Deeper proteome coverage and faster throughput for single-cell samples on the Orbitrap Astral mass spectrometer

Importance of the Topic

The accurate profiling of proteins at the single-cell level has become critical for understanding cellular heterogeneity and complex biological systems. Advances in mass spectrometry–based proteomics now make it possible to analyze minute sample amounts with high sensitivity, depth of coverage, and throughput. High-performance instrumentation and robust workflows are essential to drive discoveries in cell biology, diagnostics, and therapeutic research.

Objectives and Study Overview

This study evaluates the performance of the Thermo Scientific Orbitrap Astral mass spectrometer for single-cell proteomics. The focus is on assessing proteome coverage, quantitative precision, accuracy, and throughput using data-independent acquisition (DIA) in both library-free and library-based modes. Key metrics include the number of protein groups identified and quantified from dilution series (50 pg to 5 ng) and individual HeLa cells, as well as method reproducibility across instruments and laboratories.

Methodology and Instrumentation

A single-cell proteomics workflow was established as follows:

• Sample Preparation: HeLa protein digest standard diluted to concentrations from 5 ng down to 50 pg; single cells prepared on a Cellenion CellenONE platform.
• LC Separation: Vanquish Neo UHPLC with μPAC Neo columns (50 cm and 25 cm) operated at low flow rates (0.2 µL/min for 50 cm; 0.45 µL/min for 25 cm) using 80 samples per day (SPD) and 50 SPD gradients.
• Ion Mobility Filtering: FAIMS Pro interface to reduce chemical background and enhance signal-to-noise.
• Mass Spectrometry: Orbitrap Astral mass spectrometer combining high-resolution Orbitrap full scans (240 k) with high-speed Astral MS/MS scans in DIA mode; acquisition windows and injection times optimized by sample load.
• Data Analysis: Spectronaut™ 18 directDIA and library-based searches; Proteome Discoverer™ 3.1 for cross-validation. All searches used a 1% FDR at precursor, peptide, and protein levels.

Key Results and Discussion

• Sensitivity and Throughput: The 80 SPD method achieved rapid 18 min cycles, quantifying an average of 3 400 protein groups per single cell and up to 6 650 groups from 5 ng of digest. The 50 SPD method extended depth, identifying nearly 7 700 proteins from 250 pg in library-based searches.
• Quantitative Performance: Median CVs across dilution points remained below 10% (7% at 250 pg). Processing all injections together improved precision and accuracy, with measured ratios closely matching expected dilution factors and most calibration curves showing correlation coefficients above 0.9.
• Reproducibility: Triplicate analysis of 250 pg HeLa digest across three laboratories and instruments yielded an RSD of 2% for quantified protein groups and peptides.
• Library Strategies: Library-based DIA using spectral libraries from higher-load runs (10 ng or pooled single-cell runs) increased quantified protein groups by up to 27% and peptides by over 60% compared to directDIA.
• Single-Cell Analysis: Twelve individual HeLa cells yielded on average 3 400 proteins per cell. Joint processing of single-cell runs further increased identifications to over 4 250 protein groups and 23 550 peptides per analysis.

Benefits and Practical Applications

• High Sensitivity: Enables deep proteome coverage from sub-nanogram samples or individual cells.
• Fast Throughput: 50 SPD and 80 SPD workflows support large-scale studies and screening.
• Robustness and Reproducibility: Consistent performance across instruments, sites, operators, and days supports reliable comparative studies.
• Flexible Data Analysis: DirectDIA and library-based approaches allow users to tailor workflows to depth or speed requirements.

Future Trends and Potential Applications

• Integration with Multi-Omics: Combining single-cell proteomics with transcriptomics and metabolomics for comprehensive cell profiling.
• Clinical and Translational Studies: High-throughput single-cell analyses for biomarker discovery, tumor heterogeneity characterization, and personalized medicine.
• Artificial Intelligence–Driven Workflows: Machine learning to enhance library construction, peak picking, and quantitative accuracy.
• Instrumentation Enhancements: Further improvements in ion transmission, detector sensitivity, and scan speed to push limits of depth and throughput.

Conclusion

The Orbitrap Astral mass spectrometer, coupled with optimized UHPLC, FAIMS Pro, and advanced data analysis, sets a new standard for single-cell proteomics. It delivers unparalleled sensitivity, quantitative accuracy, and throughput, enabling robust deep-proteome coverage from minute sample amounts. This workflow unlocks new insights into cellular heterogeneity and accelerates discovery in research and clinical applications.