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

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.