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¹³C NMR: ¹H–¹³C Decoupling

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...

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Successful downsizing for high-throughput ¹³C-MFA applications.

Birgitta E Ebert¹, Lars M Blank

¹Institute of Applied Microbiology, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|September 3, 2014

Summary

This study introduces a streamlined protocol for (13)C-based metabolic flux analysis in yeast. This method enables high-throughput analysis of metabolic networks, facilitating research in quantitative physiology and metabolic engineering.

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Area of Science:

Biotechnology
Systems Biology
Metabolic Engineering

Background:

(13)C label-based metabolic flux analysis is crucial for determining intracellular reaction rates.
High-quality flux determination typically relies on steady-state cultures and complex mathematical models.
Existing methods can be time-consuming and resource-intensive.

Purpose of the Study:

To develop a protocol for parallel metabolic flux analysis.
To enable high-throughput analysis of metabolic networks.
To facilitate the study of genetic and environmental impacts on metabolism.

Main Methods:

Downsized microbial (yeast) cultivation.
Miniaturized sample preparation techniques.
Semiautomated analytics and data evaluation.

Main Results:

The protocol allows for dozens of metabolic flux analyses to be performed weekly.
Enables efficient analysis of perturbations on metabolic networks.
Provides a high-throughput approach to metabolic flux analysis.

Conclusions:

The developed protocol significantly increases the efficiency of metabolic flux analysis.
This method supports large-scale studies of metabolic network operation.
Accelerates research in systems biology, quantitative physiology, and metabolic engineering.