Determining bacterial
identity at the strain level is critical
for public health to enable proper medical treatments and reduce antibiotic
resistance. Herein, we used liquid chromatography, ion mobility, and
tandem MS (LC-IM-MS/MS) to distinguish Escherichia
coli (E. coli)
strains. Numerical multivariate statistics (principal component analysis,
followed by linear discriminant analysis) showed the capability of
this method to perform strain-level discrimination with prediction
rates of 96.1% and 100% utilizing the negative and positive ion information,
respectively. The tandem MS and LC separation proved effective in
discriminating diagnostic lipid isomers in the negative mode, while
IM separation was more effective in resolving lipid conformational
biomarkers in the positive ion mode. Because of the clinical importance
of early detection for rapid medical intervention, a faster technique,
paper spray (PS)-IM-MS/MS, was used to discriminate the E. coli strains. The achieved prediction rates
of the analysis of E. coli strains
by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion
modes, respectively. The strategy of numerical data fusion of negative
and positive ion data increased the classification rates of PS-IM-MS/MS
to 80.5%. Lipid isomers and conformers were detected, which served
as strain-indicating biomarkers. The two complementary multidimensional
techniques revealed biochemical differences between the E. coli strains confirming the results obtained
from comparative genomic analysis. Moreover, the results suggest that
PS-IM-MS/MS is a rapid, highly selective, and sensitive method for
discriminating bacterial strains in environmental and food samples.