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Bioinformatics Pipeline for Processing Single-Cell Data.

Arthur Declercq1,2, Nina Demeulemeester1,2,3, Ralf Gabriels1,2

  • 1VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.

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

This study introduces a bioinformatics pipeline for single-cell proteomics, enhancing the identification of low-abundance proteins. The advanced method improves peptide detection and protein identifications in complex cellular samples.

Keywords:
BioinformaticsDeepLCMS2PIPMS2RescoreMachine learningSingle-cell proteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Cellular Biology

Background:

  • Single-cell proteomics offers insights into cellular dynamics.
  • Identifying low-abundance proteins is a significant challenge.

Purpose of the Study:

  • To present a state-of-the-art bioinformatics pipeline for single-cell proteomics.
  • To improve the detection and identification of low-abundance peptides and proteins.

Main Methods:

  • Utilized Sage (via SearchGUI) for protein identification.
  • Employed MS2Rescore with LC-MS/MS behavior predictors (MS2PIP, DeepLC) for score recalibration.
  • Performed quantification with FlashLFQ and differential expression analysis with MSqRob2.

Main Results:

  • The pipeline enhances the detection of low-abundance peptides.
  • Achieved increased protein identifications while maintaining strict False Discovery Rate (FDR) thresholds.
  • Demonstrated improved performance in single-cell proteomic data analysis.

Conclusions:

  • The integrated bioinformatics pipeline significantly advances single-cell proteomics.
  • This approach is crucial for detailed analysis of cellular heterogeneity and function.
  • Enables more comprehensive proteomic profiling at the single-cell level.