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Related Experiment Video

Updated: Sep 30, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering.

Jongmin Woo1, Geremy C Clair2, Sarah M Williams1

  • 1Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.

Cell Systems
|March 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, transferring identification based on FAIMS filtering (TIFF), to enhance single-cell proteomics (scProteomics). TIFF improves the detection of low-abundance proteins for more accurate cell analysis.

Keywords:
FAIMSfeature matchingion mobilitylow-abundance proteinslungmacrophage activationmass spectrometrynanoPOTSsingle-cell proteomicsthree-dimensional

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Cell Biology

Background:

  • Single-cell proteomics (scProteomics) is crucial for understanding cellular heterogeneity.
  • Current scProteomics methods struggle with identifying low-abundance proteins, limiting proteome depth and accuracy.
  • Enhanced sensitivity and quantitative accuracy are needed for comprehensive single-cell proteome analysis.

Purpose of the Study:

  • To develop and validate a novel method for sensitive and accurate label-free single-cell proteomics.
  • To improve the identification of low-abundance proteins in single-cell samples.
  • To enable deeper and more reliable proteome profiling at the single-cell level.

Main Methods:

  • Developed an ion-mobility-enhanced mass spectrometry acquisition and peptide identification strategy termed transferring identification based on FAIMS filtering (TIFF).
  • TIFF extends ion accumulation times by filtering singly charged ions, enhancing sensitivity.
  • Peptide identification utilizes a 3D MS1 feature matching approach incorporating retention time, accurate mass, and FAIMS compensation voltage.

Main Results:

  • The TIFF method achieved unbiased proteome analysis, identifying over 1,700 proteins in single HeLa cells, with more than 1,100 consistently identified.
  • Demonstrated the method's utility by profiling over 150 single murine macrophages over time during lipopolysaccharide stimulation.
  • Successfully identified time-dependent proteome changes in response to stimulation, showcasing the method's dynamic analysis capabilities.

Conclusions:

  • TIFF significantly enhances the sensitivity and accuracy of label-free single-cell proteomics.
  • The method enables deeper proteome coverage and reliable quantification, overcoming limitations of existing techniques.
  • TIFF provides a powerful tool for temporal proteome profiling and understanding cellular responses at the single-cell level.