Abstract
Introduction
Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.
Objectives
In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.
Methods
Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography–mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30–120 and 30–60 min of delayed freezing were analyzed.
Results
190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.
Conclusion
Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.
Similar content being viewed by others
Data Availability
The data that support the findings of this study is available from the corresponding author upon reasonable request.
References
Brinkman, D., Callanan, D., O’Shea, R., Jawad, H., Feeley, L., & Sheahan, P. (2020). Impact of 3 mm margin on risk of recurrence and survival in oral cancer. Oral Oncology, 110, 104883
Cui, L., Lu, H., & Lee, Y. H. (2018). Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spectrometry Reviews, 37, 772–792
Dietmair, S., Timmins, N. E., Gray, P. P., Nielsen, L. K., & Krömer, J. O. (2010). Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol. Analytical Biochemistry, 404, 155–164
Drammeh, B. S., Schleicher, R. L., Pfeiffer, C. M., Jain, R. B., Zhang, M., & Nguyen, P. H. (2008). Effects of delayed sample processing and freezing on serum concentrations of selected nutritional indicators. Clinical Chemistry, 54, 1883–1891
Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., Haselden, J. N., Nicholls, A. W., Wilson, I. D., Kell, D. B., & Goodacre, R. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083
Eltzschig, H. K., & Eckle, T. (2011). Ischemia and reperfusion–from mechanism to translation. Nature Medicine, 17, 1391–1401
Espina, V., Edmiston, K. H., Heiby, M., Pierobon, M., Sciro, M., Merritt, B., Banks, S., Deng, J., VanMeter, A. J., Geho, D. H., Pastore, L., Sennesh, J., Petricoin, E. F., & Liotta, L. A. (2008). A portrait of tissue phosphoprotein stability in the clinical tissue procurement process. Molecular and Cellular Proteomics, 7, 1998–2018
Fingerhut, R., Ensenauer, R., Röschinger, W., Arnecke, R., Olgemöller, B., & Roscher, A. A. (2009). Stability of acylcarnitines and free carnitine in dried blood samples: Implications for retrospective diagnosis of inborn errors of metabolism and neonatal screening for carnitine transporter deficiency. Analytical Chemistry, 81, 3571–3575
Futschik, M. E., & Carlisle, B. (2005). Noise-robust soft clustering of gene expression time-course data. Journal Of Bioinformatics And Computational Biology, 3, 965–988
Gao, Y., Liu, X., Tang, B., Li, C., Kou, Z., Li, L., Liu, W., Wu, Y., Kou, X., Li, J., Zhao, Y., Yin, J., Wang, H., Chen, S., Liao, L., & Gao, S. (2017). Protein expression landscape of mouse embryos during pre-implantation development. Cell Rep, 21, 3957–3969.
González-Domínguez, R., González-Domínguez, Á., Sayago, A., & Fernández-Recamales, Á. (2020). Recommendations and best practices for standardizing the pre-analytical processing of blood and urine samples in metabolomics. Metabolites, 10(6), 229.
Gonzalez-Riano, C., Garcia, A., & Barbas, C. (2016). Metabolomics studies in brain tissue: A review. Journal Of Pharmaceutical And Biomedical Analysis, 130, 141–168
Gündisch, S., Grundner-Culemann, K., Wolff, C., Schott, C., Reischauer, B., Machatti, M., Groelz, D., Schaab, C., Tebbe, A., & Becker, K. F. (2013). Delayed times to tissue fixation result in unpredictable global phosphoproteome changes. Journal Of Proteome Research, 12, 4424–4434
Haijes, H. A., Willemse, E. A. J., Gerrits, J., van der Flier, W. M., Teunissen, C. E., Verhoeven-Duif, N. M., & Jans, J. J. M. (2019). Assessing the pre-analytical stability of small-molecule metabolites in cerebrospinal fluid using direct-infusion metabolomics. Metabolites, 9(10), 236.
Haukaas, T. H., Moestue, S. A., Vettukattil, R., Sitter, B., Lamichhane, S., Segura, R., Giskeødegård, G. F., & Bathen, T. F. (2016). Impact of freezing delay time on tissue samples for metabolomic studies. Frontiers In Oncology, 6, 17.
Hustad, S., Eussen, S., Midttun, Ø., Ulvik, A., van de Kant, P. M., Mørkrid, L., Gislefoss, R., & Ueland, P. M. (2012). Kinetic modeling of storage effects on biomarkers related to B vitamin status and one-carbon metabolism. Clinical Chemistry, 58, 402–410
Jackson, D., Rowlinson, R. A., Eaton, C. K., Nickson, J. A., Wilson, I. D., Mills, J. D., Wilkinson, R. W., & Tonge, R. P. (2006). Prostatic tissue protein alterations due to delayed time to freezing. Proteomics, 6, 3901–3908
Jang, C., Chen, L., & Rabinowitz, J. D. (2018). Metabolomics and isotope tracing. Cell, 173, 822–837.
Kain, J. J., Birkeland, A. C., Udayakumar, N., Morlandt, A. B., Stevens, T. M., Carroll, W. R., Rosenthal, E. L., & Warram, J. M. (2020). Surgical margins in oral cavity squamous cell carcinoma: Current practices and future directions. The Laryngoscope, 130, 128–138
Kinross, J. M., Holmes, E., Darzi, A. W., & Nicholson, J. K. (2011). Metabolic phenotyping for monitoring surgical patients. Lancet, 377, 1817–1819
Kumar, L. and M, E.F (2007). Mfuzz: a software package for soft clustering of microarray data. Bioinformation, 2, 5–7
Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. Bmc Bioinformatics, 9, 559
Lehmann, R. (2015). Preanalytics: what can metabolomics learn from clinical chemistry? Bioanalysis, 7, 927–930
Lu, W., Wang, L., Chen, L., Hui, S., & Rabinowitz, J. D. (2018). Extraction and quantitation of nicotinamide adenine dinucleotide redox cofactors. Antioxidants & Redox Signaling, 28, 167–179.
Maślanka, K., Smoleńska-Sym, G., Michur, H., Wróbel, A., Lachert, E., & Brojer, E. (2012). Lysophosphatidylcholines: Bioactive lipids generated during storage of blood components. Archivum Immunolgiae Et Therapiae Experimentalis, 60, 55–60.
Minami, Y., Kasukawa, T., Kakazu, Y., Iigo, M., Sugimoto, M., Ikeda, S., Yasui, A., van der Horst, G. T., Soga, T., & Ueda, H. R. (2009). Measurement of internal body time by blood metabolomics. Proc Natl Acad Sci U S A, 106, 9890–9895
Mock, A., Rapp, C., Warta, R., Abdollahi, A., Jäger, D., Sakowitz, O., Brors, B., von Deimling, A., Jungk, C., Unterberg, A., & Herold-Mende, C. (2019). Impact of post-surgical freezing delay on brain tumor metabolomics. Metabolomics, 15, 78
Opstad, K. S., Bell, B. A., Griffiths, J. R., & Howe, F. A. (2008). An assessment of the effects of sample ischaemia and spinning time on the metabolic profile of brain tumour biopsy specimens as determined by high-resolution magic angle spinning (1)H NMR. Nmr In Biomedicine, 21, 1138–1147
Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019). Identification of bioactive metabolites using activity metabolomics. Nature Reviews Molecular Cell Biology, 20, 353–367
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43, e47
Rosenling, T., Slim, C. L., Christin, C., Coulier, L., Shi, S., Stoop, M. P., Bosman, J., Suits, F., Horvatovich, P. L., Stockhofe-Zurwieden, N., Vreeken, R., Hankemeier, T., van Gool, A. J., Luider, T. M., & Bischoff, R. (2009). The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF). Journal Of Proteome Research, 8, 5511–5522
Salek, R. M., Neumann, S., Schober, D., Hummel, J., Billiau, K., Kopka, J., Correa, E., Reijmers, T., Rosato, A., Tenori, L., Turano, P., Marin, S., Deborde, C., Jacob, D., Rolin, D., Dartigues, B., Conesa, P., Haug, K., Rocca-Serra, P., … Steinbeck, C. (2015). COordination of Standards in MetabOlomicS (COSMOS): Facilitating integrated metabolomics data access. Metabolomics, 11, 1587–1597.
Saoi, M., & Britz-McKibbin, P. (2021). New advances in tissue metabolomics: A review. Metabolites 11(10), 672.
Sarto, C., Valsecchi, C., & Mocarelli, P. (2002). Renal cell carcinoma: Handling and treatment. Proteomics, 2, 1627–1629.
Schneeberger, S. (2018). Life of a liver awaiting transplantation. Nature, 557, 40–41
Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., Quan, S., Zhang, F., Sun, R., Qian, L., Ge, W., Liu, W., Liang, S., Chen, H., Zhang, Y., Li, J., Xu, J., He, Z., Chen, B., … Chen, H. (2020). Proteomic and metabolomic characterization of COVID-19 patient Sera. Cell, 182, 59-72e15.
Smyth, G. K. (2013). Limma: Linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconductor
Song, J. W., Lam, S. M., Fan, X., Cao, W. J., Wang, S. Y., Tian, H., Chua, G. H., Zhang, C., Meng, F. P., Xu, Z., Fu, J. L., Huang, L., Xia, P., Yang, T., Zhang, S., Li, B., Jiang, T. J., Wang, R., Wang, Z., … Shui, G. (2020). Omics-driven systems interrogation of metabolic dysregulation in COVID-19 pathogenesis. Cell Metab, 32, 188-202e5.
Timms, J. F., Arslan-Low, E., Gentry-Maharaj, A., Luo, Z., T’Jampens, D., Podust, V. N., Ford, J., Fung, E. T., Gammerman, A., Jacobs, I., & Menon, U. (2007). Preanalytic influence of sample handling on SELDI-TOF serum protein profiles. Clinical Chemistry, 53, 645–656
van Keulen, S., Nishio, N., Birkeland, A., Fakurnejad, S., Martin, B., Forouzanfar, T., Cunanan, K., Colevas, A. D., & Rosenthal, E. (2019). The sentinel margin: Intraoperative ex vivo specimen mapping using relative fluorescence intensity. Clinical Cancer Research, 25, 4656–4662.
Wang, X., Gu, H., Palma-Duran, S. A., Fierro, A., Jasbi, P., Shi, X., Bresette, W., & Tasevska, N. (2019). Influence of storage conditions and preservatives on metabolite fingerprints in urine. Metabolites, 9(10), 203.
Want, E. J., Masson, P., Michopoulos, F., Wilson, I. D., Theodoridis, G., Plumb, R. S., Shockcor, J., Loftus, N., Holmes, E., & Nicholson, J. K. (2013). Global metabolic profiling of animal and human tissues via UPLC-MS. Nature Protocols, 8, 17–32
Want, E. J., Wilson, I. D., Gika, H., Theodoridis, G., Plumb, R. S., Shockcor, J., Holmes, E., & Nicholson, J. K. (2010). Global metabolic profiling procedures for urine using UPLC-MS. Nature Protocols, 5, 1005–1018
Williams, M. D. (2016). Determining adequate margins in head and neck cancers: Practice and continued challenges. Current Oncology Reports, 18, 54.
Winder, C. L., Dunn, W. B., Schuler, S., Broadhurst, D., Jarvis, R., Stephens, G. M., & Goodacre, R. (2008). Global metabolic profiling of Escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites. Analytical Chemistry, 80, 2939–2948
Wollenberger, A., Ristau, O., & Schoffa, G. (1960). Eine einfache Technik der extrem schnellen Abkühlung größerer Gewebestücke. Pflüger’s Archiv für die gesamte Physiologie des Menschen und der Tiere, 270, 399–412
Yang, B., Li, M., Tang, W., Liu, W., Zhang, S., Chen, L., & Xia, J. (2018). Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nature Communications, 9, 678
Yang, W., Chen, Y., Xi, C., Zhang, R., Song, Y., Zhan, Q., Bi, X., & Abliz, Z. (2013). Liquid chromatography-tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites’ stability on reliability of potential biomarkers. Analytical Chemistry, 85, 2606–2610
Yang, X. H., Ding, L., Fu, Y., Chen, S., Zhang, L., Zhang, X. X., Huang, X. F., Lu, Z. Y., Ni, Y. H., & Hu, Q. G. (2019). p53-positive expression in dysplastic surgical margins is a predictor of tumor recurrence in patients with early oral squamous cell carcinoma. Cancer Manag Res, 11, 1465–1472
Yang, X. H., Jing, Y., Wang, S., Ding, F., Zhang, X. X., Chen, S., Zhang, L., Hu, Q. G., & Ni, Y. H. (2020). Integrated non-targeted and targeted metabolomics uncovers amino acid markers of oral squamous cell carcinoma. Frontiers In Oncology, 10, 426.
Acknowledgements
The authors gratefully acknowledge Guowen Sun and Shu Xia for kind assistance in providing residual patient samples.
Funding
This research was funded by National Natural Science Foundation of China (82173159, 81902759, 82002865, 82173380), the Key Research and Development Projects in Jiangsu Province (BE2020628), Doctoral Program for Entrepreneurship and Innovation of Jiangsu Province (JSSCBS20211596), and Nanjing Medical Science and Technique Development Foundation (YKK19091, YKK20151).
Author information
Authors and Affiliations
Contributions
In this study, Shuai Wang designed the study and wrote the manuscript; Yawei Sun and Yan Wu collected data; Tao Zeng analyzed data; Liang Ding, Xiaoxin Zhang and Xihu Yang reviewed the manuscript; Lei Zhang, Xiaofeng Huang and Huiling Li provided study materials; Yanhong Ni and Qingang Hu supervised the study and reviewed the manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This study was approved by the Medical Ethics Committee of Nanjing Stomatology Hospital, Medical School of Nanjing University (NJSH-2021NL-025).
Consent to participate
The patients provided written consent for participate in research.
Consent for publication
The patients provided written informed con-sent for the publication of any associated data.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, S., Sun, Y., Zeng, T. et al. Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples. Metabolomics 18, 82 (2022). https://doi.org/10.1007/s11306-022-01943-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11306-022-01943-2