Unlocking the archived proteome: High-throughput, deep FFPE proteome profiling using the Orbitrap Astral Mass spectrometer

Importance of the topic

Formalin-fixed, paraffin-embedded (FFPE) tissue archives represent a vast, clinically annotated resource for translational research and biomarker discovery. Unlocking the proteome from FFPE material enables retrospective studies across large patient cohorts, supporting precision oncology and pathology. However, formaldehyde crosslinking and paraffin embedding complicate extraction and digestion, historically limiting throughput, reproducibility, and depth of proteomic profiling. The presented workflow addresses these obstacles by combining streamlined sample preparation with high-speed LC–MS on the Orbitrap Astral platform to enable sensitive, scalable proteome coverage from archived tissues.

Objectives and study overview

The study aims to demonstrate a high-throughput, robust FFPE proteomics pipeline that (1) simplifies deparaffinization and protein extraction, (2) delivers deep proteome coverage from low on-column loads (20–200 ng), and (3) scales across fast LC modes (60, 180, 500 samples per day; SPD) using the Orbitrap Astral mass spectrometer. FFPE lung tumor and matched normal tissues were processed, analyzed by DIA (directDIA) and interpreted with Spectronaut and downstream R-based analysis to evaluate identification depth, reproducibility, and biological differentiation between tumor and normal samples.

Methods and workflow

Sample preparation:

• Deparaffinization: xylene washes followed by graded ethanol.
• Lysis and crosslink reversal optimized for FFPE material; samples prepared with the EasyPep Mini MS kit for digestion.
• Protein quantification by Rapid Gold BCA assay and peptide quantification by a fluorometric peptide assay prior to LC–MS.

LC–MS acquisition strategy:

• LC platforms: Thermo Scientific Vanquish Neo UHPLC with OptiSpray µPAC Neo cartridges for 60 SPD (20 min gradients) and 180 SPD (6.8–7 min gradients); Evosep ENO system with EV1182 Performance Column for 500 SPD (2–2.3 min gradients).
• Column temperatures: ~55 °C for µPAC Neo cartridges; ~40 °C for Evosep EV1182.
• Acquisition mode: data-independent acquisition analyzed in directDIA mode with Biognosys Spectronaut; downstream statistics and visualization in R (R Studio).
• MS settings summary: Orbitrap Astral operated with high-resolution MS1 (e.g., 240,000) and optimized MS2 isolation windows and AGC/Max-IT for each throughput regime to balance speed and sensitivity.

Data analysis:

• Raw DIA files processed in Spectronaut directDIA, export of peptide/protein quantifications, and statistical analyses (CV, differential expression, pathway enrichment) performed in R.

Used instrumentation

• Thermo Scientific Orbitrap Astral Mass Spectrometer (DIA-capable high-speed platform).
• Thermo Scientific OptiSpray µPAC Neo cartridges (50 cm and high-throughput variants) and OptiSpray ion source.
• Thermo Scientific Vanquish Neo UHPLC System for direct injection workflows.
• Evosep ENO system with EV1182 Performance Column and EvoTips for ultra-fast (500 SPD) separations.
• Thermo Scientific EasyPep Mini MS sample prep kit, Pierce Rapid Gold BCA Protein Assay kit, and Pierce Fluorometric Peptide Assay Kit for sample handling and quantification.
• Biognosys Spectronaut software for directDIA processing; R / R Studio for downstream analysis.

Main results and discussion

Depth and throughput trade-offs:

• High-depth mode (60 SPD, 20-minute gradient, 200 ng on-column) yielded up to ~8,600 protein groups and >100,000 peptide identifications from FFPE lung tissue.
• High-throughput mode (500 SPD, ~2-minute gradient) provided ~3,700 protein IDs from 20–200 ng loads, illustrating a trade-off between speed and proteome depth.
• Intermediate throughput (180 SPD, ~6.8–7 minute gradient) retained approximately 70% of the protein identifications observed at 60 SPD; 500 SPD retained ~45%.

Quantitative performance and sensitivity:

• Sensitivity extended down to 20 ng on-column with robust quantitation; median coefficients of variation (CVs) were ≤10% across acquisition speeds and sample amounts, indicating high reproducibility.
• Correlation of fold-changes between speeds was high (e.g., r ≈ 0.93 for 180 vs 60 SPD; r ≈ 0.87 for 500 vs 60 SPD), supporting consistent differential-expression trends across throughput regimes.

Biological insights:

• Differential expression between lung tumor and normal FFPE samples revealed tumor-enriched proteins and pathways; top enriched KEGG pathways were identified from proteins upregulated in tumor tissue (figure-based summaries translated into pathway lists and enrichment metrics).
• Workflow supported pathway-level interpretation and reliable identification of biologically relevant signals even at reduced gradient lengths.

Operational advantages:

• End-to-end processing (deparaffinization, digestion, LC–MS, and initial analysis) can be completed within a single working day, facilitating large-cohort studies.
• The OptiSpray ion source and method optimization reduced instrument contamination risk from FFPE-derived sample matrix, improving robustness for high-throughput operation.

Benefits and practical applications

The combined FFPE sample prep and Orbitrap Astral DIA workflow offers multiple practical advantages:

• Enables deep proteome profiling from archival clinical tissue, unlocking retrospective cohorts for biomarker discovery and translational research.
• Scalable throughput options let laboratories choose between maximal depth (20 min gradients) or very high sample throughput (2–7 min gradients) depending on study goals.
• Low-input capability (20 ng) and strong quantitative reproducibility make the protocol suitable for scarce or precious samples, microdissected regions, or spatial proteomics applications.
• Streamlined sample preparation and reduced instrument contamination support routine adoption in core facilities and clinical proteomics pipelines.

Future trends and possibilities

Anticipated developments and applications include:

• Integration with spatially resolved sampling and histology-guided microdissection to link proteomic changes to tissue microarchitecture at scale.
• Further optimization of sample prep chemistries and automated workflows to reduce manual steps, increase throughput, and standardize inter-laboratory performance.
• Expansion of DIA libraries and machine-learning–based spectral interpretation to improve identification at ultra-short gradients and for modified peptides relevant to disease mechanisms.
• Application to larger retrospective cohorts with linked clinical metadata to accelerate biomarker validation and translational discovery.

Conclusion

The presented workflow demonstrates that optimized FFPE sample preparation coupled with the Orbitrap Astral mass spectrometer and fast LC solutions can achieve both deep and high-throughput proteome profiling from archival tissue. The approach delivers high identification depth (up to ~8,600 proteins from 200 ng, 20-minute runs), reliable quantitation at low inputs (20 ng) with median CVs ≤10%, and scalable throughput (60–500 SPD) with predictable trade-offs in proteome coverage. This enables robust differential expression and pathway analyses from FFPE lung tumor and normal samples within an operationally efficient, single-day workflow, supporting broad translational and clinical-proteomics applications.

References

1. Haines M, et al. High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissues. Molecular & Cellular Proteomics. 2025.
2. Zhu Y, et al. High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification. Molecular Oncology. 2019;13:2305–2328.
3. Wang Y, et al. Effects of tumor metabolic microenvironment on regulatory T cells. Molecular Cancer. 2018. doi:10.1186/s12943-018-0913-y.
4. Liu Y, Vandekeere A, Xu M, Fendt SM, Altea-Manzano P. Metabolite-derived protein modifications modulating oncogenic signaling. Frontiers in Oncology. 2022. doi:10.3389/fonc.2022.988626.