High-Throughput Single-Cell Proteomics Enabled by Integrating nPOP Workflow with Quantitative Hyperplexing

Area of Science:

• Proteomics
• Mass Spectrometry
• Cell Biology

Background:

• Single-cell proteomics (scProteomics) offers high resolution for cellular heterogeneity but faces challenges in proteome coverage, quantitative accuracy, and throughput.
• Existing methods struggle to balance depth, accuracy, and speed for large-scale single-cell proteomic analysis.

Purpose of the Study:

• To establish a high-throughput single-cell proteomics workflow integrating nanoproteomic sample preparation (nPOP) and quantitative hyperplexing (IBT16-TMTpro 16).
• To optimize label-free and hyperplexing strategies for enhanced proteome coverage, quantitative accuracy, and scalability in scProteomics.

Main Methods:

• Developed a modified nanoproteomic sample preparation (nPOP) workflow.
• Implemented an IBT16-TMTpro 16 quantitative hyperplexing strategy.
• Systematically optimized chromatographic and mass spectrometric conditions for label-free and hyperplexing workflows using timsTOF SCP and Orbitrap Astral Zoom.

Main Results:

• Achieved identification of >3000 protein groups from individual 293T and HeLa cells using the label-free workflow.
• Quantified ~2000 protein groups per cell in single cholangiocarcinoma (CCA) cells, revealing distinct molecular patterns.
• The nPOP-based hyperplexing workflow achieved >95% labeling efficiency, identifying 1400-2000 protein groups/cell across four cell lines.
• Demonstrated ultrahigh-throughput capacity (~2000 cells/day) with the hyperplexing strategy.

Conclusions:

• The developed label-free and quantitative hyperplexing workflows offer an efficient and scalable platform for large-scale scProteomics.
• This approach significantly enhances proteome depth and throughput compared to conventional methods.
• The platform holds promise for advancing single-cell resolution studies in basic research and clinical applications.