Feature-based molecular networking in the GNPS analysis environment

Abstract

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Fig. 1:. Methods for the generation of molecular networks from non-targeted mass spectrometry data with the GNPS web platform.

a) Two methods exist for the generation of molecular networks on the GNPS web platform: classical MN and feature-based molecular networking (FBMN). For both methods, the mass spectrometry data files have first to be converted to the mzML format using tools such as Proteowizard MSConvert. The classical MN method runs entirely on the GNPS platform. In that method, MS² spectra are clustered with MS-Cluster and the consensus MS² spectra obtained are used for molecular network generation. In the case of FBMN, the user first applies a feature detection and alignment tool to first process the LC-MS² data (such as MZmine, MS-DIAL, XCMS, OpenMS, Progenesis QI, or MetaboScape) instead of using MS-Cluster (classical MN) on GNPS. Results are then exported (feature quantification table (.TXT format) along with a MS² spectral summary (.MGF format) or an mzTab-M file) and uploaded to the GNPS web platform for molecular networking analysis with the FBMN workflow. b) Graphs showing the number of molecular networking jobs performed on GNPS. The upper graph shows the number of classical MN and FBMN jobs since 2016. The lower graph shows the number of FBMN jobs since its introduction in 2017 and key events accelerating its use.

Fig. 2:. Comparisons of classical MN and FBMN.

In these examples, the node size corresponds to the relative spectral count in classical MN (orange boxes, left) or to the sum of LC-MS peak area in FBMN (blue boxes, right); diamond shape nodes are spectra annotated by spectral library matching; the edge color gradient indicates the spectral similarity degree (the lighther the less similar). (a) displays the results from classical MN with the LC-MS² data of Euphorbia dendroides plant samples (n = 1 LC-MS² experiment per sample); classical MN resulted in one node for the ion at m/z 589.313, while (b) FBMN was able to detect seven isomers. (c) Classical MN with the data from the American Gut Project (n = 1 LC-MS² experiment per sample) showed two different N-acyl amides while the use of FBMN (d) allowed the annotation of three different isomers per N-acyl amides. Classical MN (e) and FBMN (f) were used to analyse the network of EDTA in plasma (373 samples, n = 1 LC-MS² experiment per sample). By merging MS² spectra of EDTA eluting over 2.5 min into one best-quality MS² spectrum, FBMN recovered the molecular similarity of in-source fragments observed for EDTA. (g and h) Evaluation of quantitative performance using multiple dilutions of a reference serum sample (3 LC-MS² experiments per sample). The plots (g and h) are showing the distribution of the coefficient of determination (R²) from the Ordinary least squares Linear Regression (OLR) analysis between the observed and expected relative ion abundance for molecular network nodes in classical MN (g) or in FBMN (h). The upper charts present the distribution of the R² for the network nodes with classical MN (n = 3,367) and FBMN (n = 877), and the bottom charts show the R² distribution from the OLR analysis for the annotated reference compounds with classical MN (n = 49) and FBMN (n = 54). While classical MN uses the clustered MS² spectral count or the sum of the precursor ions to estimate the molecular network node abundance, FBMN uses the LC-MS feature abundance (peak area or height), resulting in a more accurate estimation of the relative ion intensity.