Phospholipidomics and Retention Times Measured Using New Workflow


In a recent study from researchers at Chongqing University Cancer Hospital in Chongqing, China, scientists developed a new system for analyzing the potential impact of retention time (RT) prediction on targeted LC–MS-based lipidomics. Their findings were later published in the journal Analyst (1).

Clinical trial | Image Credit: © Microgen -

Clinical trial | Image Credit: © Microgen -

Lipid metabolism is when fatty acids are oxidized to either generate energy or synthesize new lipids from smaller constituent molecules (2). This process, which occurs in various parts of the body such as the intestine and the pancreas, helps regulate bodily functions. However, dysfunctional lipid metabolism can be a factor in the development and progression of various diseases; one 2023 study published in Cancers even pointed out how dysfunctional lipid metabolism has a relationship with prostate cancer development. The scientists, who are from the University of Melbourne and Royal Melbourne Hospital in Melbourne, Australia, wrote, “Dysfunctional lipid metabolism has long been known to have a relationship to prostate cancer development; however, only recently have studies attempted to elucidate the exact mechanism relating genetic abnormalities and lipid metabolic pathways” (3).

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Accurately measuring lipidomes can help in understanding the complex interactions between genes, proteins, and lipids in health and diseases. For targeted and untargeted metabolomics, an increasingly important aspect of these processes is the prediction of retention time (RT). However, the potential impact RT prediction can have on targeted liquid chromatography–mass spectrometry (LC–MS)-based lipidomics is not fully understood. For the Analyst study, the scientists proposed a simplified workflow for predicting RT in phospholipidomics. Specifically, they utilized the fatty acyl chain length or carbon–carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. Overall, they found that their model’s predictive capacity for RT was comparable to those of publicly accessible programs, such as the QSRR Automator system. Additionally, MRM validation helped in further mitigating interferences in signal recognition.

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Using this workflow, the scientists conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Their findings showed that after developing drug resistance, there was an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines. Both cells lines showed 29 lipids to be co-upregulated and 5 lipids to be co-downregulated. Further validation was conducted on seven of the upregulated lipids, which was performed using an independent dataset. Overall, this study showed how applying the fatty acyl chain length can be used as a model for developing a lipidomics library. Furthermore, the study generated RTs for 526 lipids, which was done by selecting a limited number of deuterated or authentic standards.

"As a result, the utility of this workflow was demonstrated in sorafenib resistant HCC cell lines, we found an increase in MUFA or PUFA in sorafenib-resistant cells, with seven co-upregulated lipids being identified and validated using an independent data set,” the scientists concluded (1).


(1) Zhang, J.; Zhou, Y.; Lei, J.; Liu, X.; Zhang, N.; Wu, L.; Li, Y. Retention Time Prediction and MRM Validation Reinforce the Biomarker Identification of LC-MS Based Phospholipidomics. Analyst 2024, 149 (2), 515–527. DOI: 10.1039/D3AN01735D

(2) Lipid Metabolism. Lumen Learning 2024. (accessed 2024-5-13)

(3) Alberto, M.; Yim, A.; Lawrentschuk, N.; Bolton, D. Dysfunctional Lipid Metabolism—The Basis for How Genetic Abnormalities Express the Phenotype of Aggressive Prostate Cancer. Cancers 2023, 15 (2), 341. DOI: 10.3390/cancers15020341

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