Quantitative single-cell proteomics as a tool to characterize cellular hierarchies.

Area of Science:

• Proteomics
• Cell Biology
• Biotechnology

Background:

• Single-cell analyses are crucial for understanding biological heterogeneity.
• Current methods are primarily RNA-based, limiting proteomic insights.
• There is a need for scalable single-cell proteomics techniques.

Purpose of the Study:

• To establish and benchmark a global single-cell mass spectrometry-based proteomics workflow.
• To demonstrate its utility in exploring cellular heterogeneity in a leukemia model.
• To provide a computational framework for data analysis.

Main Methods:

• Developed a comprehensive experimental and computational workflow for single-cell proteomics.
• Utilized mass spectrometry for protein quantification in individual cells.
• Employed pre-enrichment and unbiased approaches in a leukemia model.
• Created the SCeptre computational workflow for data normalization and analysis.

Main Results:

• Successfully quantified approximately 1000 proteins per cell across thousands of cells.
• Demonstrated the workflow's capability to explore cellular heterogeneity in leukemia.
• Showcased efficient data processing and analysis using the SCeptre workflow.
• Achieved consistent results with limited instrument time.

Conclusions:

• Global single-cell mass spectrometry-based proteomics is now a viable tool for large-scale biological studies.
• The presented workflow and computational tools facilitate deep proteomic profiling of single cells.
• This work lays the foundation for widespread adoption of single-cell proteomics globally.