TY - JOUR
T1 - Signature Mapping (SigMa)
T2 - an efficient approach for processing complex human urine 1H NMR metabolomics data
AU - Khakimov, Bekzod
AU - Mobaraki, Nabiollah
AU - Trimigno, Alessia
AU - Aru, Violetta
AU - Engelsen, Søren Balling
PY - 2020
Y1 - 2020
N2 - Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.
AB - Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.
KW - MCR
KW - Metabolomics
KW - NMR
KW - Signature Mapping
KW - Urine
U2 - 10.1016/j.aca.2020.02.025
DO - 10.1016/j.aca.2020.02.025
M3 - Journal article
C2 - 32222235
AN - SCOPUS:85080998572
VL - 1108
SP - 142
EP - 151
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
SN - 0003-2670
ER -