Chromatography
Automated Sample Preparation: The Missing Hyphen to Hypernation
Camilla Liscio, Anatune Ltd
In 1980 the term ‘hyphenation’ was fi rstly coined by Hirschfeld [1] to denote the on-line combination of a chromatographic separation and one or more spectroscopic/spectrometric detection techniques. The marriage, to exploit the advantages of both, was driven by the constant need within the analytical community to push the boundaries of selectivity and sensitivity to tackle the continuously more challenging and demanding analytical applications.
Nearly 40 years down the line, hyphenated analytical techniques are now the favoured approach for complex qualitative and quantitative analytical problems. Why is that the case?
The preference for hyphenation can be pinned down to three key advantages this approach can provide the analyst with:
• Convenience: the need for sample fractionation is overcome, reducing instrument and operator time
• Control: detection can be tailored to the chromatographic separation for instance by use of splitter plates (GC) or divert valves (LC)
• Consistency: full automation of online sample injection and sample introduction provides better performances when compared to manual handling
With hyphenated techniques such as GC-MS and LC-MS well-established techniques of choice, special attention is now devoted to systems in which multiple hyphenation- also known as hypernation- is an integral part of the whole setup, as discussed by Wilson and Brinkman in 2003 [2]. It’s within the perspective of hypernation that the on-line automation of sample preparation fi nds its perfect scope, as an additional yet extremely valuable hyphen. In fact, sample preparation is an essential part of any analytical workfl ow and despite the excellent performances of the latest available hyphenated techniques, superior quality data for complex matrices can only be achieved when counting on a robust and reproducible sample preparation.
The added values of hyphenated Automated Sample Preparation are the very same which drive the choice of hyphenation in the fi rst place: Convenience, Control and Consistency.
Why is Automated Sample Preparation convenient?
Let’s take as an example derivatisation and GC-MS analysis for untargeted metabolomics. Metabolomics investigates the metabolite composition of a certain sample, and this metabolic profi ling can be carried out by two main approaches: targeted and untargeted. Targeted metabolomics focuses exclusively on the quantifi cation of predefi ned sets of metabolites whilst untargeted metabolomics provides the global metabolic fi ngerprint. In GC-MS metabolomics, MOX-TMS is the most adopted derivatisation method. It’s a two-step derivatisation. The preliminary methoximation step (MOX) allows reaction of carbonyl groups to form oxime. This step is crucial to prevent processes such as cyclisation of reducing sugars, formation of keto-enol tautomers and decarboxylation, with the fi nal scope to reduce the complexity of the chromatograms due to presence of multiple peaks per metabolite. The successive silylation reaction (TMS) replaces active hydrogens present in the molecule of interest with an alkyl silyl groups. Silylation is not only useful to improve GC chromatographic properties of the analytes, but it also enhances mass spectrometric properties providing diagnostic fragmentation patterns for structure elucidation. The standard workfl ow requires 2 hours of sample preparation plus GC run times between 40 min and 1 hr. Noteworthy, TMS derivatives are sensitive to moisture, and they tend to degrade overtime, so it is quite crucial to run the samples freshly derivatised. When using automated sample preparation, it is possible to multitask. Sample preparation for each sample is performed immediately preceding the GC-MS injection whilst the previous sample is running. Figure 1 shows the timeline for the preparation and analysis of 10 samples when using hyphenated and automated sample preparation.
The multi-coloured bands represent the sample preparation, and the light orange bands the GC run-time. Automated sample preparation not only offers a very convenient option, signifi cantly eliminating operator downtime, but also ensures that freshly derivatised samples are analysed promptly reducing the risk of degradation.
Figure 1. The timeline for the preparation and analysis of 10 samples when using hyphenated and automated sample preparation.
Full automation of an untargeted approach to investigate the metabolic diversity of fungal endophytes was performed in support of a grant application for a customer. Six replicates of three species of endophytic fungi (3, 11 and 21) were analysed plus 3 procedural blanks for a total of 21 samples. Deconvoluted data were processed using several statistical tools to identify key markers for the three fungi species. Figure 2 shows the obtained Principal Component Analysis Plot, an effective visual way to explore the variance in the data set and support identifi cation of patterns. The three fungi species and the procedural blanks separated nicely in different tight clusters.
Figure 2. The obtained Principal Component Analysis Plot.
INTERNATIONAL LABMATE - JULY 2022
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