Calibration curve-free GC-MS method for quantitation of amino and non-amino organic acids in biological samples

Introduction Technological advances in the area of analytical chemistry and the development of state-of-the-art analytical instrumentation have allowed for a shift in the focus from a previously strict targeted approach towards the approach adopted in metabolomics, the essence of which is non-targeted providing an unbiased analysis of metabolites in biological samples. Metabolite profiling methods have served their purpose in providing descriptive information about biological systems through qualitative and relative semi-quantitative data. However, quantitative characterization of a system cannot be fully accomplished without using absolute metabolite concentrations, an area which is lacking in most current metabolomics platforms. Objectives The objective of this work was to develop a calibration-curve free method for quantitation of amino and non-amino organic acids in biological samples. Methods We developed a novel calibration curve-free GC-MS method based on isotope-coded derivatization for absolute non-targeted quantification of polar metabolites. A new R-based package MetabQ was created for automated data processing of GC-MS data files performing data extraction and calculation of absolute metabolite values. The new method requires metabolite response factors which should be calculated only once for each equipment, and was validated for metabolite quantification of different biological matrices. Results The method showed high reproducibility and accuracy, and does not require the use of calibration curves using standards to be analyzed in parallel with every sample batch. However, there is a small group of metabolites where their quantification required additional steps of correction due to their chemical instability. The introduced R package significantly increased the throughput in the data analysis process, extensively reducing the time required to perform the task manually. Conclusion Our novel approach gives the potential to identify and quantify hundreds of metabolites, far exceeding the capabilities of any absolute quantitative targeted metabolite analysis, limited only by the size of the mass spectral library.