Statistical Heterospectroscopy, an Approach to the Integrated Analysis of NMR and UPLC-MS Data Sets:  Application in Metabonomic Toxicology Studies

Abstract

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.

Fast analysis in liquid chromatography using small particle size and high pressure

“…A fast separation was carried out in 1.5 min, while 10 min was necessary in HPLC with similar peak capacity and number of detected peaks. Other publications also demonstrated the potential of UPLC-MS for metabolomic studies [118][119][120][121][122] with improvement of sensitivity, peak capacity, and analysis time as main objectives.…”

Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in 1H NMR Metabolic Data Sets

“…The technique has been used to correlate signals from different NMR observed nuclei, termed HET-STOCSY [8][9][10] , allowing cross-assignment of signals between 1 H, 31 P and 19 F spectra, as well as editing of the homonuclear STOCSY according to heteronuclear correlations. It is also possible to apply the analysis to cross-platform comparisons such as the cross-correlation of mass spectrometric and NMR signals, known as Statistical HeteroSpectroscopy (SHY) 14 .…”

Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13 C NMR

“…Metabolic profiling or metabolomics, defined as the analysis of multiple biofluid metabolites in parallel, holds the promise of earlier disease detection and improved understanding of systems biology (1-3). Early indications of this potential have been reported for the detection of several diseases, including inborn errors of metabolism, cardiovascular diseases, and cancer (4-7).NMR and mass spectrometry are the two most often used analytical methods for metabolite profiling because of their high resolution and rich data content (8)(9)(10)(11)(12)(13)(14). Although mass spectrometry is the more sensitive technique, NMR provides broad coverage of the metabolome by detecting all of the (hydrogencontaining) metabolites present in the biofluid simultaneously, with excellent reproducibility and only limited sample pretreatment.…”

“…NMR and mass spectrometry are the two most often used analytical methods for metabolite profiling because of their high resolution and rich data content (8)(9)(10)(11)(12)(13)(14). Although mass spectrometry is the more sensitive technique, NMR provides broad coverage of the metabolome by detecting all of the (hydrogencontaining) metabolites present in the biofluid simultaneously, with excellent reproducibility and only limited sample pretreatment.…”