ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra

Abstract

: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, Springer Verlag, 2017, 10.1007/s11306-017-1244 Abstract:Introduction Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues. ResultsThe performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion ASICS is a completely automated procedure to identify and quantify metabolites in 1 H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

NMR Analysis of the Correlation of Metabolic Changes in Blood and Cerebrospinal Fluid in Alzheimer Model Male and Female Mice

“…In order to determine significant changes as well as possible relationships between metabolism and the disease development in APP/PS1 mice we have performed assignment and quantification of metabolites from NMR spectra using previously published methods [23][24][25] as briefly described in the Material and Methods. List of analyzed metabolites and their 1D standard spectra obtained from HMDB [26] or BMRB [27] are used for the determination of relative concentrations in the sample spectra are shown in Figure 3 with details of their selection and preprocessing described in Materials and Methods.…”

“…Metabolites determined by ASICS [24] and Metabohunter [25] as possible observed in CSF and blood NMR. Shown are complete spectral regions used for metabolite quantification.…”

“…Metabolite assignment was performed using 1D 1 H data in comparison to the standard metabolite spectra provided in databases HMDB [26] and BMRB [27] and NMR search tools MetaboHunter [25] and ASICS [24]. Additionally, all automated assignments have been confirmed based on literature information for metabolites' measurement in related samples.…”

Hochdurchsatz‐Metabolomik mit 1D‐NMR

“…Werkzeuge für eine (halb‐)automatische Quantifizierung wurden ebenfalls entwickelt. Zu ihnen gehören BATMAN, BAYESIL, ASICS und das NMR Suite Software Package (Chenomx Inc., Edmonton, Canada). Die ersten drei sind weitgehend automatisierte Module, die auf der Bayes‐Interferenz (BATMAN und BAYESIL) oder auf linearen Modellen (ASICS) beruhen.…”

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

“…Once metabolite identification has been achieved, the next challenge is to quantify metabolite concentrations. This process works robustly only for a relatively small set of metabolites, and requires expert refinement when using publicly available quantification tools such as BATMAN (Hao et al 2012) , FOCUS (Alonso et al 2014) , BAYESIL (Ravanbakhsh et al 2015) , ASICS (Tardivel et al 2017) , AQuA (Röhnisch et al 2018) or rDolphin (Cañueto et al 2018) . This is unsatisfactory in light of the fact that a sizable number of metabolites have been identified in human biofluids.…”