Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies.
Atiqah ZaidMohammad Sharif KhanDandan YanPhilip John MarriottYong Foo WongPublished in: The Analyst (2022)
The detection of human-derived metabolites as potential diagnostic biomarkers of genetic disorders, metabolic diseases, systemic diseases, and infectious diseases has been much studied in recent years, especially as technical capabilities improve, and statistical procedures are increasingly able to tease critical chemical attributes from complex data sets. Given the complex distribution of human biological matrices, the characterization and/or identification of these chemical entities is technically challenging, and is often confounded by incomplete chromatographic resolution or insufficient discriminatory power of the mass spectrometry (MS) domain. Recently, comprehensive two-dimensional gas chromatography (GC×GC) has evolved into a mature higher separation order technique that offers unprecedented resolving power, which in turn can greatly advantage clinical metabolomics studies via the expansion of metabolite coverage. In this contribution, the current state of knowledge in the development of GC×GC coupled to MS as a high-resolution bioanalytical technique for the analysis of clinical metabolites is reviewed. Selected recent applications (years 2012 to 2021) that emphasize improved GC×GC-MS strategies for clinical human metabolites' detection, identification, and quantitative analysis are described. In addition, we share our perspectives on current challenges and potential future directions of GC×GC in clinical applications.
Keyphrases
- gas chromatography
- mass spectrometry
- liquid chromatography
- high resolution
- tandem mass spectrometry
- high resolution mass spectrometry
- high performance liquid chromatography
- endothelial cells
- gas chromatography mass spectrometry
- capillary electrophoresis
- ms ms
- simultaneous determination
- infectious diseases
- healthcare
- induced pluripotent stem cells
- multiple sclerosis
- machine learning
- risk assessment
- pluripotent stem cells
- bioinformatics analysis
- label free
- dna methylation
- real time pcr
- deep learning
- human health
- genome wide