Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering

Cell Syst. 2022 May 18;13(5):426-434.e4. doi: 10.1016/j.cels.2022.02.003. Epub 2022 Mar 16.


Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper's transparent peer review process is included in the supplemental information.

Keywords: FAIMS; feature matching; ion mobility; low-abundance proteins; lung; macrophage activation; mass spectrometry; nanoPOTS; single-cell proteomics; three-dimensional.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Chromatography, Liquid / methods
  • HeLa Cells
  • Humans
  • Ions
  • Mice
  • Peptides / chemistry
  • Proteome* / analysis
  • Proteomics* / methods


  • Ions
  • Peptides
  • Proteome