Automated analysis for multiplet identification from ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra

Laura Ferrante; Kashif Rajpoot; Mark Jeeves; Christian Ludwig

doi:10.12688/wellcomeopenres.18248.2

Automated analysis for multiplet identification from ultra-high resolution 2D- ¹H, ¹³C-HSQC NMR spectra

Wellcome Open Res. 2023 May 19:7:262. doi: 10.12688/wellcomeopenres.18248.2. eCollection 2022.

Authors

Laura Ferrante¹, Kashif Rajpoot², Mark Jeeves^#³, Christian Ludwig^#⁴

Affiliations

¹ School of Computer Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
² University of Birmingham Dubai, Dubai International Academic City, United Arab Emirates.
³ Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
⁴ Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, B15 2TT, UK.

^# Contributed equally.

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: Metabolism; NMR spectroscopy; automated; independent component analysis; machine learning; metabolic tracing.

Grants and funding

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).