Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.
Nat Commun
; 15(1): 2582, 2024 Mar 22.
Article
in En
| MEDLINE
| ID: mdl-38519477
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 10-10 M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer241, and GalCer241 using their untrained spectra in the models.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cerebrosides
/
Glucosylceramides
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Affiliation country:
Singapur
Country of publication:
Reino Unido