach facilitates deeper biological understanding by simplifying data analysis.
  • It enhances the utility of MS-based SCP for biological discovery.