Direct MALDI-TOF MS Identification of Bacterial Mixtures
Yi Yang
Yu Lin
Liang Qiao
10.1021/acs.analchem.8b02258.s005
https://acs.figshare.com/articles/dataset/Direct_MALDI-TOF_MS_Identification_of_Bacterial_Mixtures/6998336
Matrix-assisted
laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF MS) is now widely used to characterize bacterial
samples for clinical diagnosis, food safety control, environmental
monitoring, and so on. However, existing standard approaches are only
applied to analyze single colonies purified by plate culture, which
limits the approaches to cultivable bacteria and makes the whole approaches
time-consuming. In this work, we propose a new framework to analyze
MALDI-TOF spectra of bacterial mixtures and to directly characterize
each component without purification procedures. The framework is a
combination of a synthetic mixture model based on a non-negative linear
combination of candidate reference spectra and a statistical assessment
by in silico generated spectra via a jackknife resampling. Ninety-seven
model bacterial mixture samples and 8 cocultured blind-coded bacterial
mixture samples, containing up to 6 strains in varied ratios in each
sample, together with a reference database containing the mass spectra
of 1081 strains, were used to validate the framework. High sensitivity
(>80%, with error rate <5%) was achieved for balanced binary
and
ternary mixtures. The sensitivity was >60% for balanced quaternary
and pentabasic mixtures, and 48%–71% for asymmetric situation,
with error rate <5%. The work can facilitate rapid and reliable
characterization of bacterial mixtures without purification procedures,
which is of practical value in clinical diagnosis, food safety control,
environmental monitoring, and so on. The framework can be further
applied to many other spectroscopy-based analytics to interpret spectra
from mixed samples.
2018-08-09 00:00:00
framework
food safety control
mixture samples
approach
purification procedures
8 cocultured blind-coded
Direct MALDI-TOF MS Identification
candidate reference spectra