Automated analysis for multiplet identification from ultrahigh resolution 2D-1H,13C-HSQC NMR spectra [version 1; peer review: 2 approved]

Laura Ferrante; Kashif Rajpoot; Mark Jeeves; Christian Ludwig

doi:10.12688/wellcomeopenres.18248.1

Automated analysis for multiplet identification from ultrahigh resolution 2D-¹H,¹³C-HSQC NMR spectra [version 1; peer review: 2 approved]

Laura Ferrante, Kashif Rajpoot, Mark Jeeves, Christian Ludwig^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- ¹H, ¹³C-HSQC NMR spectra arising from ¹³C - ¹³C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- ¹H, ¹³C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package.

Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts.

Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively.

Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- ¹H, ¹³C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Original language	English
Article number	262
Number of pages	22
Journal	Wellcome Open Research
Volume	7
DOIs	https://doi.org/10.12688/wellcomeopenres.18248.1
Publication status	Published - 28 Mar 2023

Bibliographical note

Funding Information:
The authors thank HWB-NMR staff at the University of Birmingham for providing open access to their Wellcome Trust-funded 800 MHz spectrometer. We also acknowledge support by the metabolic tracer analysis core (MTAC) at the Institute of Metabolism and Systems Research (IMSR) at the University of Birmingham.

This work was supported by Wellcome [208400, https://doi.org/10.35802/208400]. We gratefully acknowledge financial support for LF from the Engineering and Physical Sciences Research Council (EPSRC) through a studentship from the Physical Sciences for Health Centre for Doctoral Training (EP/L016346/1).

Publisher Copyright:
Copyright: © 2022 Ferrante L et al.

Keywords

automated
independent component analysis
machine learning
metabolic tracing
Metabolism
NMR spectroscopy

ASJC Scopus subject areas

Medicine (miscellaneous)
General Biochemistry,Genetics and Molecular Biology

Access to Document

10.12688/wellcomeopenres.18248.1Licence: Creative Commons: Attribution (CC BY)

HWB-NMR: a national resource for biomolecular research
Tennant, D., Young, S., Lavery, G., Peet, A., Ludwig, C., Futterer, K., Guenther, U., Henderson, I., Willcox, B., Adams, D. & Carlomagno, T.
THE WELLCOME TRUST
25/09/17 → 25/09/24
Project: Research

Cite this

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title = "Automated analysis for multiplet identification from ultrahigh resolution 2D-1H,13C-HSQC NMR spectra [version 1; peer review: 2 approved]",

abstract = "Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.",

keywords = "automated, independent component analysis, machine learning, metabolic tracing, Metabolism, NMR spectroscopy",

author = "Laura Ferrante and Kashif Rajpoot and Mark Jeeves and Christian Ludwig",

note = "Funding Information: The authors thank HWB-NMR staff at the University of Birmingham for providing open access to their Wellcome Trust-funded 800 MHz spectrometer. We also acknowledge support by the metabolic tracer analysis core (MTAC) at the Institute of Metabolism and Systems Research (IMSR) at the University of Birmingham. This work was supported by Wellcome [208400, https://doi.org/10.35802/208400]. We gratefully acknowledge financial support for LF from the Engineering and Physical Sciences Research Council (EPSRC) through a studentship from the Physical Sciences for Health Centre for Doctoral Training (EP/L016346/1). Publisher Copyright: Copyright: {\textcopyright} 2022 Ferrante L et al.",

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doi = "10.12688/wellcomeopenres.18248.1",

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AU - Ferrante, Laura

AU - Rajpoot, Kashif

AU - Jeeves, Mark

AU - Ludwig, Christian

N1 - Funding Information: The authors thank HWB-NMR staff at the University of Birmingham for providing open access to their Wellcome Trust-funded 800 MHz spectrometer. We also acknowledge support by the metabolic tracer analysis core (MTAC) at the Institute of Metabolism and Systems Research (IMSR) at the University of Birmingham. This work was supported by Wellcome [208400, https://doi.org/10.35802/208400]. We gratefully acknowledge financial support for LF from the Engineering and Physical Sciences Research Council (EPSRC) through a studentship from the Physical Sciences for Health Centre for Doctoral Training (EP/L016346/1). Publisher Copyright: Copyright: © 2022 Ferrante L et al.

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N2 - Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

AB - Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

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