A. Peter Snyder

Interactive self-modeling multivariate analysis

Abstract Windig, W., Lippert, J.L., Robbins, M.J., Kresinske, K.R., Twist, J.P. and Snyder, A.P., 1990. Interactive self-modeling multivariate analysis. Chemometrics and Intelligent Laboratory Systems, 9: 7–30. In many practical applications in a laboratory, the data output from the analytical instruments is very complex. For example, a series of spectra following a reaction in time may contain information on intermediate and/or new products, of which no model spectra are available. In these kinds of cases, such standard data analysis tools as librrary search or subtraction methods cannot be applied. In order to analyze this type of data properly, self-modeling multivariate data analysis techniques have been developed. These techniques are capable of extracting spectra of the pure components from a data set of mixtures, without using prior knowledge of the pure components. This paper will explain a new approach for self-modeling multivariate analysis. The techniques involved will be explained by geometrical means. Examples of analyses of the data resulting from monitoring reactions in time will be shown. The first data set consist of Raman spectra monitoring the formation of silica glasses from a solution. The second data set consists of Fourier transform infrared data monitoring a reaction that produces a widely used ingredient in photography. The data have been analyzed with the Interactive Self-modeling Multivariate Analysis (ISMA) package, which is a higly interactive graphics oriented set of programs developed for this approach.

Detection of the picolinic acid biomarker in Bacillus spores using a potentially field‐portable pyrolysis—gas chromatography—ion mobility spectrometry system

The absence of a field-portable device to provide real-time detection of Gram-positive bacterial spores has prompted the interfacing of a pyrolysis (Py) module to an existing, hand-held gas-chromatography—ion-mobility spectrometry (GC/IMS) device. In this configuration, spore detection is achieved by the observation of picolinic (2-pyridinecarboxylic) acid (PA), which is the most characteristic pyrolysis decomposition product of the parent dipicolinic (2,6-pyridinedicarboxylic) acid (DPA). Positive identification of PA was demonstrated using a laboratory-based GC instrument with dual, parallel mass spectrometry (MS) and IMS detectors. Spores and vegetative microorganisms of the genus Bacillus were characterized by the presence and absence of DPA, respectively, and the picolinic acid marker was identified from the GC/IMS and GC/MS profiles. A field-portable prototype Py-GC/IMS system is described and appears to provide similar bioanalytical information with respect to the laboratory-based system. Preliminary results of this study indicate that the degree of compound separation afforded by a short GC capillary column guards against common environmental interferences including urban particulate matter and biological particles such as fungal spores and pollen.

Classification and identification of bacteria using mass spectrometry-based proteomics.

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.

Field‐portable, automated pyrolysis‐GC/IMS system for rapid biomarker detection in aerosols: A feasibility study

A prototype automated pyrolysis-gas chromatography/ion mobility spectrometry (Py-GC/IMS) instrument was developed for (bio)aerosol characterization. The system combines a commercially available, hand-held GC/IMS device with a specially built platinum wire grid heater, a 1-in.-diam. quartz microfiber filter and a 60-l/min air pump. The prototype Py-GC/IMS system can be operated in stand-alone mode or in series with a particle concentrator. Fully automated collection/desorption/pyrolysis of aerosols and other particulate matter can be performed at repetition rates of up to 60 h−1 by means of a special remote control and display software package. The feasibility of detecting submicrogram quantities of Bacillus endospores with the use of picolinic acid and pyridine as biochemical marker compounds for the characteristic dipicolinic acid moiety in spore cell walls was demonstrated by laboratory experiments as well as preliminary field tests. Other particulate matter that could be collected and analyzed includes allergens such as pollen or home dust, as well as a broad range of bioaerosols and reaerosolized organics like explosives or drugs. In addition to its potential use as a screening device for the presence of specific classes of aerosol components, the Py-GC/IMS system has been demonstrated to retain its capability to detect and identify a broad range of volatile and semivolatile organic compounds.

Double-Blind Characterization of Non-Genome-Sequenced Bacteria by Mass Spectrometry-Based Proteomics

ABSTRACT Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.

Waterborne Pathogen Detection Using Raman Spectroscopy

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection. We have investigated the impact of key experimental and background interference parameters on the bacterial species level identification performance of Raman detection. These parameters include laser-induced photodamage threshold, composition of water matrix, and organism aging in water. The laser-induced photodamage may be minimized by operating a 532 nm continuous wave laser excitation at laser power densities below 2300 W/cm2 for Gram-positive Bacillus atrophaeus (formerly Bacillus globigii, BG) vegetative cells, 2800 W/cm2 for BG spores, and 3500 W/cm2 for Gram-negative E. coli (EC) organisms. In general, Bacillus spore microorganism preparations may be irradiated with higher laser power densities than the equivalent Bacillus vegetative preparations. In order to evaluate the impact of background interference and organism aging, we selected a biomaterials set comprising Gram-positive (anthrax simulants) organisms, Gram-negative (plague simulant) organisms, and proteins (toxin simulants) and constructed a Raman signature classifier that identifies at the species level. Subsequently, we evaluated the impact of tap water and storage time in water (aging) on the classifier performance when characterizing B. thuringiensis spores, BG spores, and EC cell preparations. In general, the measured Raman signatures of biological organisms exhibited minimal spectral variability with respect to the age of a resting suspension and water matrix composition. The observed signature variability did not substantially degrade discrimination performance at the genus and species levels. In addition, Raman chemical imaging spectroscopy was used to distinguish a mixture of BG spores and EC cells at the single cell level.

Performance advances in ion mobility spectrometry through combination with high speed vapor sampling, preconcentration and separation techniques

Abstract Rugged, low weight, hand-held ion mobility spectrometry devices, initially developed for chemical warfare detection purposes, possess attractive characteristics as field-portable instruments for paramilitary (treaty verification, chemical demilitarization, drug interdiction, counterterrorism operations) and civilian (environmental monitoring, forensic characterization, process control) applications. Generally, however, such devices tend to exhibit limited resolution, narrow dynamic range, nonlinear response and long clearance times which severely limit their usefulness for qualitative and quantitative analysis of mixtures. To overcome these restrictions a prototype combined gas chromatography-ion mobility spectrometry (GC-IMS) unit was constructed by replacing the membrane inlet of a military IMS device known as the CAM (chemical agent monitor) with suitable front-end modules. These modules enable high speed automated vapor sampling (AVS), microvolume preconcentration/thermal desorption, and isothermal GC preseparation of analytes using a short capillary column while operating the IMS source and cell at subambient pressures as low as 0.5 atm. The AVS-GC-IMS methodology sharply reduces competitive ionization and facilitates identification of mixture components, thereby enabling quantitation of volatile and semivolatile compounds over a broad range of concentration in air. At higher concentration levels (e.g. > 1 ppm) using the AVS inlet in automatic attenuation control (AAC) mode maintains excellent linear response. At ultralow concentration levels, e.g.

Field detection of bacillus spore aerosols with stand‐alone pyrolysis–gas chromatography–ion mobility spectrometry

A commercially available, hand-held chemical vapor detector was modified to detect gram-positive Bacillus subtilis var. globigii spores (BG) in outdoor field scenarios. An airborne vapor monitor (AVM) ion mobility spectrometry (IMS) vapor detector was interfaced to a biological sample processing and transfer introduction system. The biological sample processing was accomplished by quartz tube pyrolysis (Py), and the resultant vapor was transferred by gas chromatography (GC) to the IMS detector. The Py-GC/IMS system can be described as a hyphenated device where two analytical dimensions, in series, allow the separation and isolation of individual components from the pyrolytic decomposition of biological analytes. Gram-positive spores such as BG contain 5–15% by weight of dipicolinic acid (DPA), and picolinic acid is a pyrolysis product of DPA. Picolinic acid has a high proton affinity, and it is detected in a sensitive fashion by the atmospheric pressure-based IMS device. Picolinic acid occupies a unique region in the GC/IMS data domain with respect to other bacterial pyrolysis products. A 1000 to 1, air-to-air aerosol concentrator was interfaced to the Py-GC/IMS instrument, and the system was placed in an open-air, western United States desert environment. The system was tested with BG spore aerosol releases, and the instrument was remotely operated during a trial. A Met-One aerosol particle counter was placed next to the Py-GC/IMS so as to obtain a real-time record of the ambient and bacterial aerosol challenges. The presence/absence of an aerosol event, determined by an aerosol particle counter and a slit-sampler–agar-plate system, was compared to the presence/absence of a picolinic acid response in a GC/IMS data window at selected times in a trial with respect to a BG challenge. In the 21 BG trials, the Py-GC/IMS instrument experienced two true negatives and no false positives, and developed a software failure in one trial. The remaining 18 trials were true positive determinations for the presence of BG aerosol, and a limit of detection for the Py-GC/IMS instrument was estimated at approximately 3300 BG spore-containing particles.

Identification of Yersinia pestis and Escherichia coli strains by whole cell and outer membrane protein extracts with mass spectrometry-based proteomics.

Whole cell protein and outer membrane protein (OMP) extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest neighbor database organism strains. These extracts were isolated from pathogenic and nonpathogenic strains of Yersinia pestis and Escherichia coli using ultracentrifugation and a sarkosyl extraction method followed by protein digestion and analysis using liquid chromatography tandem mass spectrometry (MS). Whole cell protein extracts contain many different types of proteins resident in an organism at a given phase in its growth cycle. OMPs, however, are often associated with virulence in Gram-negative pathogens and could prove to be model biomarkers for strain differentiation among bacteria. The mass spectra of bacterial peptides were searched, using the SEQUEST algorithm, against a constructed proteome database of microorganisms in order to determine the identity and number of unique peptides for each bacterial sample. Data analysis was performed with the in-house BACid software. It calculated the probabilities that a peptide sequence assignment to a product ion mass spectrum was correct and used accepted spectrum-to-sequence matches to generate a sequence-to-bacterium (STB) binary matrix of assignments. Validated peptide sequences, either present or absent in various strains (STB matrices), were visualized as assignment bitmaps and analyzed by the BACid module that used phylogenetic relationships among bacterial species as part of a decision tree process. The bacterial classification and identification algorithm used assignments of organisms to taxonomic groups (phylogenetic classification) based on an organized scheme that begins at the phylum level and follows through the class, order, family, genus, and species to the strain level. For both Gram-negative organisms, the number of unique distinguishing proteins arrived at by the whole cell method was less than that of the OMP method. However, the degree of differentiation measured in linkage distance units on a dendrogram with the OMP extract showed similar or significantly better separation than the whole cell protein extract method between the sample and correct database match compared to the next nearest neighbor. The nonpathogenic Y. pestis A1122 strain used does not have its genome available, and thus, data analysis resulted in an equal similarity index to the nonpathogenic 91001 and pathogenic Antiqua and Nepal 516 strains for both extraction methods. Pathogenic and nonpathogenic strains of E. coli were correctly identified with both protein extraction methods, and the pathogenic Y. pestis CO92 strain was correctly identified with the OMP procedure. Overall, proteomic MS proved useful in the analysis of unique protein assignments for strain differentiation of E. coli and Y. pestis. The power of bacterial protein capture by the whole cell protein and OMP extraction methods was highlighted by the data analysis techniques and revealed differentiation and similarities between the two protein extraction approaches for bacterial delineation capability.

Chemotaxonomic differentiation between the Bacillus cereus group and Bacillus subtilis by phospholipid extracts analyzed with electrospray ionization tandem mass spectrometry

Abstract Electrospray ionization tandem mass spectrometry (ESI MS–MS) was developed for analysis of bacterial glycerophospholipids and was applied to bacterial differentiation. Bacillus anthracis , B. cereus , B. thuringiensis (referred to as the B. cereus group) and B. subtilis are four closely related members of the Bacillus genus. Conventional taxonomic methods, as well as whole cell carbohydrate profiling and fatty acid profiling are sufficient to differentiate between the B. cereus group and B. subtilis . Phospholipids are the main source of fatty acids in the bacterial cell and therefore should maintain the discriminating power of the fatty acid data. Under negative ionization conditions, phospholipids form predominantly deprotonated molecular ions. The principal bacterial phospholipids detected using negative ion ESI are phosphatidylglycerols (PGs). Chloroform:methanol extraction of whole bacterial cells resulted in ESI mass spectra. The product ion mass spectra of deprotonated PGs provided the molecular weights of the fatty acid moieties. The distribution of fatty acids for the different PG species, as well as the different profiles of PGs, allowed differentiation between the B. cereus group and B. subtilis and this could provide a chemotaxonomic method for bacterial differentiation.