Novel technology for rapid species-specific detection of Bacillus spores

Immediate and sensitive detection of sporulated Bacillus subtilis by microwave release and tandem mass spectrometry of dipicolinic acid

Spore lysis of Bacillus species is achieved by brief (1 min) microwave irradiation while tandem mass spectrometry (MS/MS) allows identification of the characteristic spore marker, dipicolinic acid. This rapid measurement, made on 10⁵-10⁸ spores, has significant implications for biothreat recognition.

Modular and reconfigurable gas chromatography / differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs)

Due to the versatility of present day microcontroller boards and open source development environments, new analytical chemistry devices can now be built outside of large industry and instead within smaller individual groups. While there are a wide range of commercial devices available for detecting and identifying volatile organic compounds (VOCs), most of these devices use their own proprietary software and complex custom electronics, making modifications or reconfiguration of the systems challenging. The development of microprocessors for general use, such as the Arduino prototyping platform, now enables custom chemical analysis instrumentation. We have created an example system using commercially available parts, centered around on differential mobility spectrometer (DMS) device. The Modular Reconfigurable Gas Chromatography - Differential Mobility Spectrometry package (MR-GC-DMS) has swappable components allowing it to be quickly reconfigured for specific application purposes as well as broad, generic use. The MR-GC-DMS has a custom user-friendly graphical user interface (GUI) and precisely tuned proportional-integral-derivative controller (PID) feedback control system managing individual temperature-sensitive components. Accurate temperature control programmed into the microcontroller greatly increases repeatability and system performance. Together, this open-source platform enables researchers to quickly combine DMS devices in customized configurations for new chemical sensing applications.

Proteomic Methods of Detection and Quantification of Protein Toxins

Biological toxins are a heterogeneous group of compounds that share commonalities with biological and chemical agents. Among them, protein toxins represent a considerable, diverse set. They cover a broad range of molecular weights from less than 1000 Da to more than 150 kDa. This review aims to compare conventional detection methods of protein toxins such as in vitro bioassays with proteomic methods, including immunoassays and mass spectrometry-based techniques and their combination. Special emphasis is given to toxins falling into a group of selected agents, according to the Centers for Disease Control and Prevention, such as Staphylococcal enterotoxins, Bacillus anthracis toxins, Clostridium botulinum toxins, Clostridium perfringens epsilon toxin, ricin from Ricinus communis, Abrin from Abrus precatorius or control of trade in dual-use items in the European Union, including lesser known protein toxins such as Viscumin from Viscum album. The analysis of protein toxins and monitoring for biological threats, i.e., the deliberate spread of infectious microorganisms or toxins through water, food, or the air, requires rapid and reliable methods for the early identification of these agents.

Supervised Semi-Automated Data Analysis Software for Gas Chromatography / Differential Mobility Spectrometry (GC/DMS) Metabolomics Applications

Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.

Quantitative and sensitive RNA based detection of Bacillus spores

The fast and reliable detection of bacterial spores is of great importance and still remains a challenge. Here we describe a direct RNA-based diagnostic method for the specific detection of viable bacterial spores which does not depends on an enzymatic amplification step and therefore is directly appropriate for quantification. The procedure includes the following steps: (i) heat activation of spores, (ii) germination and enrichment cultivation, (iii) cell lysis, and (iv) analysis of 16S rRNA in crude cell lysates using a sandwich hybridization assay. The sensitivity of the method is dependent on the cultivation time and the detection limit; it is possible to detect 10 spores per ml when the RNA analysis is performed after 6 h of enrichment cultivation. At spore concentrations above 10(6) spores per ml the cultivation time can be shortened to 30 min. Total analysis times are in the range of 2-8 h depending on the spore concentration in samples. The developed procedure is optimized at the example of Bacillus subtilis spores but should be applicable to other organisms. The new method can easily be modified for other target RNAs and is suitable for specific detection of spores from known groups of organisms.

A mobile instrumentation platform to distinguish airway disorders

Asthma and chronic obstructive pulmonary disease (COPD) are distinct but clinically overlapping airway disorders which often create diagnostic and therapeutic dilemmas. Current strategies to discriminate these diseases are limited by insensitivity and poor performance due to biologic variability. We tested the hypothesis that a gas chromatograph/differential mobility spectrometer (GC/DMS) sensor could distinguish between clinically well-defined groups with airway disorders based on the volatile organic compounds (VOCs) obtained from exhaled breath. After comparing VOC profiles obtained from 13 asthma, 5 COPD and 13 healthy control subjects, we found that VOC profiles distinguished asthma from healthy controls and also a subgroup of asthmatics taking the drug omalizumab from healthy controls. The VOC profiles could not distinguish between COPD and any of the other groups. Our results show a potential application of the GC/DMS for non-invasive and bedside diagnostics of asthma and asthma therapy monitoring. Future studies will focus on larger sample sizes and patient cohorts.

Identifying experimental surrogates for Bacillus anthracis spores: a review

Bacillus anthracis, the causative agent of anthrax, is a proven biological weapon. In order to study this threat, a number of experimental surrogates have been used over the past 70 years. However, not all surrogates are appropriate for B. anthracis, especially when investigating transport, fate and survival. Although B. atrophaeus has been widely used as a B. anthracis surrogate, the two species do not always behave identically in transport and survival models. Therefore, we devised a scheme to identify a more appropriate surrogate for B. anthracis. Our selection criteria included risk of use (pathogenicity), phylogenetic relationship, morphology and comparative survivability when challenged with biocides. Although our knowledge of certain parameters remains incomplete, especially with regards to comparisons of spore longevity under natural conditions, we found that B. thuringiensis provided the best overall fit as a non-pathogenic surrogate for B. anthracis. Thus, we suggest focusing on this surrogate in future experiments of spore fate and transport modelling.

Simultaneous detection of five biothreat agents in powder samples by a multiplexed suspension array

A suspension array-based multiplexed immunoassay was developed for rapid, sensitive, specific, and simultaneous detection of multiple biothreat-associated agents in powder samples. The 5-plexed immunoassays using sets of 9-plexed coupled fluorescent beads were employed to simultaneously detect five representative biothreat agents, including B. anthracis spore, Y. pestis, SARS-CoV, staphylococcal enterotoxin B (SEB) and ricin from a single powder sample and the feasibility for field samples was demonstrated by both blinded and standard laboratory trials. The detection sensitivity and dynamic range for the five biothreat agents from different powders might be varied depending on the nature of the powder and the feature of the contaminating agent. The limit of detection for Y. pestis, B. anthracis spores, SEB, ricin, SARS-CoV N protein in milk powder was 20 cfu, 111 cfu, 110pg, 5.4 ng and 2 ng per test respectively. Compared to conventional ELISA method, the suspension array has a higher sensitive ability, and can detect five biothreat agents simultaneously with high reproducibility.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Assessment of serum proteomics to detect large colon adenomas

A noninvasive blood test that could reliably detect early colorectal cancer or large adenomas would provide an important advance in colon cancer screening. The purpose of this study was to determine whether a serum proteomics assay could discriminate between persons with and without a large (> or =1 cm) colon adenoma. To avoid problems of "bias" that have affected many studies about molecular markers for diagnosis, specimens were obtained from a previously conducted study of colorectal cancer etiology in which bloods had been collected before the presence or absence of neoplasm had been determined by colonoscopy, helping to assure that biases related to differences in sample collection and handling would be avoided. Mass spectra of 65 unblinded serum samples were acquired using a nanoelectrospray ionization source on a QSTAR-XL mass spectrometer. Classification patterns were developed using the ProteomeQuest algorithm, performing measurements twice on each specimen, and then applied to a blinded validation set of 70 specimens. After removing 33 specimens that had discordant results, the "test group" comprised 37 specimens that had never been used in training. Although in the primary analysis, no discrimination was found, a single post hoc analysis, done after hemolyzed specimens had been removed, showed a sensitivity of 78%, a specificity of 53%, and an accuracy of 63% (95% confidence interval, 53-72%). The results of this study, although preliminary, suggest that further study of serum proteomics, in a larger number of appropriate specimens, could be useful. They also highlight the importance of understanding sources of "noise" and "bias" in studies of proteomics assays.

Machine Learning: A Crucial Tool for Sensor Design

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS)

Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography-differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument system, and we implemented a factorial design-of-experiments to systematically test several combinations of parameter settings while concurrently analyzing effects and interactions. We examined four parameters that affect sensitivity and detection for our instrument, requiring a 2(4) factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.

Recent Advances in Real-time Mass Spectrometry Detection of Bacteria

The analysis of bio-aerosols poses a technology challenge, particularly when sampling and analysis are done in situ. Mass spectrometry laboratory technology has been modified to achieve quick bacteria typing of aerosols in the field. Initially, aerosol material was collected and subjected off-line to minimum sample treatment and mass spectrometry analysis. More recently, sampling and analysis were combined in a single process for the real-time analysis of bio-aerosols in the field. This chapter discusses the development of technology for the mass spectrometry of bio-aerosols, with a focus on bacteria aerosols. Merits and drawbacks of the various technologies and their typing signatures are discussed. The chapter concludes with a brief view of future developments in bio-aerosol mass spectrometry.