Se Pu. 2020 Sep 8;38(9):1013-1021. doi: 10.3724/SP.J.1123.2020.02025.
ABSTRACT
Capillary electrophoresis-mass spectrometry (CE-MS) has the advantages of higher sensitivity, higher efficiency, and less sample consumption. Moreover, it possesses obvious advantages during the analysis of strongly charged and highly polar samples. CE-MS has been widely applied in life sciences, medicine, and pharmacology. In the past ten years, the main factors affecting its application were system stability, reproducibility, and data accuracy. In order to solve the existing problems of CE-MS, researchers have invested significant effort in technology innovation to further expand CE-MS application. In the fields of medicine and analytical chemistry, substantial research indicates that CE-MS is superior compared to other metabolomic and proteomic approaches. This study aims at reviewing the latest methods and applications developed in the fields of medicine and analytical chemistry since 2015. Furthermore, it also aims at enhancing the technology development-related application value of CE-MS and serving as a reference for future development. Further development of the CE-MS technology is discussed from the aspects of coating-sample interaction, interface types, and data processing methods. Concerning the coating types, neutral coatings had been applied extensively in CE-MS and there should be no limitation to the charge of the analyte. The coating decreased sample adsorption on the inner wall by covering the surface charge, greatly reducing the electroosmotic flow (EOF). A charged capillary coating could modify such an EOF direction. The cationic coating could reduce the hydrophobic interaction between the sample and the capillary column, resulting in higher EOF. If it is applied to the sheathless interface, the resolution could be improved by extending the capillary length. Anionic coatings are predominant among the anionic compounds, shortening the separation time by reducing the interaction between the anionic compounds and the capillary. The coating type should be chosen relative to the analyte characteristics. Concerning the interface technology, all interfaces should be simple, practical, and non-dependent on sheath liquid and background electrolytes. As far as data processing methods are concerned, it is necessary to design and develop a practical method for span space data comparison and processing. The optimized experimental conditions have effectively improved separation efficiency and data comparison analysis. Furthermore, they established a solid foundation for its application development. CE-MS analysis of complex samples in the fields of metabolomics and proteomics (e. g., of tissues, cells, body fluids, etc.) could provide a visualization method for future clinical analysis. It contributes to the development of cancer pathological analysis, drug development, disease surveillance, etc. The characteristic analysis of small molecule metabolites and protein biomarkers directly reflects on enzymatic activity in the biological systems. It could be associated with the development of various diseases/complications. Omics analysis also has an important directive to disease detection and surveillance with obvious advantages in disease diagnosis, staged treatment, drug development, and patient treatment progress. CE-MS is useful in detecting complications and promoting personalized medicine. It provides technical support for future clinical developments. In addition to a comprehensive review of the recent advances of CE-MS research, this paper also indicates the development directions of CE-MS. In order to avoid the problem of omics analysis and obtain the optimized analysis results, future analysis should be improved from the following three aspects:(i) The analysis conditions should be optimized concerning sample preparation methods and separation techniques. (ii) The analytic techniques should be supported to adjust to capillary coating and interface technology. (iii) New ideas should be developed in the fields of clinical research and statistical analysis.
PMID:34213267 | DOI:10.3724/SP.J.1123.2020.02025
