Digitizing the Proteomes From Big Tissue Biobanks

Significance of the Topic

A robust analysis of biobanked formalin-fixed paraffin-embedded (FFPE) tissues is essential for uncovering protein biomarkers related to health and disease. High-throughput proteomic profiling of large cohorts enables the correlation of protein expression patterns with clinical outcomes, accelerating marker discovery and supporting precision medicine.

Objectives and Study Overview

This study aimed to implement a scalable workflow for quantitative proteomics of FFPE colon tissue samples. By leveraging microflow SWATH® acquisition and Spectronaut Pulsar analysis, the authors quantified proteomes from 105 samples (95 cancerous and 10 healthy) in less than five days, with the goal of identifying differential proteins and potential cancer subtypes.

Methodology and Instrumentation

Sample Preparation:

• Tissue: FFPE colon biopsies classified as healthy or cancerous.
• Extraction: Adapted deparaffinization and tryptic digestion of 10 μm slices yielding ~140 μg protein.
• iRT peptides spiked for retention time normalization; 6 μg digest injected per run.

Chromatography and MS:

• Column: Triart C18 (150×0.3 mm) on NanoLC™ 425 at 5 μL/min over a non-linear 43 min gradient.
• Mass Spectrometer: SCIEX TripleTOF® 6600 with microflow SWATH Acquisition (120 variable windows; 2.4 s cycle time).

Data Analysis:

• Spectral library: DDA of pooled samples, resulting in 5,499 protein groups and 49,176 precursors.
• Quantification and normalization via Spectronaut Pulsar with 1% FDR filtering.
• Statistical testing: t-test with Storey correction; PCA and clustering for subtype discovery.

Main Results and Discussion

The workflow achieved high reproducibility (10% CV for peptide yield; 0.4% RSD for retention time) and depth (median 3,644 proteins in cancer; 2,882 in healthy). Differential analysis identified 1,023 proteins (Q<0.01, |log2FC|>0.58), of which 703 were upregulated in tumors. Gene ontology highlighted enrichment of translation and RNA metabolism in cancer, reflecting elevated protein synthesis. Unsupervised clustering revealed three proteomic subtypes (A, B, C), with subtype B showing elevated HNF4α, consistent with previous reports.

Benefits and Practical Applications of the Method

• Enables large-scale, reproducible proteome quantification from archived FFPE tissues.
• Supports biomarker discovery and subtype stratification in oncology.
• Delivers rapid throughput (24 proteomes/day) and robust data analysis.

Future Trends and Applications

Integration with multi-omics, machine learning–driven subtype classification, and automation of sample preparation are promising directions. Applying this workflow to diverse biobanks could uncover novel markers across diseases and support clinical translation.

Conclusion

Microflow SWATH acquisition coupled with Spectronaut Pulsar provides a fast, robust platform for high-throughput proteomics of FFPE biobank samples. The approach yields deep quantitative datasets that facilitate differential protein analysis and tumor subtype identification, advancing precision oncology research.

References

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