#Carbon backbone full#

To minimize spurious peaks in CT-TOCSY cross sections, a filtering procedure (DeCoDeC) was applied, which generates from a pair of TOCSY traces a consensus trace that contains only peaks that appear in both original traces. For more complex mixtures the extraction of ‘pure’ traces is increasingly hard because of the higher likelihood of peak overlaps.

#Carbon backbone full#

6 However, full or partial peak overlap along one of the frequency domains produces traces that contain additional peaks, which stem from nearby cross-peaks of other mixture components. In principle, a cross-section through a cross-peak along ω 2 (ω 1) represents the homonuclear (de)coupled 13C 1D spectrum of the corresponding spin system. For linear spin systems, the transfer efficiency over ~10 13C spins is quite efficient for the mixing time of 47 ms used here (see Figure S5). The TOCSY spectrum with a sufficiently long mixing time correlates 13C spins within the same spin system with each other. The large resolution improvement along ω 1 in the CT-TOCSY experiment enables the extraction of unique traces for their assignment to individual metabolites. Selected region of (A) 13C- 13C 2D TOCSY and (B) 13C- 13C 2D constant-time (CT) TOCSY of uniformly 13C-labeled E. The favorable resolution achieved in this way is not a limiting factor any longer, except for the analysis of highly complex carbohydrate mixtures, which could benefit from partial fractionation prior to the NMR experiments. The resolution enhancement along ω 1 over the standard 2D 13C- 13C TOCSY amounts on average to a factor >4, improving the average multiplet width from >70 Hz to ~15 Hz, which turned out to be critical for the analysis of a spectrum of the complexity of a cell lysate. By contrast, the CT-TOCSY ( Figure 1B) is decoupled along the ω 1 dimension with respect to the dominant 1J( 13C, 13C)-couplings and therefore displays significantly reduced cross-peak overlap. In the regular 2D TOCSY these splittings appear along both frequency dimensions leading to severely congested cross-peak regions ( Figure 1A). The presence of homonuclear 1J( 13C, 13C)-couplings leads to prominent peak splittings with average multiplet widths ~75 Hz, which substantially exceed the intrinsic line widths. coli cell lysate of a 2D 13C- 13C CT TOCSY with a regular 2D 13C- 13C TOCSY ( Figure S1 shows the full CT-TOCSY spectrum). 5įigure 1 compares a spectral region of E. They can be mitigated along the indirect ω 1 dimension by 13C- 13C constant-time (CT) TOCSY spectroscopy. 3 On the other hand, the same 1J( 13C, 13C)-couplings lead to broad multiplet structures 4 resulting in increased cross-peak overlap. The large one-bond scalar couplings ( 1J( 13C, 13C) > 30 Hz) make the efficient transfer of spin magnetization during 13C-TOCSY mixing possible. Here we present a comprehensive approach for the characterization of the metabolic content of uniformly 13C-enriched cells based on homonuclear 2D 13C NMR. Furthermore, the spectral information of protons may not be sufficient for the complete reconstruction of the carbon backbone of metabolites and their bonding topology, which is a prerequiste for structure determination. 2 The strong conformation-dependence of vicinal 3J( 1H, 1H)-couplings, however, can cause uneven magnetization transfer in TOCSY and COSY spectra, thereby impeding the assignment of cross-peaks to individual spin systems or entire molecules. For sensitivity reasons, so far, a majority of applications has been based on 2D 1H NMR experiments taking advantage of the high natural abundance of proton spins and their relatively large magnetic moment. Although more time-consuming than 1D NMR, the achievable gain in resolution makes 2D NMR an attractive method for this task. Their systematic identification and structural characterization is therefore an important target. Many biological samples, however, contain a significant number of unknown metabolites that are not catalogued in databanks. 1 The latter approach is commonly used for the identification of biomarkers by statistical analysis of 1D NMR spectra from different samples and for the identification of metabolites by databank screening. The metabolic characterization of biological samples either uses potentially elaborate chromatographic separation procedures prior to analysis or it applies NMR or mass spectroscopic methods directly to the non-fractionated samples. A multitude of metabolites serve diverse cellular functions, such as messengers, enzymatic substrates, energy source, and molecular and structural building blocks. A distinctive feature of biological systems is their high level of chemical complexity.