Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis.

Autor: Guo J; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Sun D; Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China., Li K; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Dai Q; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Geng S; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Yang Y; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Mo M; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Zhu Z; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Shao C; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China., Wang W; Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China., Song J; Institute for Developmental and Regenerative Cardiovascular Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China., Yang C; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China.; Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China., Zhang H; Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China.
Jazyk: angličtina
Zdroj: Small (Weinheim an der Bergstrasse, Germany) [Small] 2024 Nov; Vol. 20 (45), pp. e2402177. Date of Electronic Publication: 2024 Jul 30.
DOI: 10.1002/smll.202402177
Abstrakt: Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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