The use of laser-based diagnostics for the rapid identification of blood borne viruses in human plasma samples.

AIMS: To demonstrate the use of a laser-based method of detection as a potential diagnostic test for the rapid identification of blood borne viruses in human plasma. METHODS AND RESULTS: In this study, using light emissions from laser sparks on plasma samples, the successful differentiation of both human immunodeficiency virus (HIV) and hepatitis C virus (HCV) in both residual de-identified plasma samples and plasma samples spiked to clinically relevant levels with each virus were demonstrated using plasma from more than 20 individuals spanning six different blood types (O+, O-, A+, A-, B+, B-). CONCLUSIONS: These experiments demonstrate that mathematical analysis of spectral data from laser sparks can provide accurate results within minutes. This capability was demonstrated using both spiked laboratory plasma samples and clinical plasma samples collected from infected and uninfected individuals. SIGNIFICANCE AND IMPACT OF THE STUDY: There is an ongoing need to rapidly detect viral infections and to screen for multiple viral infections. A laser-based approach can achieve sensitive, multiplex detection with minimal sample preparation and provide results within minutes. These properties along with the flexibility to add new agent detection by adjusting the detection programming make it a promising tool for clinical diagnosis. The potential for a laser-based approach has been previously demonstrated using pathogens spiked into human blood to clinically relevant levels. This study demonstrates this same ability to detect infections in clinical and laboratory spiked plasma samples. The ability to differentiate between plasma samples from infected and uninfected donors and determine the virus type using a laser-based diagnostic has not been previously demonstrated. Furthermore, this study is the first demonstration of the capability to differentiate viral infections in clinical plasma samples whereas previously published work used laboratory samples spiked with a virus or dealt with the detection of cancer in clinical plasma samples.

Proof of Principle for a Real-Time Pathogen Isolation Media Diagnostic: The Use of Laser-Induced Breakdown Spectroscopy to Discriminate Bacterial Pathogens and Antimicrobial-Resistant Staphylococcus aureus Strains Grown on Blood Agar.

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid, in situ, diagnostic technique in which light emissions from a laser plasma formed on the sample are used for analysis allowing automated analysis results to be available in seconds to minutes. This speed of analysis coupled with little or no sample preparation makes LIBS an attractive detection tool. In this study, it is demonstrated that LIBS can be utilized to discriminate both the bacterial species and strains of bacterial colonies grown on blood agar. A discrimination algorithm was created based on multivariate regression analysis of spectral data. The algorithm was deployed on a simulated LIBS instrument system to demonstrate discrimination capability using 6 species. Genetically altered Staphylococcus aureus strains grown on BA, including isogenic sets that differed only by the acquisition of mutations that increase fusidic acid or vancomycin resistance, were also discriminated. The algorithm successfully identified all thirteen cultures used in this study in a time period of 2 minutes. This work provides proof of principle for a LIBS instrumentation system that could be developed for the rapid discrimination of bacterial species and strains demonstrating relatively minor genomic alterations using data collected directly from pathogen isolation media.

Detection of biological contaminants on foods and food surfaces using laser-induced breakdown spectroscopy (LIBS).

The rapid detection of biological contaminants, such as Escherichia coli O157:H7 and Salmonella enterica , on foods and food-processing surfaces is important to ensure food safety and streamline the food-monitoring process. Laser-induced breakdown spectroscopy (LIBS) is an ideal candidate technology for this application because sample preparation is minimal and results are available rapidly (seconds to minutes). Here, multivariate regression analysis of LIBS data is used to differentiate the live bacterial pathogens E. coli O157:H7 and S. enterica on various foods (eggshell, milk, bologna, ground beef, chicken, and lettuce) and surfaces (metal drain strainer and cutting board). The type (E. coli or S. enterica) of bacteria could be differentiated in all cases studied along with the metabolic state (viable or heat killed). This study provides data showing the potential of LIBS for the rapid identification of biological contaminants using spectra collected directly from foods and surfaces.

Detection of pesticides and dioxins in tissue fats and rendering oils using laser-induced breakdown spectroscopy (LIBS).

In laser-induced breakdown spectroscopy (LIBS), a series of powerful laser pulses are directed at a surface to form microplasmas from which light is collected and spectrally analyzed to identify the surface material. In most cases, no sample preparation is needed, and results can be automated and made available within seconds to minutes. Advances in LIBS spectral data analysis using multivariate regression techniques have led to the ability to detect organic chemicals in complex matrices such as foods. Here, the use of LIBS to differentiate samples contaminated with aldrin, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, chlorpyrifos, and dieldrin in the complex matrices of tissue fats and rendering oils is described. The pesticide concentrations in the samples ranged from 0.005 to 0.1 µg/g. All samples were successfully differentiated from each other and from control samples. Sample concentrations could also be differentiated for all of the pesticides and the dioxin included in this study. The results presented here provide first proof-of-principle data for the ability to create LIBS-based instrumentation for the rapid analysis of pesticide and dioxin contamination in tissue fat and rendered oils.

Comparison of two partial least squares-discriminant analysis algorithms for identifying geological samples with the ChemCam laser-induced breakdown spectroscopy instrument.

ChemCam, a laser-induced breakdown spectroscopy (LIBS) instrument on the Mars Science Laboratory rover, will analyze the chemistry of the martian surface beginning in 2012. Prior to integration on the rover, the ChemCam instrument collected data on a variety of rock types to provide a training set for analysis of data from Mars. Models based on calibration data can be used to classify rocks via multivariate statistical techniques such as partial least squares-discriminant analysis (PLS-DA). In this study, we employ a version of PLS-DA in which modeling is applied in a defined classification flow to a variety of geological materials and compare the results with the traditional PLS-DA technique. Results show that the modified algorithm is more effective at classifying samples.

Use of laser-induced breakdown spectroscopy for the differentiation of pathogens and viruses on substrates.

In this work, the use of laser-induced breakdown spectroscopy (LIBS) to differentiate live pathogens and killed viruses on substrates is investigated. Live pathogens B. anthracis Sterne strain and F. tularensis live vaccine strain were interrogated as lawn and colonies on agar; dilutions on agar; and dilutions on glass slides, and it was found possible to differentiate among all samples. UV killed hantavirusstrains were studied as dilutions on slides and it was also found possible to differentiate among strains. To the best of our knowledge, this is the first study in which LIBS has been used to differentiate virus samples.

The use of laser-induced breakdown spectroscopy for distinguishing between bacterial pathogen species and strains.

Laser-induced breakdown spectroscopy (LIBS) was used in a blind study to successfully differentiate bacterial pathogens, both species and strain. The pathogens used for the study were chosen and prepared by one set of researchers. The LIBS data were collected and analyzed by another set of researchers. The latter researchers had no knowledge of the sample identities other than that (1) the first five of fifteen samples were unique (not replicates) and (2) the remaining ten samples consisted of two replicates of each of the first five samples. Using only chemometric analysis of the LIBS data, the ten replicate bacterial samples were successfully matched to each of the first five samples. The results of this blind study show it is possible to differentiate the bacterial pathogens Escherichia coli, three clonal methicillin-resistant Staphylococcus aureus (MRSA) strains, and one unrelated MRSA strain using LIBS. This is an important finding because it demonstrates that LIBS can be used to determine bacterial pathogen species within a defined sample set and can be used to differentiate between clonal relationships among strains of a single multiple-antibiotic-resistant bacterial species. Such a capability is important for the development of LIBS instruments for use in medical, water, and food safety applications.