Automatic differential analysis of NMR experiments in complex samples

• Published in: Magnetic resonance in chemistry Vol. 56; no. 6; pp. 469 - 479
• Main Authors: Margueritte, Laure, Markov, Petar, Chiron, Lionel, Starck, Jean‐Philippe, Vonthron‐Sénécheau, Catherine, Bourjot, Mélanie, Delsuc, Marc‐André
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2018; Wiley
• Subjects: Analytical chemistry; automatic processing; Avalanches; Chemical Sciences; Experiments; Life Sciences; Medicinal Chemistry; mixture analysis; Molecular chains; Natural products; NMR; Nuclear magnetic resonance; Pharmaceutical sciences; Plants (botany); recursive feature elimination; Signal processing; Software; spectral fingerprint; Spectrum analysis; automatic processing; mixture analysis; recursive feature elimination; spectral fingerprint
• ISSN: 0749-1581; 1097-458X
• DOI: 10.1002/mrc.4683

Liquid state nuclear magnetic resonance (NMR) is a powerful tool for the analysis of complex mixtures of unknown molecules. This capacity has been used in many analytical approaches: metabolomics, identification of active compounds in natural extracts, and characterization of species, and such studies require the acquisition of many diverse NMR measurements on series of samples. Although acquisition can easily be performed automatically, the number of NMR experiments involved in these studies increases very rapidly, and this data avalanche requires to resort to automatic processing and analysis. We present here a program that allows the autonomous, unsupervised processing of a large corpus of 1D, 2D, and diffusion‐ordered spectroscopy experiments from a series of samples acquired in different conditions. The program provides all the signal processing steps, as well as peak‐picking and bucketing of 1D and 2D spectra, the program and its components are fully available. In an experiment mimicking the search of a bioactive species in a natural extract, we use it for the automatic detection of small amounts of artemisinin added to a series of plant extracts and for the generation of the spectral fingerprint of this molecule. This program called Plasmodesma is a novel tool that should be useful to decipher complex mixtures, particularly in the discovery of biologically active natural products from plants extracts but can also in drug discovery or metabolomics studies. Natural extracts studies require the acquisition of many NMR spectra on sample series, and the associated NMR data avalanche requires to resort to automatic processing and analysis. The Plasmodesma program allows the autonomous, unsupervised processing of a large corpus of 1D, 2D, and diffusion‐ordered spectroscopy experiments from such series and prepares for their automatic analysis. The automatic detection of artemisinin natural extracts presented here shows that this novel tool should be useful particularly in drug discovery or metabolomics studies.