Rapid chemotaxonomy of pathogenic bacteria using in situ thermal hydrolysis and methylation as a sample preparation step coupled with a field–portable membrane-inlet quadrupole ion trap mass spectrometer

Hendricker, Alan D.; Abbas‐Hawks, Christy; Basile, Franco; Voorhees, Kent J.; Hadfield, Ted L.

doi:10.1016/s1387-3806(98)14266-5

Cited by 28 publications

(24 citation statements)

References 26 publications

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“…Four strains (vollum, sternes, ames, zimbabwe) of B. anthracis grown on two different media, both vegetative cells and spores, were pyrolyzed [47]. Fatty acids that were important in the detection included the following chain lengths: saturated 12, 14-22, and 24; monounsaturated 16, 18, 24; cyclic 17 and 19; iso-15, 17; and anteiso 15.…”

Section: Cellsmentioning

confidence: 99%

Miniature Sensors for Biological Warfare Agents using Fatty Acid Profiles: LDRD 10775 Final Report

Mowry¹,

Morgan²,

Frye-Mason³

et al. 2003

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Rapid detection and identification of bacteria and other pathogens is important for many civilian and military applications. The taxonomic significance, or the ability to differentiate one microorganism from another, using fatty acid content and distribution is well known. For analysis fatty acids are usually converted to fatty acid methyl esters (FAMEs). Bench-top methods are commercially available and recent publications have demonstrated that FAMEs can be obtained from whole bacterial cells in an in situ single-step pyrolysis/methylation analysis.This report documents the progress made during a three year Laboratory Directed Research and Development (LDRD) program funded to investigate the use of microfabricated components (developed for other sensing applications) for the rapid identification of bioorganisms based upon pyrolysis and FAME analysis. Components investigated include a micropyrolyzer, a microGC, and a surface acoustic wave (SAW) array detector. Results demonstrate that the micropyrolyzer can pyrolyze whole cell bacteria samples using only milliwatts of power to produce FAMEs from bacterial samples. The microGC is shown to separate FAMEs of biological interest, and the SAW array is shown to detect volatile FAMEs. Results for each component and their capabilities and limitations are presented and discussed. This project has produced the first published work showing successful pyrolysis/methylation of fatty acids and related analytes using a microfabricated pyrolysis device. 4 AcknowledgmentsThe authors would like to acknowledge the team that provided this project with components, without their work this LDRD would not have existed:

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Section: Cellsmentioning

confidence: 99%

Miniature Sensors for Biological Warfare Agents using Fatty Acid Profiles: LDRD 10775 Final Report

Mowry¹,

Morgan²,

Frye-Mason³

et al. 2003

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“…These samples were prepared as 10 mg/mL water suspensions. For mass spectral analysis, 15 L of the bacterial suspension was coinjected with 5 L of 1 M tetramethylammonium hydroxide (TMAH) in water immediately prior to pyrolysis [11,21]. TMAH and similar derivatization reagents have been used on bacteria to chemically lyse the biopolymers and increase the volatility of analytes, allowing analysis by gas chromatography and mass spectrometry [22,23].…”

Section: Reagents and Instrumentationmentioning

confidence: 99%

“…TMAH and similar derivatization reagents have been used on bacteria to chemically lyse the biopolymers and increase the volatility of analytes, allowing analysis by gas chromatography and mass spectrometry [22,23]. This reagent also enhances membrane permeability, as the methylation process forms products that are less polar than the underivatized species [21]. All analyses were performed using a chemical/ biological mass spectrometer (CBMS) that is an air buffered quadrupole ion trap mass spectrometer fitted with an infrared pyrolyzer (Bruker-Franzen, Bremen, Germany) [21].…”

Section: Reagents and Instrumentationmentioning

confidence: 99%

“…This reagent also enhances membrane permeability, as the methylation process forms products that are less polar than the underivatized species [21]. All analyses were performed using a chemical/ biological mass spectrometer (CBMS) that is an air buffered quadrupole ion trap mass spectrometer fitted with an infrared pyrolyzer (Bruker-Franzen, Bremen, Germany) [21]. See Figure 1 for a schematic of the instrument.…”

Section: Reagents and Instrumentationmentioning

confidence: 99%

“…Ten spectra were removed from this data set, because they were of poor quality. Further details may be found in Hendricker et al [21]. The Bacillus anthracis spores and cells were grouped into separate classes because their mass spectra were different.…”

Section: Evaluation Of the Neural Network Modelsmentioning

confidence: 99%

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Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra

Harrington

Voorhees

Basile

et al. 2002

J. Am. Soc. Mass Spectrom.

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Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 Ϯ 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities. (J Am Soc Mass Spectrom 2002, 13, 10 -21)

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Sample Preparation in Membrane Introduction Mass Spectrometry

Ketola

Kotiaho

Lauritsen

2010

Handbook of Sample Preparation

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Rapid chemotaxonomy of pathogenic bacteria using in situ thermal hydrolysis and methylation as a sample preparation step coupled with a field–portable membrane-inlet quadrupole ion trap mass spectrometer

Cited by 28 publications

References 26 publications

Miniature Sensors for Biological Warfare Agents using Fatty Acid Profiles: LDRD 10775 Final Report

Miniature Sensors for Biological Warfare Agents using Fatty Acid Profiles: LDRD 10775 Final Report

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra

Sample Preparation in Membrane Introduction Mass Spectrometry

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