Table1_Comparison of extraction methods for intracellular metabolomics of human tissues.csv

Analyses of metabolic compounds inside cells or tissues provide high information content since they represent the endpoint of biological information flow and are a snapshot of the integration of many regulatory processes. However, quantification of the abundance of metabolites requires their careful extraction. We present a comprehensive study comparing ten extraction protocols in four human sample types (liver tissue, bone marrow, HL60, and HEK cells) aiming to detect and quantify up to 630 metabolites of different chemical classes. We show that the extraction efficiency and repeatability are highly variable across protocols, tissues, and chemical classes of metabolites. We used different quality metrics including the limit of detection and variability between replicates as well as the sum of concentrations as a global estimate of analytical repeatability of the extraction. The coverage of extracted metabolites depends on the used solvents, which has implications for the design of measurements of different sample types and metabolic compounds of interest. The benchmark dataset can be explored in an easy-to-use, interactive, and flexible online resource (R/shiny app MetaboExtract: http://www.metaboextract.shiny.dkfz.de) for context-specific selection of the optimal extraction method. Furthermore, data processing and conversion functionality underlying the shiny app are accessible as an R package: https://cran.r-project.org/package=MetAlyzer.

对细胞或组织内代谢化合物的分析可提供丰富的信息内涵，因为它们是生物信息流的终点，也是多种调控过程整合状态的快照。然而，要对代谢物的丰度进行定量，需先对其进行严谨的提取操作。本研究开展了一项系统性对比分析，针对四种人类样本类型（肝组织、骨髓、HL60细胞、HEK细胞）的10种提取方案进行比对，目标是检测并定量多达630种隶属于不同化学类别的代谢物。研究结果表明，提取效率与重复性在不同提取方案、样本组织以及代谢物化学类别间均存在显著差异。本研究采用了多种质量评估指标，包括检测限、重复样本间的变异度以及浓度总和，以此作为提取分析重复性的全局评估依据。提取得到的代谢物覆盖度取决于所使用的溶剂，这为不同样本类型与目标代谢物的检测实验设计提供了参考依据。该基准数据集可通过一款易用、交互式且灵活的在线工具（R语言Shiny应用MetaboExtract：http://www.metaboextract.shiny.dkfz.de）进行探索，以实现针对具体实验场景的最优提取方法选择。此外，该Shiny应用所依托的数据处理与转换功能，可通过一款R包获取：https://cran.r-project.org/package=MetAlyzer。