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Quantitative detection of isotopically enriched E. coli cells by SERS.

Malama Chisanga1, Howbeer Muhamadali, Richard Kimber

  • 1School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK. roy.goodacre@manchester.ac.uk.

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Summary

This study uses stable isotope probing and surface-enhanced Raman scattering (SERS) to track bacterial metabolism. Researchers successfully identified and quantified isotopic labeling in Escherichia coli, linking microbial function to phylogeny.

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Area of Science:

  • Microbiology
  • Spectroscopy
  • Biochemistry

Background:

  • Understanding bacterial functional roles is crucial in environmental, clinical, and industrial microbiology.
  • Linking genes to functions aids disease identification and wastewater treatment optimization.
  • Stable isotope probing and spectroscopy are key techniques for microbial characterization.

Purpose of the Study:

  • To investigate the application of surface-enhanced Raman scattering (SERS) for analyzing microbial metabolic processes in situ.
  • To correlate SERS spectral data with isotopic labeling (13C and 15N) in bacterial cells.
  • To establish a method for linking microbial phylogeny to bioprocesses using SERS and chemometrics.

Main Methods:

  • Cultivation of Escherichia coli in minimal media with varying ratios of 12C/13C glucose and 14N/15N ammonium chloride.
  • In situ synthesis of silver nanoparticles for surface-enhanced Raman scattering (SERS) analysis of bacterial cells.
  • Multivariate chemometric analysis, including partial least squares regression, of SERS spectral data.

Main Results:

  • SERS analysis revealed distinct spectral clusters corresponding to isotopic labeling (13C and 15N) in *E. coli*.
  • Spectral shifts in biomolecules directly correlated with the isotopic content of the cells.
  • Quantification of isotope levels was achievable using chemometrics based on partial least squares regression.

Conclusions:

  • SERS is a powerful technique for in situ analysis of microbial metabolic processes and isotopic labeling.
  • The developed method enables the correlation of microbial phylogeny with specific bioprocesses.
  • This approach offers a novel way to understand and potentially manipulate microbial communities for various applications.