Analytica chimica acta · 2025
DIATAGeR: Triacylglycerol annotation of data-independent acquisition based lipidomics.
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Lee Victor C L, Nguyen Khanh C K, Zhu Linglan, White Chloe A K, Lim Yong Jin, Huan Tao, Velenosi Thomas J
Аннотация
BACKGROUND: Triacylglycerols (TGs) are the most abundant lipids in the human body and the primary source of energy storage. TGs are comprised of three fatty acyls with various lengths and double bond composition, complicating structural annotation when performing lipidomics by LCMS. Data-independent acquisition (DIA) based lipidomics enables a continuous and unbiased acquisition of all TGs, creating the potential for more comprehensive TG analysis. However, TG identification in DIA lipidomics data is challenging due to the difficulty analyzing multiplexed tandem mass spectra (MS2).
RESULTS: In this study, we present DIATAGeR, an R package aimed to improve and automate TG identifications to the molecular species level in DIA-based lipidomics. With DIATAGeR, TGs are identified using a TG-centric approach, where each TG in the reference database is considered as an analysis target, searched in DIA spectra, and scored using a logistic regression machine learning algorithm. Additionally, DIATAGeR uses a false discovery rate (FDR) correction calculated by a target-decoy approach to improve the confidence of TG identification and limit false positives due to interference from unrelated ions. The performance of DIATAGeR was validated in a lipidomic study of liver and plasma samples from mice with metabolic dysfunction-associated steatohepatitis (MASH) and healthy controls. All 9 TG standards were annotated at an FDR <0.1 in both datasets. When benchmarked against MS-DIAL, TGs identified by DIATAGeR contained 18 % and 12 % more even-carbon fatty acyls in liver and plasma datasets, respectively.
SIGNIFICANCE: DIATAGeR is a valuable tool for streamlining complex TG annotation in DIA-lipidomics data. It supports vendor-neutral MS spectra data formats and offers a customizable reference database. By combining TG-centric and target-decoy approaches, DIATAGeR showed improvements in TG identification by addressing primary challenges associated with multiplexed MS2 spectra. DIATAGeR is freely available at https://github.com/Velenosi-Lab/DIATAGeR.
RESULTS: In this study, we present DIATAGeR, an R package aimed to improve and automate TG identifications to the molecular species level in DIA-based lipidomics. With DIATAGeR, TGs are identified using a TG-centric approach, where each TG in the reference database is considered as an analysis target, searched in DIA spectra, and scored using a logistic regression machine learning algorithm. Additionally, DIATAGeR uses a false discovery rate (FDR) correction calculated by a target-decoy approach to improve the confidence of TG identification and limit false positives due to interference from unrelated ions. The performance of DIATAGeR was validated in a lipidomic study of liver and plasma samples from mice with metabolic dysfunction-associated steatohepatitis (MASH) and healthy controls. All 9 TG standards were annotated at an FDR <0.1 in both datasets. When benchmarked against MS-DIAL, TGs identified by DIATAGeR contained 18 % and 12 % more even-carbon fatty acyls in liver and plasma datasets, respectively.
SIGNIFICANCE: DIATAGeR is a valuable tool for streamlining complex TG annotation in DIA-lipidomics data. It supports vendor-neutral MS spectra data formats and offers a customizable reference database. By combining TG-centric and target-decoy approaches, DIATAGeR showed improvements in TG identification by addressing primary challenges associated with multiplexed MS2 spectra. DIATAGeR is freely available at https://github.com/Velenosi-Lab/DIATAGeR.