Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

Kate E. Stephen, Darren Homrighausen, Glen Depalma, Cindy H. Nakatsu, Joseph Irudayaraj

Research output: Contribution to journalArticle

Abstract

Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.

Original languageEnglish (US)
Pages (from-to)4280-4286
Number of pages7
JournalAnalyst
Volume137
Issue number18
DOIs
StatePublished - Sep 21 2012
Externally publishedYes

Fingerprint

Arthrobacter
Raman Spectrum Analysis
Raman spectroscopy
Surface structure
Chromates
rRNA Genes
chromate
Genes
Discriminant Analysis
Principal Component Analysis
Liquids
Discriminant analysis
Culture Media
liquid
gene
Principal component analysis
discriminant analysis
Bacteria
principal component analysis
environmental change

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Environmental Chemistry
  • Spectroscopy
  • Electrochemistry

Cite this

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition. / Stephen, Kate E.; Homrighausen, Darren; Depalma, Glen; Nakatsu, Cindy H.; Irudayaraj, Joseph.

In: Analyst, Vol. 137, No. 18, 21.09.2012, p. 4280-4286.

Research output: Contribution to journalArticle

Stephen, Kate E. ; Homrighausen, Darren ; Depalma, Glen ; Nakatsu, Cindy H. ; Irudayaraj, Joseph. / Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition. In: Analyst. 2012 ; Vol. 137, No. 18. pp. 4280-4286.
@article{30ba442b37714214b7a9fec8a1e2b901,
title = "Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition",
abstract = "Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97{\%} accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.",
author = "Stephen, {Kate E.} and Darren Homrighausen and Glen Depalma and Nakatsu, {Cindy H.} and Joseph Irudayaraj",
year = "2012",
month = "9",
day = "21",
doi = "10.1039/c2an35578g",
language = "English (US)",
volume = "137",
pages = "4280--4286",
journal = "Analyst",
issn = "0003-2654",
publisher = "Royal Society of Chemistry",
number = "18",

}

TY - JOUR

T1 - Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

AU - Stephen, Kate E.

AU - Homrighausen, Darren

AU - Depalma, Glen

AU - Nakatsu, Cindy H.

AU - Irudayaraj, Joseph

PY - 2012/9/21

Y1 - 2012/9/21

N2 - Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.

AB - Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.

UR - http://www.scopus.com/inward/record.url?scp=84865043139&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865043139&partnerID=8YFLogxK

U2 - 10.1039/c2an35578g

DO - 10.1039/c2an35578g

M3 - Article

C2 - 22842541

AN - SCOPUS:84865043139

VL - 137

SP - 4280

EP - 4286

JO - Analyst

JF - Analyst

SN - 0003-2654

IS - 18

ER -