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Review
. 2021 Mar 9;11(3):157.
doi: 10.3390/metabo11030157.

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

Roy Chih Chung Wang  1 , David A Campbell  1 , James R Green  2 , Miroslava Čuperlović-Culf  3   4
Affiliations
Review

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

Roy Chih Chung Wang et al. Metabolites. .

Abstract

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Keywords: NMR; biomanufacturing; bioprocessing; bioreactors; metabolite quantification; metabolomics; quantitative NMR.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Two absorption Lorentzians that share the same frequency parameter. Both are multiplied by an intensity parameter α, which is set at 3.5.
Figure 2
Figure 2
The autophased spectrum of four NMR experiments: (top left) experiment containing only l-Isoleucine, (top right) experiment containing only l-Leucine, (bottom left) experiment containing only l-Valine, and (bottom right) experiment containing a mixture of l-Isoleucine, l-Leucine, l-Valine, and other metabolites. Autophasing is discussed in Section 3.5.
Figure 3
Figure 3
The real part of the autophased spectrum of an mammalian cell bioreactor NMR experiment.
Figure 4
Figure 4
Reconstructed spectrum (blue, label: fit) vs. the preprocessed data spectrum (green, label: spectrum). Some significant artifacts are visible in the preprocessed data spectrum.
Figure 5
Figure 5
Close-up of Figure 4.
Figure 6
Figure 6
Reconstructed spectrum (orange, label: estimated) vs. the preprocessed data spectrum (blue, label: data).
Figure 7
Figure 7
Close-up of Figure 6.
See this image and copyright information in PMC

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