Single-Cell Identification, Drug Susceptibility Test, and Whole-genome Sequencing of Helicobacter pylori Directly from Gastric Biopsy by Clinical Antimicrobial Susceptibility Test Ramanometry.
Clin Chem
; 68(8): 1064-1074, 2022 07 27.
Article
em En
| MEDLINE
| ID: mdl-35714147
BACKGROUND: The battle against Helicobacter pylori (H. pylori) infections demands fast, reliable, and sensitive methods for pathogen identification (ID), antimicrobial susceptibility tests (ASTs) based on metabolic response, and genome-wide mutation profiling that reveals resistance mechanisms. METHODS: Here we introduce Clinical Antimicrobial Susceptibility Test Ramanometry for H. pylori (CAST-R-HP), and its validation with clinical samples. This method performs rapid ID, metabolism inhibition-based AST, and high-quality whole-genome sequencing for cells of targeted resistance phenotype, all at precisely 1-cell resolution and directly from biopsy samples. RESULTS: In CAST-R-HP, automated acquisition and machine learning of single-cell Raman spectra (SCRS) enable distinguishing individual H. pylori cells directly from a biopsy sample, with 98.5 ± 0.27% accuracy in ID. Moreover, by adding a 48- to72-h D2O feeding and drug exposure step prior to SCRS acquisition, CAST-R-HP reports AST for levofloxacin and clarithromycin with 100% accuracy, based on metabolic inhibition level. Furthermore, CAST-R-HP supports rapid sorting, low-bias DNA amplification, and full genome sequencing of single H. pylori cells with the SCRS defined, targeted drug-susceptibility phenotype, via Raman-activated gravity-driven cell encapsulation and sequencing. The genome-wide mutation map (maximum 99.70% coverage), at precisely 1-cell resolution, not only elucidates the drug-susceptibility phenotypes but also unveils their underlying molecular mechanisms. CONCLUSION: The culture independency, shorter turnaround time, high resolution, and comprehensive information output suggest that CAST-R-HP is a powerful tool for diagnosing and treating H. pylori infections.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article