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10 May / June 2021


Additionally, it should be noted that in both previous discussed works, a conventional sheath-liquid ESI CE-MS interface was used. When using a sheathless CE-MS interface, a 10-fold improvement of detection sensitivity could be achieved. Thus, CE-MS could definitely yield comparable detection limits as compared to conventional LC-MS methods, with the main difference being that for CE only a volume of about 30-300 nL is injected, while 5-10 µL in the sample vial is sufficient for injection, whereas typically 300-10000 nL is injected in a conventional LC-MS method. This makes using CE-MS beneficial when the sample amount is very limited, such as for example is the case for single cell analysis. Recently, the potential of CE-MS for single cell analysis has been reported repeatedly [27] and Lombard-Banek et al. reported a CE-MS method that allowed in vivo single-cell proteomics and metabolomics in the same single cell in chordate embryos using X. laevis [28]. With a custom-build CE-MS system, quantitative proteo-metabolomic differences were observed between cells at the cleavage stage. This work shows the potential of CE-MS for trace-sensitive metabolomics, including the ability to study cell heterogeneity in future metabolomics studies.


Misconception 4: CE-MS not suited for high-throughput metabolomics


High-throughput analysis of dozens, hundreds or even thousands of biological samples is gaining importance for metabolomics studies. Especially there is a requirement for fast and robust metabolomics workflows for volume- restricted samples. A notable improvement in CE-MS analysis strategies is the multi- segment injection (MSI) approach [29], developed in 2013 by the research group of Britz-Mckibbin. MSI allows for serial injections of seven or more samples within a single capillary, thereby significantly improving the sample throughput. Furthermore, when including a quality control sample, stringent quality control and batch correction can be performed during the same run. In the last few years, MSI-CE- MS has been an efficient analysis tool for metabolomics studies in various sample types and CE-MS methodologies [30-32].


Recently, MSI-CE-MS potential for large- scale metabolomics was shown in a study including more than a thousand serum samples [20]. In this study, metabolic profiles


in serum samples from pregnant woman all over Canada were analysed for 7 months using standardised methodology and data treatment. The results showed acceptable intermediate precision for a range of metabolites. Overall, this work clearly demonstrated the value of MSI-CE-MS for executing in a robust way large-scale high throughput metabolomics studies, including successful correction for long-term signal drift and inter-batch variations.


Misconception 5: CE-MS lacks versatility for metabolomics


To date, the vast majority of CE-MS-based metabolomics reports have focused on the analysis of polar ionogenic metabolites using an aqueous buffer system that may include small amount of organic solvent modifier (5-10% v/v) using CZE as the main separation mode. CZE-MS is ideal for the profiling of diverse classes of highly polar metabolites (including, organic acids, nucleotides, sugar phosphates) and their intact conjugates (e.g., glycine, sulfate or glucuronide) that are poorly retained on reversed-phase LC or undergo excessive band broadening in hydrophilic interaction LC analyses. Next to CZE, there are other CE separation modes showing potential for metabolomics studies, such as non-aqueous CE (NACE) and micellar electrokinetic chromatography (MEKC). NACE, in which background electrolytes (BGEs) are composed of organic solvents containing volatile salts such as ammonium acetate in a small portion of water, has interesting features for the analysis of apolar and charged compounds. Moreover, the use of high organic solvent-based BGEs may further improve the electrospray ionisation efficiency. Recently, Azab et al. developed a NACE-MS method to profile more than 20 non-esterified fatty acids in human plasma and serum [33]. The NACE-MS approach in conjunction with MSI allowed rapid yet comprehensive profiling of fatty acids in volume-restricted samples.


MEKC, first introduced by Terabe and coworkers [34], can be used for the separation of neutral and charged compounds. In MEKC, ionic micelles or surfactants, often SDS, are used as pseudo- stationary phase and the separation is based on the differential partition of neutral and charged compounds between the micellar phase and the aqueous BGE. Given the nonvolatile nature of SDS, the on-line coupling of MEKC to MS is challenging since the introduction of nonvolatile surfactants


into the MS may decrease ESI efficiency (ion suppression) and contaminate the ion source. To tackle this issue of incompatibility, Moreno-González et al. developed a selective and sensitive MEKC-MS method employing ammonium perfluorooctanoate (APFO) as volatile surfactant for the analysis of amino acids in human urine [35]. This method was further optimised by Prior et al. and used for the enantioselective analysis of amino acids in cerebrospinal fluid [36]. It is anticipated that NACE and MEKC will further expand the role of CE-MS in metabolomics in the coming years.


Acknowledgements


Marlien van Mever and Rawi Ramautar would like to acknowledge the financial support of the Vidi grant scheme of the Netherlands Organization for Scientific Research (NWO Vidi 723.016.003).


Conflict of Interest


The authors have no other relevant affiliations or financial involvement with any organisation or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.


References


1. Miggiels, P., Wouters, B., van Westen, G. J. P., Dubbelman, A.-C., Hankemeier, T., TrAC Trends in Analytical Chemistry 2019, 120, 115323.


2. Buscher, J. M., Czernik, D., Ewald, J. C., Sauer, U., Zamboni, N., Anal Chem 2009, 81, 2135-2143.


3. Gonzalez-Ruiz, V., Gagnebin, Y., Drouin, N., Codesido, S., Rudaz, S., Schappler, J., Electrophoresis 2018, 39, 1222-1232.


4. Drouin, N., Pezzatti, J., Gagnebin, Y., Gonzalez-Ruiz, V., Schappler, J., Rudaz, S., Anal Chim Acta 2018, 1032, 178-187.


5. Drouin, N., van Mever, M., Zhang, W., Tobolkina, E., Ferre, S., Servais, A. C., Gou, M. J., Nyssen, L., Fillet, M., Lageveen-Kammeijer, G. S. M., Nouta, J., Chetwynd, A. J., Lynch, I., Thorn, J. A., Meixner, J., Lossner, C., Taverna, M., Liu, S., Tran, N. T., Francois, Y., Lechner, A., Nehme, R., Al Hamoui Dit Banni, G., Nasreddine, R., Colas, C., Lindner, H. H., Faserl, K., Neususs, C., Nelke, M., Lammerer, S., Perrin, C., Bich-Muracciole, C., Barbas, C., Gonzalvez, A. L., Guttman, A., Szigeti, M., Britz-McKibbin, P.,


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