Mass spectrometry in metabolome analysis

Villas‐Bôas, Silas G.; Mas, Sandrine; Åkesson, Mats; Smedsgaard, Jørn; Nielsen, Jens

doi:10.1002/mas.20032

Cited by 549 publications

(477 citation statements)

References 240 publications

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“…A nontargeted approach is effectively used to identify a number of unknown metabolites known as the metabolome, whereas targeted metabolomics is restricted to known metabolites, usually for specific metabolites or within a single class of compounds (14).…”

Section: Metabolomicsmentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

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Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

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Section: Metabolomicsmentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

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“…FT-MS therefore knows a growing interest, in particular for proteomics, metabolomics, and petroleomics (31,32). In these "-omics" studies, a large number of samples have to be processed rapidly, and the throughput of the processing techniques is thus a paramount parameter.…”

Section: Application To Ft-icr Ms Ft-ms Measures the Frequencies Of mentioning

confidence: 99%

Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry

Chiron¹,

Agthoven

Kieffer

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Modern scientific research produces datasets of increasing size and complexity that require dedicated numerical methods to be processed. In many cases, the analysis of spectroscopic data involves the denoising of raw data before any further processing. Current efficient denoising algorithms require the singular value decomposition of a matrix with a size that scales up as the square of the data length, preventing their use on very large datasets. Taking advantage of recent progress on random projection and probabilistic algorithms, we developed a simple and efficient method for the denoising of very large datasets. Based on the QR decomposition of a matrix randomly sampled from the data, this approach allows a gain of nearly three orders of magnitude in processing time compared with classical singular value decomposition denoising. This procedure, called urQRd (uncoiled random QR denoising), strongly reduces the computer memory footprint and allows the denoising algorithm to be applied to virtually unlimited data size. The efficiency of these numerical tools is demonstrated on experimental data from high-resolution broadband Fourier transform ion cyclotron resonance mass spectrometry, which has applications in proteomics and metabolomics. We show that robust denoising is achieved in 2D spectra whose interpretation is severely impaired by scintillation noise. These denoising procedures can be adapted to many other data analysis domains where the size and/or the processing time are crucial. big data | noise reduction | matrix rank reduction | FT-ICR MS

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“…This fast analytical approach allows informative, straightforward chemical characterization with the ultimate goal of identifying distinctive metabolites. MS fingerprinting normally applies direct sample infusion and requires minimal sample preparation (Villas-Boas et al 2005, Dettmer et al 2007. Spectra obtained in high-resolution modes provide large amounts of MS data (several m/z vs ion abundances) that require multivariate statistics (or chemometrics) for sample classification (Blekherman et al 2011, Horgan et al 2011, Theodoridis et al 2011.…”

Section: Introductionmentioning

confidence: 99%

Prediction of embryo implantation potential by mass spectrometry fingerprinting of the culture medium

Cortezzi¹,

Cabral²,

Trevisan³

et al. 2013

Reproduction

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This study has evaluated the performance of a multivariate statistical model to predict embryo implantation potential by processing data from the chemical fingerprinting of culture medium samples used for human embryo culture. The culture medium for 113 embryos from 55 patients undergoing ICSI was collected after embryo transfer. The samples were split into positive (nZ29) and negative (nZ84) implantation groups according their implantation outcomes (100% or 0% implantation). The samples were individually diluted and analyzed by electrospray ionization mass spectrometry (ESI-MS). The m/z ratios and relative abundances of the major ions in each spectrum were considered for partial least square discriminant analysis. Data were divided into two subsets (calibration and validation), and the models were evaluated and applied to the validation set. A total of 5987 ions were observed in the groups. The multivariate statistical model described more than 82% of the data variability. Samples of the positive group were correctly identified with 100% probability and negative samples with 70%. The culture media used for embryos that were positive or negative for successful implantation showed specific biochemical signatures that could be detected in a fast, simple, and noninvasive way by ESI-MS. To our knowledge, this is the first report that uses MS fingerprinting to predict human embryo implantation potential. This biochemical profile could help the selection of the most viable embryo, improving single-embryo transfer and thus eliminating the risk and undesirable outcomes of multiple pregnancies.

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Mass spectrometry in metabolome analysis

Cited by 549 publications

References 240 publications

Metabolomics: Applications and Promise in Mycobacterial Disease

Metabolomics: Applications and Promise in Mycobacterial Disease

Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry

Prediction of embryo implantation potential by mass spectrometry fingerprinting of the culture medium

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