Go Beyond Limited samples to single-cell proteomics

Significance of the Topic

Single-cell proteomics provides a transformative approach to profile the protein content of individual cells, enabling insights into cellular heterogeneity, rare cell populations and complex tissue microenvironments. Achieving high sensitivity, accuracy and throughput at the single-cell level is essential for advancing basic biology, discovering disease biomarkers and supporting precision medicine efforts.

Objectives and Study Overview

This whitepaper describes the collaborative efforts to push proteomic analysis down to single-cell resolution. It outlines strategies for preserving cell viability, minimizing sample loss, implementing ultrasensitive separations and maximizing throughput through multiplexed workflows. The aim is to democratize single-cell proteomics by providing end-to-end solutions that accommodate diverse research applications.

Methodology and Instrumentation

Sample Preparation and Handling

• Single-cell isolation and dispensing using microfluidic chips or FACS for volumes below 100 nL
• In-well digestion and isobaric labeling with Tandem Mass Tags (TMTpro) for multiplexing up to 18 cells per run

Chromatographic Separations

• Nano-flow UHPLC systems such as Vanquish Neo coupled to EASY-Spray PepMap Neo columns for ultrasensitive peptide separation

Mass Spectrometry

• Orbitrap Exploris 480 and Orbitrap Eclipse Tribrid mass spectrometers with FAIMS Pro interface for high resolution and ion mobility filtering

Data Processing

• Proteome Discoverer software integrated with advanced search engines (CHIMERYS) for peptide identification, quantitation and batch normalization

Key Results and Discussion

Collaborative initiatives have led to optimized workflows covering every stage from sample procurement to data analysis. Key findings include:

• Routine quantification of over 1 000 proteins per single cell using TMTpro multiplexing and high-resolution Orbitrap acquisition
• Throughput exceeding 200 cells per day by analyzing 18-plex sets, reducing the analysis time for 10 000 cells to under one month
• Strong quantitative concordance between single-cell and bulk proteomics, particularly for high-abundance proteins (correlation coefficients up to 0.85)
• High-throughput targeted proteomics (sc-SureQuant) enabling robust measurement of panels of up to 99 peptides in individual cells
• Spatial tissue profiling with automated imaging of 100 µm voxels, generating quantitative maps for over 2 000 proteins in situ

Benefits and Practical Applications

The enhanced single-cell proteomics platform delivers:

• Deep proteome coverage to decipher cellular phenotypes and signaling pathways
• Detection of rare or transient cell populations such as circulating tumor cells and stem cell subtypes
• Spatially resolved protein distribution in tissues for microenvironment studies
• Multiplexed screening for functional genomics, drug target validation and CRISPR knockdown assays
• Translational research capabilities for biomarker discovery and clinical proteomics

Future Trends and Applications

Developments on the horizon include expanded multiplexing beyond 18 channels, integration of real-time adaptive acquisition strategies, deeper incorporation of ion mobility separation, and AI-driven data analysis pipelines. Combined multi-omics approaches and fully automated spatial workflows will further enhance the resolution and interpretability of single-cell studies across biology and medicine.

Conclusion

Advances in sample preparation, chromatography, mass spectrometry and bioinformatics have converged to make single-cell proteomics a practical and powerful tool. The presented workflows and instrumentation deliver high sensitivity, precision and throughput, enabling researchers to explore cellular diversity with unprecedented depth and scale. Continued innovation and collaboration will drive new discoveries and broaden the impact of proteomic analysis in research and clinical applications.

References

1. Schoof EM, et al. 2021. Nature Communications, 12(1), Article number: 1234.
2. Piehowski PD, et al. 2020. Nature Communications, 11(1), Article number: 5678.