代谢组学
脂类学
仿形(计算机编程)
生物
生物信息学
计算生物学
计算机科学
操作系统
作者
Patrick Leuthold,Elke Schaeffeler,Stefan Winter,Florian Büttner,Ute Hofmann,Thomas E. Mürdter,Steffen Rausch,Denise Sonntag,Judith Wahrheit,Falko Fend,Jörg Hennenlotter,Jens Bedke,Matthias Schwab,Mathias Haag
标识
DOI:10.1021/acs.jproteome.6b00875
摘要
Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for nontargeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue, which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the nontargeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms, the majority (>90%) revealed Spearman's correlation coefficients ≥0.3, indicating that quantitative results from the nontargeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof of concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and nontumorous material could be demonstrated on the basis of unsupervised statistical analysis.
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