Combination of CE‐MS and advanced <b>chemometric</b> methods for high‐throughput metabolic profiling

In this work, an untargeted metabolomic approach based on sensitive analysis by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry (SPE‐CE‐MS) in combination with multivariate data analysis is proposed as an efficient method for the identification of biomarkers of Huntington's disease (HD) progression in plasma. For this purpose, plasma samples from wild‐type (wt) and HD (R6/1) mice of different ages (8, 12, and 30 weeks), were analyzed by C18‐SPE‐CE‐MS in order to obtain the characteristic electrophoretic profiles of low molecular mass compounds. Then, multivariate curve resolution alternating least squares (MCR‐ALS) was applied to the multiple full scan MS datasets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. A total number of 29 compounds were relevant to discriminate between wt and HD plasma samples, as well as to follow‐up the HD progression. The intracellular signaling was found to be the most affected metabolic pathway in HD mice after 12 weeks of birth, when mice already showed motor coordination deficiencies and cognitive decline. This fact agreed with the atrophy and dysfunction of specific neurons, loss of several types of receptors, and changed expression of neurotransmitters.

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“…and Fig. B), following the strategy described in the work of Ortiz‐Villanueva :

D_{aug} = [\begin{matrix} D_{1} \\ ⋮ \\ D_{15} \end{matrix}] = [\begin{matrix} C_{1} \\ ⋮ \\ C_{15} \end{matrix}] S^{T} + [\begin{matrix} E_{1} \\ ⋮ \\ E_{15} \end{matrix}] = C_{aug} S^{T} + E_{aug} .

…”

Section: Methodsmentioning

confidence: 99%

“…Then, the resulting data matrices were further reduced in their m/z mode dimension in 30 different m/z ranges ( m/z widths for reduction were 20, 50, and 100 m/z in the m/z ranges 40–400, 400–800, and 800–1250 m/z , respectively; Fig. B) .…”

Section: Methodsmentioning

confidence: 99%

Metabolic profiling for the identification of Huntington biomarkers by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools

et al. 2016

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“…This robustness is demonstrated by the fact that satisfactory results can also be obtained when dealing with lower resolution data. For instance, MCR-ALS has successfully overcome inherent difficulties of GC-MS and CE-MS data analysis, such as multiple MS signals for the same metabolite due to derivatization in GC-MS and large migration time shifts between samples in CE-MS [33,34].…”

Section: Assessment Of the Effects Of Experimental Factorsmentioning

confidence: 99%

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies

et al. 2015

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The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical Abstract Summary of the workflow for untargeted metabolomics using the compared approaches.

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“…Several groups consistently trust CE‐MS for metabolomic applications in a routine productivity environment with remarkable results . Notwithstanding its advantageous features, CE‐MS remains an oddity in the field of metabolomics when compared to the more widely used chromatography‐based approaches.…”

Section: Introductionmentioning

confidence: 99%

ROMANCE: A new software tool to improve data robustness and feature identification in CE‐MS metabolomics

et al. 2018

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The use of capillary electrophoresis coupled to mass spectrometry (CE-MS) in metabolomics remains an oddity compared to the widely adopted use of liquid chromatography. This technique is traditionally regarded as lacking the reproducibility to adequately identify metabolites by their migration times. The major reason is the variability of the velocity of the background electrolyte, mainly coming from shifts in the magnitude of the electroosmotic flow and from the suction caused by electrospray interfaces. The use of the effective electrophoretic mobility is one solution to overcome this issue as it is a characteristic feature of each compound. To date, such an approach has not been applied to metabolomics due to the complexity and size of CE-MS data obtained in such studies. In this paper, ROMANCE (RObust Metabolomic Analysis with Normalized CE) is introduced as a new software for CE-MS-based metabolomics. It allows the automated conversion of batches of CE-MS files with minimal user intervention. ROMANCE converts the x-axis of each MS file from the time into the effective mobility scale and the resulting files are already pseudo-aligned, present normalized peak areas and improved reproducibility, and can eventually follow existing metabolomic workflows. The software was developed in Scala, so it is multi-platform and computationally-efficient. It is available for download under a CC license. In this work, the versatility of ROMANCE was demonstrated by using data obtained in the same and in different laboratories, as well as its application to the analysis of human plasma samples.

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Combination of CE‐MS and advanced chemometric methods for high‐throughput metabolic profiling

Cited by 25 publications

References 51 publications

Metabolic profiling for the identification of Huntington biomarkers by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools

Metabolic profiling for the identification of Huntington biomarkers by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies

ROMANCE: A new software tool to improve data robustness and feature identification in CE‐MS metabolomics

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