Application of Hadamard Transform Ion Mobility Mass Spectrometry to Global Metabolomics

Abstract

Conventional Ion mobility mass spectrometry (IMMS) provides rapid separation and detection of complex mixtures. It is merging as a powerful analytical platform for the field of metabolomics but is limited in throughput. Global metabolomics aims at comprehensive measurement for all metabolites and presents challenges in analytical technique. The work describe herein evaluates the capability of hadamard transform ion mobility mass spectrometry (HT-IMMS) for comprehensive metabolomics analysis with high throughput and high resolving power. The work also presents a number of applications of global metabolomics regarding to biological systems including human blood, rat brain tissue and mice plasma. HT-IMMS has been developed by superimposing a Hadamard transform sequence on the ion gate. This development provides a 50% duty cycle while retaining high IMS resolving power. Coupling rapid chromatographic separation prior to HT-IMMS enables the detection of more metabolite features compared to conventional direct infusion IMMS. Global metabolomic applications of HT-IMMS were extended in this work by developing general procedure for sample analysis, metabolite identification, data processing and statistical analysis. The major findings from this work include: 1) HPLC couple with HT-IMMS provides comprehensive metabolomics analysis within 2 - 3 minutes; 2) ambient pressure IMMS has high resolving power and allows isomeric separations, providing accurate assessment of critical biomarkers without the interference of their isomers; 3) the structural information generated from IMMS analysis complements the MS detection and helps metabolite identifications; 4) principle component analysis yields pattern recognition that can reveal the differences between different metabolic states; 5) biomarkers selection requires the combination of multivariate analysis and univariate analysis; 6) IMMS data pre-processing, including normalization and the evaluation of censored data, improves statistical analysis. HT-IMMS is a natural fit for analyzing complex mixtures.