An untargeted metabolomic workflow to improve structural characterization of metabolites

Abstract

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

Figure 1. Comparison of MS² isolation window widths

(a) Effect of MS² isolation window width on signal intensity. Ions measured are small molecule standards from Agilent's ESI-TOF tuning mix solution. (b) With an MS² isolation window of 9 m/z, more than 75% of acquired scans have peaks that cannot be purely isolated in the collision cell for MS² analysis and therefore their MS² spectra may contain artifacts that hinder metabolite identification. With an MS² isolation window of 1 m/z, the proportion of acquired scans with peaks that cannot be purely isolated in the collision cell is reduced to ~20%. (c) By using an MS² isolation window of 9 m/z instead of 1 m/z, sensitivity is improved such that ~900 more metabolite peaks (i.e., features) can be structurally characterized by MS² analysis.

Figure 2. Basis of deconvolution approach

(a) MS² isolation window shapes for Agilent 6520 QTOF. Using a larger MS² isolation window increases the likelihood of isolating more than 1 precursor in the collision cell for MS² analysis, but also improves the relative intensity of ions transmitted. Representative (b) MS² at 0 V and (c) MS² at 20 V data used for deconvolution over a 30 second chromatographic window. Each box in the plots represents a single scan. Peak intensity is denoted by color, with bright red representing the most intense peaks. The blue brackets indicate the position of the 9 Da MS² isolation window used. Ion intensities that are high and low in alternating scans (i.e., bright red followed by light red) are ions that are included and excluded respectively with shifts in the position of the MS² isolation window. Our deconvolution approach is based on identifying matching patterns in the MS² plots at 0 and 20 V. An example of a matched precursor and associated fragment is shown with arrows.

Figure 3. decoMS2 applied to amino acid standards

Amino acid standards were mixed and analyzed by LC/MS², using a 9 Da MS² isolation window. Because the amino acid standards were inadequately separated, each of their MS² spectra were contaminated by additional precursors in the collision cell. (a) The experimental MS² spectra for 4 representative amino acids are shown on the top of each plot. The standard MS² spectra for each of these amino acids as obtained from pure model compounds is shown on the bottom. Fragments that match in the 2 spectra are colored black, while fragments that do not match are colored red. (b) After the application of decoMS2, the top experimental MS² spectra of each amino acid are highly consistent with the MS² spectra from their respective amino acid standards.

Figure 4. decoMS2 applied to a biological sample

To evaluate the performance of decoMS2 on a large set of metabolites in a biological sample, we identified ions in the metabolic extract of astrocytes that were contaminated when isolated with a 9 Da MS² isolation window but that were not contaminated when isolated using a 1 Da MS² isolation window. (a) The experimental MS² spectra for 4 representative accurate masses as obtained using a 9 Da MS² isolation window are shown on the top of each plot. The experimental MS² spectra for the same 4 compounds as obtained using a 1 Da MS² isolation window is shown on the bottom of each plot. Fragments that match in the 2 spectra are colored black, while fragments that do not match are colored red. (b) After applying decoMS2 to the top spectra using the shifting window approach, the MS² spectra obtained with a 9 Da MS² isolation window are in concordance with those MS² spectra obtained with a 1 Da MS² isolation window.

Figure 5. decoMS2 applied to methionine from astrocytes

(a) The metabolic extract of astrocytes was analyzed by using the standard metabolomic workflow and MS² data for methionine were obtained. The raw experimental MS² spectrum for methionine is shown on top and the MS² spectrum from a pure standard is shown on bottom. Fragments that match in the 2 spectra are colored black, while fragments that do not match are colored red. The additional fragments in the experimental MS² spectrum preclude identification of this compound as methionine. (b) After applying decoMS2, the experimental MS² data matches the MS² data of the methionine standard and thereby supports the structural assignment.

Figure 6. decoMS2 applied to myristoylcarnitine from peripheral nerve tissue

Peripheral nerve tissue was analyzed from 2 groups of mice using the standard metabolomic workflow. A feature of interest was identified to be dysregulated with statistical significance and was targeted for structural characterization. MS² data were acquired for the feature and searched in metabolite databases. (a) The raw MS² spectrum acquired for this feature is shown on top. The accurate mass of the feature was consistent with that of myristoylcarnitine and the MS² data had similarities to the MS² data of the myristoylcarnitine standard, which is shown on the bottom. Fragments that match in the 2 spectra are colored black. Fragments that do not match are colored red. Although we hypothesized that this feature of interest in the peripheral nerve tissue may be myristoylcarnitine, the additional fragments in the experimental MS² spectrum precluded identification and publication of the compound as myristoylcarnitine. (b) After applying decoMS2, the experimental MS² data matched the MS² data of the myristoylcarnitine standard and thereby supported the structural assignment.