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Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

ABSTRACT: Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints. Corroborating with in-silico experiments, we establish five spectral features, each corresponding to a structural characteristic: (1) presence/absence of epimers, (2) monosaccharide/cerebroside, (3) saturated/unsaturated cerebroside, (4) glucosyl/galactosyl, and (5) GlcCer or GalCer's carbon chain lengths. Leveraging these insights, we create a fully generalizable framework to identify and quantify cerebrosides at concentrations between 10^-4 to 10^-10M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer_24:1, and GalCer_24:1 using their untrained spectra in the models.

SUBMITTER: Tan EX

PROVIDER: S-EPMC10960001 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

Tan Emily Xi EX Leong Shi Xuan SX Liew Wei An WA Phang In Yee IY Ng Jie Ying JY Tan Nguan Soon NS Lee Yie Hou YH Ling Xing Yi XY

Nature communications 20240322 1

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture th ...[more]

PMID: 38519477

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Project description:Cerebrosides (n-HexCer) and glycosphingosines (n-HexSph) constitute two sphingolipid subclasses. Both are comprised of a monosaccharide headgroup (glucose or galactose in mammalian cells) linked via either an α- or β-glycosidic linkage to the sphingoid backbone (n = α or β, depending upon the nature of the linkage to the anomeric carbon of the sugar). Cerebrosides have an additional amide-bonded fatty acyl chain linked to the sphingoid backbone. While differentiating the multiple isomers (i.e. glucose vs galactose, α- vs β-linkage) is difficult, it is crucial for understanding their specific biological roles in health and disease states. Shotgun tandem mass spectrometry has been a powerful tool in both lipidomics and glycomics analysis but is often limited in its ability to distinguish isomeric species. This work describes a new strategy combining shotgun tandem mass spectrometry with gas-phase ion chemistry to achieve both differentiation and quantification of isomeric cerebrosides and glycosphingosines. Briefly, deprotonated cerebrosides, [n-HexCer-H]-, or glycosphingosines, [n-HexSph-H]-, are reacted with terpyridine (Terpy) magnesium complex dications, [Mg(Terpy)2]2+, in the gas phase to produce a charge-inverted complex cation, [n-HexCer-H+MgTerpy]+ or [n-HexSph-H+MgTerpy]+. The collision-induced dissociation (CID) of the charge-inverted complex cations leads to significant spectral differences between the two groups of isomers, α-GalCer, β-GlcCer, and β-GalCer for cerebrosides and α-GlcSph, α-GalSph, β-GlcSph, and β-GalSph for glycosphingosines, which allows for isomer distinction. Moreover, we describe a quantification strategy with the normalized percent area extracted from selected diagnostic ions that quantify either three isomeric cerebroside or four isomeric glycosphingosine mixtures. The analytical performance was also evaluated in terms of accuracy, repeatability, and interday precision. Furthermore, CID of the product ions resulting from 443 Da loss from the charge-inverted complex cations ([n-HexCer-H+MgTerpy]+) has been performed and demonstrated for localization of the double-bond position on the amide-bonded monounsaturated fatty acyl chain in the cerebroside structure. The proposed strategy was successfully applied to the analysis of total cerebroside extracts from the porcine brain, providing in-depth structural information on cerebrosides from a biological mixture.

Dataset Information

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

Publications

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

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