= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Bishop

MSACL 2023 Abstract

Self-Classified Topic Area(s): Lipidomics

Podium Presentation in Steinbeck 2 on Wednesday at 15:30 (Chair: Bo Burla / Xueheng Zhao)

Evaluating Matrix-Specific Adduct Formation for Improved Quantitative Accuracy in Nontargeted Lipidomics

Lauren Bishop (1), Tong Shen (1), Oliver Fiehn(1)
(1) University of California, Davis, CA

Lauren Bishop (Presenter)
University of California, Davis

Presenter Bio: I am a fifth year PhD student working under the mentorship of Oliver Fiehn at UC Davis. My research interests primarily focus on improving the scope of absolute quantitation and quantitative accuracy within nontargeted lipidomic analyses.


INTRODUCTION: Neutral lipids have frequently been presented as potential biomarkers for diseases such as nonalcoholic fatty liver disease, gestational diabetes mellitus, and chronic kidney disease. With such implications, our ability to reliably detect and accurately quantify these molecules is crucial for the implementation of lipidomic analyses in the clinical lab. However, neutral lipids only become detectable in LC-electrospray mass spectrometry when forming adducts with buffer components and the mechanisms and conditions that dictate adduct formation are still poorly understood. In positive mode, lipids typically generate [M+H]+ or [M+NH4]+ adduct ions, though [M+Na]+, [M+K]+ and other (more complex) species can also be significantly abundant in MS1 precursor ion spectra. When variations in the ratios of these adduct forms are left unaccounted for, it can cause inaccuracies during quantification.

OBJECTIVES: The primary objectives of this work are to highlight the variability in adduct formation as influenced by different biological and technical factors, as well as underline the significance of adduct selection for robust and accurate quantification of neutral lipids.

METHODS: After a biphasic extraction with methanol/water/MTBE, we analyzed the lipid fractions of 8 different animal tissues by charged surface hybrid LC-QExactive HF+ tandem mass spectrometry. For each tissue type, 25 biological replicates were used. Data were processed in MS-DIAL v4.28 and lipids were annotated by MassBank.us libraries using MS/MS and retention time matching. Adduct species were identified through MS-FLO and verified by retention time and intensity correlations.

RESULTS: Quantitative analyses focused on diacylglycerols (DAG) because this lipid class regularly forms multiple adducts that are quantitatively significant, thus creating uncertainty in the accuracy when only one adduct is used. Adduct ratio variability was evaluated across individual samples, across lipid species of the same matrix type, and across lipid species annotated from different matrix types. The changes in adduct ratios for each matrix type were modeled against three potential contributing factors: carbon chain length, degree of unsaturation, and absolute peak intensity. Importantly, we found that quantitative accuracy varied between 5% and 70% for each matrix type with respect to absolute (molar) concentrations when utilizing different combinations of adducts.

CONCLUSION: The formation of specific adduct ions is influenced by a range of factors including instrument conditions, matrix type, and the structural properties of individual lipids. We found that using a single adduct form to represent an entire lipid class can significantly impact the quantitative accuracy of the data. Resulting from this work, guidelines for proper adduct selection are proposed.

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