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Review
. 2023 Jan 10;95(1):287-303.
doi: 10.1021/acs.analchem.2c04406.

Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions

Affiliations
Review

Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions

Tito Damiani et al. Anal Chem. .

Erratum in

No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors declare the following competing financial interest(s): A.K. is employed at Bruker Daltonics GmbH & Co. KG, Bremen, Germany. C.A.K. is employed at Enveda Biosciences, Boulder, CO, USA.

Figures

Figure 1
Figure 1
Epilipidome chemical space within the molecular weight range of 150–1500 Da. (A) Combinatorial enumeration of all permutations with repetition of all modifications at all possible allylic and bis-allylic positions. In this example, a lipid species which is not reported in common databases such as SM(d18:1/20:4) can be constructed and selected as a precursor for oxidation, aiming at enumerating all possible modifications at all possible positions. (B) Knowledge-based enumeration of only actually possible modifications of fatty acid residues on certain sites reported for enzymatic and free-radical based mechanisms. As an example, the widely reported oxylipin products from arachidonic acid can be used to predict oxidation addition products from commonly reported phospholipids with arachidonic acid residue such as PE(18:0/20:4) and generate products with oxylipin residue such as PE(18:0/15-HETE) and PE(0:0/PGE2) which are confirmed by literature., The figure is adapted from Ni, Z.; Goracci, L.; Cruciani, G.; Fedorova, M. Computational Solutions in Redox Lipidomics - Current Strategies and Future Perspectives. Free Radic Biol Med2019, 144, 110–123 under the terms of the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The original script for epilipidome estimation is available on GitHub (https://github.com/SysMedOs/LipidomeEstimation).
Figure 2
Figure 2
Examples of isobaric and isomeric lipid species in LIPID MAPS. (A) Three isobaric oxidized phosphatidylcholines obtained by a search for a mass of 649.40 ± 0.05 Da. (B) Three isomeric oxidized phosphatidylcholines obtained by a search for C44H80NO11P (accessed September 2022).
Figure 3
Figure 3
Scheme of the (epi)lipidomics workflow described in four stages: sample preparation, LC-MS analysis, data analysis, and interpretation.
Figure 4
Figure 4
Visualization of epilipid identification results. (A) KMD plot for cardiolipins (CL), monolysocardiolipins (MLCL), and their corresponding oxidation products. The difference in saturation and the degree of oxidation are displayed on the y-axis (i.e., KMD(H)) and x-axis (i.e., KMD(O)), respectively. The RT dimension is represented as a color-coded scale. The figure is adapted from Helmer et al., under the terms of the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. (B) Circos diagram illustrating the relationship between the identified/quantified oxidized and parent phospholipid species. (PA: Glycerophosphates; PC: Glycerophosphocholines; PE: Glycerophosphoethanolamines; PG: Glycerophosphoglycerols; PS: Glycerophosphoserines; Lyso PL: Monoacylglycerophospholipids; OAP: Oxygen addition products; OCP: Oxidation cleavage products). The figure is adapted from Ni, Z.; Angelidou, G.; Hoffmann, R.; Fedorova, M. LPPtiger Software for Lipidome-Specific Prediction and Identification of Oxidized Phospholipids from LC-MS Data sets. Sci Rep2017, 7 (1), 15138 under the terms of the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Figure 5
Figure 5
Scheme of the integration between Lipostar and LPPtiger 2.

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