Lipidomics of environmental microbial communities. I: visualization of component distributions using untargeted analysis of high-resolution mass spectrometry data
Bale, N.J.; Ding, S.; Hopmans, E.C.; Arts, M.G.I.; Villanueva, L.; Boschman, C.; Haas, A.F.; Schouten, S.; Sinninghe Damsté, J.S (2021). Lipidomics of environmental microbial communities. I: visualization of component distributions using untargeted analysis of high-resolution mass spectrometry data. Front. Microbiol. 12. https://dx.doi.org/10.3389/fmicb.2021.659302 In: Frontiers in Microbiology. Frontiers Media: Lausanne. ISSN 1664-302X; e-ISSN 1664-302X, more |   |
| Author keywords | lipids; liquid chromatography mass spectrometry; lipidome; lipidomics; MZmine; Black Sea |
| Authors | | Top | | - Arts, M.G.I., more
- Villanueva, L., more
- Boschman, C.
| - Haas, A.F., more
- Schouten, S., more
- Sinninghe Damsté, J.S, more
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| Abstract | Lipids, as one of the main building blocks of cells, can provide valuable information on microorganisms in the environment. Traditionally, gas or liquid chromatography coupled to mass spectrometry (MS) has been used to analyze environmental lipids. The resulting spectra were then processed through individual peak identification and comparison with previously published mass spectra. Here, we present an untargeted analysis of MS1 spectral data generated by ultra-high-pressure liquid chromatography coupled with high-resolution mass spectrometry of environmental microbial communities. Rather than attempting to relate each mass spectrum to a specific compound, we have treated each mass spectrum as a component, which can be clustered together with other components based on similarity in their abundance depth profiles through the water column. We present this untargeted data visualization method on lipids of suspended particles from the water column of the Black Sea, which included >14,000 components. These components form clusters that correspond with distinct microbial communities driven by the highly stratified water column. The clusters include both known and unknown compounds, predominantly lipids, demonstrating the value of this rapid approach to visualize component distributions and identify novel lipid biomarkers. |
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