Robust and Easy-to-Use One-Pot Workflow for Label-Free Single-Cell Proteomics

Area of Science:

• Biochemistry
• Proteomics
• Cell Biology

Background:

• Current proteomic workflows lack sensitivity and reproducibility for ultralow input samples, hindering biomedical research.
• Analyzing individual cells is crucial for understanding complex biological systems and disease mechanisms.

Purpose of the Study:

• To develop and validate a comprehensive, sensitive, and reproducible proteomic workflow for ultralow input samples, including single cells.
• To benchmark different data acquisition and analysis strategies for maximizing proteome coverage and throughput.

Main Methods:

• A novel workflow integrating improved cell lysis, sample handling (1 μL volume, 384-well plates), and semi-automated processing (CellenONE).
• Utilized ultrashort gradients (down to 5 min) with μ-pillar columns for high-throughput analysis.
• Benchmarked data-dependent acquisition (DDA), wide-window acquisition (WWA), and data-independent acquisition (DIA) with advanced algorithms.

Main Results:

• Identified 1790 proteins from a single cell using DDA across a four-order-of-magnitude dynamic range.
• Achieved identification of over 2200 proteins from single-cell input using DIA with a 20 min gradient.
• Successfully differentiated two cell lines, demonstrating the workflow's utility for cellular heterogeneity analysis.

Conclusions:

• The developed proteomic workflow significantly enhances sensitivity and reproducibility for ultralow input samples, including single cells.
• The workflow supports high-throughput analysis and provides deep proteome coverage, advancing single-cell proteomics.
• This method is suitable for characterizing cellular heterogeneity and addressing key biomedical questions.