Sample preparation for polar metabolites in bioanalysis

Drouin, Nicolas; Rudaz, Serge; Schappler, Julie

doi:10.1039/c7an01333g

Cited by 51 publications

(15 citation statements)

References 49 publications

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“…All these metabolites are very polar compounds with logP between −4.49 and −0.93 (Table 1). Due to their very low lipophilicity, these highly polar molecules are difficult to extract using conventional sample preparation methods such as solid-phase extraction or reversed-phase SPE [9], which are based on the partition coefficients of analytes in two-phase systems. Indeed, only PP has been reported as efficient sample pre-treatment for these class metabolites so far [31,32].…”

Section: Resultsmentioning

confidence: 99%

“…PP is typically used in metabolomics, especially in large-scale studies where high throughput is essential. Fast analytical techniques are also required, such as fast LC-MS, but may lead to strong ME, typically for poorly-retained compounds and particularly in combination with straightforward sample pre-treatments [9,33]. This ME issue highlights the needs for novel sample preparation approaches adapted to the extraction of highly polar compounds.…”

Section: Resultsmentioning

confidence: 99%

“…The most common practice in metabolomics is to remove proteins by protein precipitation [7] or ultrafiltration [8]. However, these two approaches cannot separate the compounds of interest from interferences such as salts or phospholipids [9,10]. To do this, more complex sample preparation approaches, e.g., liquid-liquid extraction or reversed phase solid phase extraction are often considered.…”

Section: Introductionmentioning

confidence: 99%

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Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

et al. 2019

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Cardiovascular diseases (CVDs) represent a major concern in today's society, with more than 17.5 million deaths reported annually worldwide. Recently, five metabolites related to the gut metabolism of phospholipids were identified as promising predictive biomarker candidates for CVD. Validation of those biomarker candidates is crucial for applications to the clinic, showing the need for high-throughput analysis of large numbers of samples. These five compounds, trimethylamine N-oxide (TMAO), choline, betaine, l-carnitine, and deoxy-l-carnitine (4-trimethylammoniobutanoic acid), are highly polar compounds and show poor retention on conventional reversed phase chromatography, which can lead to strong matrix effects when using mass spectrometry detection, especially when high-throughput analysis approaches are used with limited separation of analytes from interferences. In order to reduce the potential matrix effects, we propose a novel fast parallel electromembrane extraction (Pa-EME) method for the analysis of these metabolites in plasma samples. The evaluation of Pa-EME parameters was performed using multi segment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). Recoveries up to 100% were achieved, with variability as low as 2%. Overall, this study highlights the necessity of protein precipitation prior to EME for the extraction of highly polar compounds. The developed Pa-EME method was evaluated in terms of concentration range and response function, as well as matrix effects using fast-LC-MS/MS. Finally, the developed workflow was compared to conventional sample pre-treatment, i.e., protein precipitation using methanol, and fast-LC-MS/MS. Data show very strong correlations between both workflows, highlighting the great potential of Pa-EME for high-throughput biological applications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

et al. 2019

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“…Crucial to obtaining high quality metabolomics data is how the samples are prepared. There are many excellent papers in the literature concerning sample preparation for metabolomics studies [22][23][24][25][26][27][28][29] and individual methodologies are beyond the scope of this review. However, it is important to consider some key factors when designing the sample preparation approaches most appropriate for the biofluid or tissue of interest.…”

Section: Sample Preparation Approachesmentioning

confidence: 99%

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Ivanišević

Want

2019

Metabolites

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Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

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“…For untargeted cellular metabolome characterization, sample preparation must be non-discriminative and precise. The protocol must also maintain sample stability, because intracellular metabolites can be rapidly degraded or metabolized by enzymatic reactions, and must minimize contaminants in both sample and detector (Drouin et al 2018 ; Pinu et al 2017 ). Precise results from single-time-point analyses or kinetic studies are necessary in order to obtain an accurate snapshot of the metabolome.…”

Section: Introductionmentioning

confidence: 99%

Impact of sample preparation upon intracellular metabolite measurements in 3D cell culture systems

et al. 2019

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Introduction Interest in cell culture metabolomics has increased greatly in recent years because of its many potential applications and advantages (e.g., in toxicology). The first critical step for exploring the cellular metabolome is sample preparation. For metabolomics studies, an ideal sample preparation would extract a maximum number of metabolites and would enable reproducible, accurate analysis of a large number of samples and replicates. In addition, it would provide consistent results across several studies over a relatively long time frame. Objectives This study was conducted to evaluate the impact of sample preparation strategies on monitoring intracellular metabolite responses, highlighting the potential critical step(s) in order to finally improve the quality of metabolomics studies. Methods The sample preparation strategies were evaluated by calculating the sample preparation effect, matrix factor, and process efficiency (PE) for 16 tobacco exposition-related metabolites, including nicotine, nicotine-derived nitrosamine ketone, their major metabolites, and glutathione, using isotopically-labelled internal standards. Samples were analyzed by liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS). Results A sample drying step increased losses or variability for some selected metabolites. By avoiding evaporation, good sample preparation recovery was obtained for these compounds. For some metabolites, the cell or culture type impacted PE and matrix factor. Conclusion In our sample preparation protocol, the drying–reconstitution step was identified as the main cause of metabolite losses or increased data variability during metabolomics analysis by LC-HRMS. Furthermore, PE was affected by the type of matrix. Isotopologue internal standards fully compensate losses or enhancements.

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Sample preparation for polar metabolites in bioanalysis

Cited by 51 publications

References 49 publications

Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Impact of sample preparation upon intracellular metabolite measurements in 3D cell culture systems

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