it is anticipated that the same strategy could also be applied to anionic metabolic profiling. Overall, this study, which could be regarded as unique given its design, clearly exemplified CE-MS’s actual reproducibility in metabolomics, i.e., effective electrophoretic mobility can be used as a robust parameter in metabolomics.

It should be noted that CE-MS has been used for more than two decades to investigate urinary peptides as biomarkers for the diagnosis and prognosis of (complex) diseases [6]. Until now, the CE-MS peptidomics approach was employed for the comparable analysis of more than 70000 urine samples and has previously been qualified for prognosis of progression and outcome in large-scale prospective and longitudinal clinical studies [7, 8]. In Germany, the CE-MS peptidomics approach has been registered now as an in vitro diagnostics for a number of clinical applications [9].

Misconception 2: CE-MS is technically challenging

The coupling of CE to MS is often perceived as a technically challenging endeavor, notably when compared to LC-MS or GC-MS [10]. The lack of standard operating procedures and data workflows that are fit for purpose may also have hindered the widespread use of CE-MS in metabolomics despite new advances in sample throughput and quality control [11, 12]. For cationic metabolic profiling, well-established CE-MS protocols have been developed over the past decades and employed to analyse large sample cohorts, such as the Tsuruoka Metabolomics Cohort Study, comprised of more than 8000 human plasma samples [13]. The CE-MS approach utilised for this metabolomics study was provided by Human Metabolome Technologies (HMT), a Japan-based biotechnology company founded by Soga and coworkers at Keio University. They developed the first CE-MS methods for metabolomics [14]. Today, the CE-MS approach of HMT for cationic metabolic profiling can be used in a robust way and currently employed by various research groups. However, the development of a robust CE-MS approach for anionic metabolic profiling is still an ongoing development. For example, Yamamoto et al. recently showed that commonly employed ammonium-based BGEs with a pH above 9.0 could contribute to incidental capillary fractures via irreversible aminolysis of the outer polyimide coating [15]. The study revealed that polyimide

aminolysis could be simply prevented by employing weakly ammonium-based BGEs with a pH below 9.0.The previous example, and some other recent work, clearly show the effort and willingness of the CE-MS community to highlight relevant technological and practical aspects for metabolomics studies in protocol papers [16- 20]. An important recent trend in this context is sharing key experimental procedures and best practices via peer-reviewed video articles [21-24], and it is anticipated that such work will encourage researchers to actively consider CE-MS for metabolomics studies. Worthwhile to mention in this context is that the recent CE-MS Metabo-ring trial clearly revealed that this approach can be used in a rather straightforward way even by groups without having (any) experience with metabolomics research [5].

Misconception 3:

CE-MS not suited for trace- sensitive metabolomics

The prerequisite of low sample volumes for CE-MS analysis makes it an attractive

tool for the analysis of volume-limited or scarcely available samples. However, due to the limited loading capacity of CE and concentration-sensitive detection of ESI-MS, the technique is often perceived as non-suitable for trace metabolomics. Nevertheless, the loading capacity of CE has been addressed effectively by the use of in-capillary sample preconcentration techniques. Recently, Wells et al. applied preconcentration based on electrokinetic supercharging for neurotransmitter analysis in volume-limited tissue samples from rat brain tissue whole Drosophila [25], thereby reaching detection limits as low as 10 picomolar. In another study, van Mever et al. optimised the use of dynamic pH junction stacking for rat brian microdialysis samples [26], thereby showing the compatibility of the stacking procedure with the high-salt matrix of the microdialysate (Figure 1). Detection limits were in the low nanomolar range for amino acid neurotransmitters, showing its potential for trace-sensitive brain metabolomics studies.

Figure 1. Extracted-ion electropherograms obtained by CE-MS for the analysis of endogenous metabolites in basal rat brain microdialysate. Separation conditions: BGE, 10% acetic acid; sample injection volume 291 nL; ammonium hydroxide (concentration: 5%) pre-injection volume, 12 nL. Adapted from [26] with permission.

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