Increased sensitivity for low-input label-free proteomics using the Orbitrap Astral MS and μPAC Neo Plus Columns

Significance of the topic

The analysis of very low-input proteomic samples is a growing need in biological research and clinical translational studies, where material is limited (e.g., rare cells, microdissected tissue, or single-cell preparations). Improving label-free quantitation (LFQ) sensitivity and reproducibility at sub-nanogram loads enables deeper proteome coverage and more reliable biological interpretation without relying on labeling strategies. This work evaluates a combined platform—Orbitrap Astral mass spectrometry, FAIMS Pro Duo interface, Vanquish Neo UHPLC and μPAC Neo Plus 50 cm micro-pillar array columns—to push detection limits and throughput for data-independent acquisition (DIA) LFQ workflows.

Goals and study overview

The study aimed to demonstrate peptide and protein identification depth and quantitative precision at low sample inputs using DIA on the Orbitrap Astral MS. Specific objectives included:

• Assessing sensitivity and identification depth across a HeLa digest dilution series (50 pg–20 ng).
• Comparing μPAC Neo Plus 50 cm micro-pillar array column performance against a conventional 75 μm I.D. × 25 cm packed-bed pulled-tip column.
• Optimizing Astral DIA parameters in a load-dependent manner (isolation window, injection time).
• Evaluating FAIMS compensation voltages and single versus multi-CV strategies for improving coverage.
• Testing direct injection and trap-and-elute workflows across throughputs from 20 to 100 samples per day (SPD).

Methodology

Key experimental elements:

• Sample: Pierce HeLa Protein Digest Standard prepared into a dilution series from 50 pg to 20 ng; triplicate injections per level.
• Chromatography: μPAC Neo Plus 50 cm column (75 μm I.D.) used in direct injection and trap-and-elute (backward flush) modes. Comparative experiments used a 75 μm × 25 cm pulled-tip column (sub-2 μm particles) operated with comparable LC methods.
• LC settings: Multiple throughput methods covering 20–100 SPD; column temperature 50 °C; sampler 7 °C; gradients and flow rates adjusted per throughput (detailed load-specific methods were used).
• Mass spectrometry: Thermo Scientific Orbitrap Astral Mass Spectrometer combined with FAIMS Pro Duo and EASY-Spray source. DIA acquisition windows and maximum Astral injection times were optimized based on sample load (see load-dependent settings such as wider windows and longer injection times for very low inputs).
• Data processing: Spectronaut 19.9 for peptide and protein identification and quantification with a 1% FDR threshold; three-run group analysis for each condition.

Used instrumentation

Primary hardware and consumables employed in the study included (select list):

• Orbitrap Astral Mass Spectrometer (high-resolution Astral analyzer + Orbitrap).
• FAIMS Pro Duo interface for gas-phase filtering and fractionation.
• Vanquish Neo UHPLC System with Vanquish Neo user interface.
• μPAC Neo Plus 50 cm HPLC Column and μPAC trapping column (for trap-and-elute).
• EASY-Spray emitter and EASY-Spray source.
• Sonation PRSO-V2 column oven (for μPAC columns).
• Consumables: MS-grade solvents (water, acetonitrile, formic acid), Pierce HeLa digest standard, SureSTART microvials/screw caps, DDM for reconstitution.

Main results and discussion

Summary of principal findings:

• Load-dependent Astral DIA optimization: Tuning isolation window width and Astral injection time according to sample amount preserved linearity of response across many orders of magnitude and avoided wasted dynamic range. Low-input benefits came from longer injection times and (in some cases) wider windows to boost signal.
• Chromatographic advantage of μPAC Neo Plus 50 cm: At inputs below ~5 ng, the μPAC column increasingly outperformed the 25 cm packed-bed column, yielding 6%–33% more peptide identifications at the lowest loads and up to 35% gains in peptides meeting CV <20% criteria. This improvement stems from the μPAC’s ordered microstructure which reduces dispersion and sharpens peaks.
• Proteome depth and throughput trade-offs: Best identification counts were observed at medium throughputs (30–50 SPD), reaching over ~49,000 peptides and >6,300 protein groups for optimized conditions. Across 20–100 SPD tested with 250 pg HeLa, protein group median CVs stayed in the ~13.5%–15% range, indicating stable quantitative performance even at high throughput.
• FAIMS benefits: Use of FAIMS to remove singly charged background and to perform gas-phase fractionation improved sensitivity. Acquisition with two FAIMS compensation voltages (longer gradients, 32 SPD) increased peptide identifications ~37% on average and yielded a ~5% increase in protein groups compared with a single CV (50 SPD).
• Trap-and-elute workflow: The μPAC trapping column enabled rapid loading and on-line desalting of larger volumes (useful for low-concentration samples). Trap-and-elute resulted in modest peak broadening compared with direct injection but largely preserved peptide and protein identifications; sample adsorption was observed with large injection volumes (7.5 µL) causing reduced peptide IDs.
• Reproducibility: Repeated experiments 50 hours apart produced consistent peptide and protein identification numbers (variation within ±10% for peptides and ±5% for proteins), demonstrating robustness under routine usage.

Practical benefits and applications

The combined platform provides several practical advantages for low-input and single-cell proteomics:

• Higher sensitivity and deeper coverage from sub-nanogram injections, enabling studies where material is scarce.
• Improved quantitative precision (more peptides with CV <10% and <20%) suitable for comparative LFQ experiments.
• Flexible workflows: direct injection for highest chromatographic sharpness, trap-and-elute for handling dilute samples and increased sample throughput.
• FAIMS integration reduces chemical background and can be used strategically (single vs multiple CVs) to trade off throughput and depth.
• Throughput scalability: validated methods across 20–100 SPD with maintained reproducibility support both discovery and higher-throughput screening uses.

Future trends and potential uses

Anticipated developments and opportunities arising from these results:

• Further tuning of DIA strategies and real-time acquisition schemes to maximize ion utilization for single-cell and sub-nanogram samples.
• Wider adoption of microfabricated stationary phases (μPAC) for routine low-flow, low-input proteomics workflows, and continued engineering to reduce adsorption and broaden chemical compatibility.
• Deeper integration of FAIMS/gas-phase fractionation in automated pipelines and multi-CV strategies to push proteome depth without proportionally extending run times.
• Advances in data processing (AI/ML–based deconvolution, library-free DIA analysis) to better interpret complex DIA spectra at low signal-to-noise ratios.
• Standardized low-input sample preparation kits and vial/solvent practices to minimize adsorption losses for dilute injections.
• Application in clinical and translational studies where biopsy size or cellular material is limiting, and in single-cell proteomics for cell-type resolved studies.

Conclusion

The study demonstrates that combining the Orbitrap Astral Mass Spectrometer with a μPAC Neo Plus 50 cm micro-pillar array column, FAIMS Pro Duo, and load-dependent DIA optimization substantially improves sensitivity and quantitative precision for label-free proteomics at very low inputs. The μPAC column provided marked gains in peptide identifications and precision at sub-nanogram loads compared with a conventional 25 cm pulled-tip column. FAIMS and optimized Astral DIA settings further increased proteome coverage. Both direct injection and trap-and-elute workflows are viable: direct injection delivers the narrowest peaks while trap-and-elute supports flexible loading of dilute samples with modest losses in chromatographic sharpness. Overall, the platform supports robust, high-throughput, low-input proteomics workflows applicable to single-cell and other material-limited studies.

References

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