Impact of post-surgical freezing delay on brain tumor metabolomics
Introduction: Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive. Objectives: To evaluate the imp...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 May 2019
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| In: |
Metabolomics
Year: 2019, Jahrgang: 15, Heft: 5 |
| ISSN: | 1573-3890 |
| DOI: | 10.1007/s11306-019-1541-2 |
| Online-Zugang: | Verlag, Pay-per-use, Volltext: https://doi.org/10.1007/s11306-019-1541-2
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| Verfasserangaben: | Andreas Mock, Carmen Rapp, Rolf Warta, Amir Abdollahi, Dirk Jäger, Oliver Sakowitz, Benedikt Brors, Andreas von Deimling, Christine Jungk, Andreas Unterberg, Christel Herold-Mende |
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| 520 | |a Introduction: Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive. Objectives: To evaluate the impact of post-surgical freezing delays on cancer tissue metabolomics and to investigate changes per metabolite and per metabolic pathway. Methods: We performed untargeted metabolomics on three cortically located and bulk-resected glioblastoma tissues that were sequentially frozen as duplicates at up to six different time delays (0-180 min, 34 samples). Results: Statistical modelling revealed that 10% of the metabolome (59 of 597 metabolites) changed significantly after a 3 h delay. While carbohydrates and energy metabolites decreased, peptides and lipids increased. After a 2 h delay, these metabolites had changed by as much as 50-100%. We present the first list of metabolites in glioblastoma tissues that are sensitive to post-surgical freezing delays and offer the opportunity to define individualized fold change thresholds for future comparative metabolomic studies. Conclusion: More researchers should take these pre-analytical factors into consideration when analyzing metabolomic data. We present a strategy for how to work with metabolites that are sensitive to freezing delays. | ||
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