Optimized protocol for metabolomic and lipidomic profiling in formalin-fixed paraffin-embedded kidney tissue by LC-MS
Graphical abstract
Introduction
Increasing evidence suggests that cancer should be considered as a metabolic disease [1,2]. In this context, liquid-chromatography mass spectrometry (LC-MS)-based metabolomics of tissue samples can provide unique information on physiological and pathological mechanisms. Kidney cancer metabolomics in fresh-frozen (FF) tissue has been successfully used to investigate the metabolite and lipid composition of tumor subtypes derived from different cells of origin [3,4]. Moreover, non-targeted approaches [5] allow for the comprehensive metabolomic and lipidomic profiling of tissue samples [[6], [7], [8]] and are continuously refined to maximize information yield from single pieces of tissue [9,10] or to enable in-depth profiling with accurate metabolite quantification [11]. Although FF tissue reflects the matrix of choice for metabolite profiling of localized tumors, well annotated specimens are a limited resource and technically demanding regarding storage and handling. In contrast, formalin-fixed paraffin-embedded (FFPE) tissue, as part of routine diagnostic applications in pathology [12,13], represents a promising alternative already used in genomic, transcriptomic and proteomic biomarker research [[14], [15], [16]]. With respect to LC-MS the limited number of available protocols for FFPE metabolomics rather focus on the profiling of small, polar molecules [17] while lipids have been scarcely considered [18]. In addition, employing high-resolution matrix-assisted laser desorption-/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR MSI) has allowed for the in situ detection and spatial analysis of small molecules with minimum requirements on FFPE tissue sample amounts [19,20]. However, the inability of monitoring isobaric species and the decreased detection of lipids [20] are limitations of imaging technologies that hamper the assessment of pre-analytical factors in a comprehensive fashion. In this regard, the capability of LC-MS to enable broad metabolite profiling, including lipids, allows for a more complete estimation of FFPE tissue metabolite content potentially affected by pre-analytical factors such as fixation time.
We here present a novel sample preparation protocol for comprehensive metabolomic and lipidomic profiling of FFPE tissue by LC-MS. Assessment of different extraction strategies, extraction solvents and conditions enabled us to determine methods with improved lipid detection from FFPE tissue. All procedures were evaluated regarding repeatability of sample preparation, analytical precision and day-to-day variation. To verify protocol applicability, a proof of concept experiment was carried out by analyzing FFPE tissue samples of clear cell renal cell carcinoma (ccRCC) and corresponding non-tumorous material. To assess pre-analytical factors, the impact of tissue fixation time on metabolite and lipid profiles was investigated, followed by MALDI-FT-ICR MS detection of compounds found to be unaffected by fixation time in an independent cohort of ccRCC tissue microarrays (TMAs). Ultimately, protocol optimization and assessment of pre-analytical factors by LC-MS with subsequent detection of selected lipid species by an independent in situ imaging approach demonstrates the complementary use of both techniques.
Section snippets
Chemicals and reagents
Ultra LC-MS grade acetonitrile (ACN) and methanol (MeOH) were purchased from Carl Roth GmbH & Co KG (Karlsruhe, Germany) and LC-MS grade methyl tert-butyl ether (MTBE), isopropanol (IPA), formic acid (FA) and ammonium acetate (AmAc) was obtained from Sigma-Aldrich (Taufkirchen, Germany). Pure water was provided by a Milli-Q system (Millipore, Billerica, MA, USA) and used for the preparation of aqueous solvents.
FFPE kidney tissue samples
Porcine kidney was obtained as fresh food product and used to prepare formalin fixed
Optimization of sample preparation for metabolomic and lipidomic profiling in FFPE tissue
Metabolomics analysis from FFPE specimens requires paraffin removal in order to make tissue accessible for subsequent metabolite extraction. Incubation in xylene for deparaffinization has been applied for metabolomics analysis of small polar molecules [35,36]. However, due to the non-polar nature of the solvent, lipid losses are expected hence making lipidomics analysis unreliable. Alternatively, incubation in 80% heated methanol for a combined deparaffinization and analyte extraction has been
Conclusion
Because of its broad metabolite profiling capability and robustness, LC-MS-based analytical chemistry provides an important tool to investigate metabolites and lipids in frozen tissue specimens [6,57]. Nevertheless, biomarker discovery in frozen tissue samples is often limited by small cohort sizes as repositories with high numbers of well-annotated samples are limited. These limitations could be overcome by using FFPE tissue samples from which large cohorts are available in pathology archives
CRediT authorship contribution statement
Sylvia K. Neef: Methodology, Formal analysis, Investigation, Visualization, Writing - original draft. Stefan Winter: Conceptualization, Supervision, Funding acquisition, Writing - review & editing. Ute Hofmann: Conceptualization, Supervision, Writing - review & editing. Thomas E. Mürdter: Conceptualization, Supervision, Writing - review & editing. Elke Schaeffeler: Conceptualization, Supervision, Funding acquisition, Writing - review & editing. Heike Horn: Resources, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We gratefully acknowledge Ursula Waldherr and Petra Hitschke for excellent technical assistance. This work was supported by the Robert Bosch Stiftung (Stuttgart, Germany), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2180–390900677, and the ICEPHA Graduate Program, University of Tübingen (Tübingen, Germany). Funding was provided by the Ministry of Education and Research of the Federal Republic of Germany (BMBF; Grant Nos.
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