• Open Access

Proteomic workflow for analysis of archival formalin-fixed and paraffin-embedded clinical samples to a depth of 10 000 proteins

Authors

  • Jacek R. Wiśniewski,

    Corresponding author
    • Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany
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  • Kamila Duś,

    1. Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany
    2. Department of Pathology, Wrocław Medical University, Wrocław, Poland
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  • Matthias Mann

    1. Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany
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Correspondence: Dr. Jacek R. Wisniewski, Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry Am Klopferspitz 18, D-82152 Martinsried, Germany

E-mail: jwisniew@biochem.mpg.de

Fax: +49-8578-2219

Abstract

Purpose

Archival formalin-fixed and paraffin-embedded clinical samples represent a very diverse source of material for proteomic investigation of diseases, often with follow-up patient information. Here, we describe an analytical workflow for analysis of laser-capture microdissected formalin-fixed and paraffin-embedded samples that allows studying proteomes to a depth of 10 000 proteins per sample.

Experimental design

The workflow involves lysis of tissue in SDS-containing buffer, detergent removal, and consecutive digestion of the proteins with two enzymes by the multienzyme digestion filter-aided sample preparation method. Resulting peptides are fractionated by pipette-tip based strong anion exchange into six fractions and analyzed by LC-MS/MS on a bench top quadrupole Orbitrap mass spectrometer.

Results

Analysis of the data using the MaxQuant software resulted in the identification of 9502 ± 28 protein groups per a 110 nL sample of microdissected cells from human colonic adenoma. This depth of proteome analysis enables systemic insights into the organization of the adenoma cells and an estimation of the abundances of known biomarkers. It also allows the identification of proteins expressed from tumor suppressors, oncogenes, and other key players in the development and progression of the colorectal cancer.

Conclusion and clinical relevance

Our proteomic platform can be used for quantitative comparisons between samples representing different stages of diseases and thus can be applied to the discovery of biomarkers or drug targets.

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