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Date of Award
3-21-2013
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Bioinformatics
First Advisor
Jerry Darsey
Abstract
Rapid Identification of Bacteria Using Mass Spectrometry and Spectral Pattern Recognition A novel atmospheric pressure ionization process, namely Direct Impact Ionization (DII), is used to ionize bacteria. Electric discharges impinge on an indentation from underneath a stainless steel wire mesh holding a thin dried film of whole bacterial cells, the analyte. The latter is immersed in (350 oC) hot He gas flow. Biomolecular ions as heavy as 560 m/z are generated. Mass spectra are obtained and extracted with a high degree of reproducibility. A bioinformatic methodology for effective representation of spectral data that incorporates noise reduction was developed for this purpose. Other analytical factors (sample size, lower detection limit, reducing contamination, dynamic range, and spectrum extraction) are also investigated. Data pre-processing (spectral smoothing, baseline correction, background subtraction, and binning of spectra) is addressed in this work. Custom-made OMNIPrintTM screening software enables rapid screening of spectra stored in a library. This software compares binned spectra through spectral similarity calculations based on ion-intensity variances among the spectra. Screening for best or close matches is explained. The performance of other commercially-available software packages is tested in parallel. An unknown Bacillus sample was successfully identified using spectra pattern recognition techniques, based on spectral comparison and principal component analysis used in conjunction with linear regression. The unknown Bacillus was later characterized using traditional microbiology methods. The established Bioinformatics & Computational Biology-based reference spectral platform for accurate and rapid identification of bacterial species and sub-species was put to test. Members of the Bacillus cereus group, such as Bacillus cereus, are easily mistaken for Bacillus anthracis. The proposed methodology proved to be potentially useful to rapidly detect and identify Bacillus anthracis Sterne strain in a complex mixture such as airborne dust. This successful achievement paves the path for this particular Bioinformatics & Computational Biology tool - spectral pattern recognition - to be effectively used in monitoring of biological warfare agents, outbreaks of infectious diseases in public venues, in quality control of food processing plants, and in clinics to assist microbiology testing.
Recommended Citation
Alusta, Pierre, "Rapid Identification of Bacteria Using Mass Spectrometry and Spectral Pattern Recognition" (2013). Theses and Dissertations. 406.
https://research.ualr.edu/etd/406
