Metabolites 2019, 9(1), 16; https://doi.org/10.3390/metabo9010016
1D “Spikelet” Projections from Heteronuclear 2D NMR Data—Permitting 1D Chemometrics While Preserving 2D Dispersion
Maryam Tabatabaei Anaraki 1, Wolfgang Bermel 2, Rudraksha Dutta Majumdar 3, Ronald Soong 1, Myrna Simpson 1,4
, Martine Monnette 3 and André J. Simpson 1,4,*
, Martine Monnette 3 and André J. Simpson 1,4,*
1
Environmental NMR Center, Department of Physical and Environmental Sciences, University of Toronto Scarborough, Military Trial, Toronto, ON 1265, Canada
2
Bruker BioSpin GmbH, Silberstreifen 4, 76287 Rheinstetten, Germany
3
Bruker Ltd., 2800 High Point Drive, Milton, ON L9T 6P4, Canada
4
Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M1C 1A4, Canada
*
Author to whom correspondence should be addressed.
Received: 13 December 2018 / Revised: 8 January 2019 / Accepted: 9 January 2019 / Published: 16 January 2019
(This article belongs to the Special Issue NMR-based Metabolomics and Its Applications Volume 2)
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this application note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This application note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers. View Full-Text
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tabatabaei Anaraki, M.; Bermel, W.; Dutta Majumdar, R.; Soong, R.; Simpson, M.; Monnette, M.; Simpson, A.J. 1D “Spikelet” Projections from Heteronuclear 2D NMR Data—Permitting 1D Chemometrics While Preserving 2D Dispersion. Metabolites 2019, 9, 16.
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