Abiotic stressors impact outer membrane vesicle composition in a beneficial rhizobacterium: Raman spectroscopy characterization.

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have roles in cell-to-cell signaling, biofilm formation, and stress responses. Here, the effects of abiotic stressors on OMV contents and composition from biofilm cells of the plant health-promoting bacterium Pseudomonas chlororaphis O6 (PcO6) are examined. Two stressors relevant to this root-colonizing bacterium were examined: CuO nanoparticles (NPs)-a potential fertilizer and fungicide- and H2O2-released from roots during plant stress responses. Atomic force microscopy revealed 40-300 nm diameter OMVs from control and stressed biofilm cells. Raman spectroscopy with linear discriminant analysis (LDA) was used to identify changes in chemical profiles of PcO6 cells and resultant OMVs according to the cellular stressor with 84.7% and 83.3% accuracies, respectively. All OMVs had higher relative concentrations of proteins, lipids, and nucleic acids than PcO6 cells. The nucleic acid concentration in OMVs exhibited a cellular stressor-dependent increase: CuO NP-induced OMVs > H2O2-induced OMVs > control OMVs. Biochemical assays confirmed the presence of lipopolysaccharides, nucleic acids, and protein in OMVs; however, these assays did not discriminate OMV composition according to the cellular stressor. These results demonstrate the sensitivity of Raman spectroscopy using LDA to characterize and distinguish cellular stress effects on OMVs composition and contents.

Effect of growth media and phase on Raman spectra and discrimination of mycobacteria.

When developing a Raman spectral library to identify bacteria, differences between laboratory and real world conditions must be considered. For example, culturing bacteria in laboratory settings is performed under conditions for ideal bacteria growth. In contrast, culture conditions in the human body may differ and may not support optimized bacterial growth. To address these differences, researchers have studied the effect of conditions such as growth media and phase on Raman spectra. However, the majority of these studies focused on Gram-positive or Gram-negative bacteria. This article focuses on the influence of growth media and phase on Raman spectra and discrimination of mycobacteria, an acid-fast genus. Results showed that spectral differences from growth phase and media can be distinguished by spectral observation and multivariate analysis. Results were comparable to those found for other types of bacteria, such as Gram-positive and Gram-negative. In addition, the influence of growth phase and media had a significant impact on machine learning models and their resulting classification accuracy. This study highlights the need for machine learning models and their associated spectral libraries to account for various growth parameters and stages to further the transition of Raman spectral analysis of bacteria from laboratory to clinical settings.

Simultaneous isolation and label-free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy.

The traditional bacterial identification method of growing colonies on agar plates can take several days to weeks to complete depending on the growth rate of the bacteria. Successfully decreasing this analysis time requires cell isolation followed by identification. One way to decrease analysis time is by combining dielectrophoresis (DEP), a common technique used for cell sorting and isolation, and Raman spectroscopy for cell identification. DEP-Raman devices have been used for bacterial analysis, however, these devices have a number of drawbacks including sample heating, cell-to-electrode proximity that limits throughput and separation efficiency, electrode fouling, or inability to address sample debris. Presented here is a contactless DEP-Raman device to simultaneously isolate and identify particles from a mixed sample while avoiding common drawbacks associated with other DEP designs. Using the device, a mixed sample of bacteria and 3 µm polystyrene spheres were isolated from each other and a Raman spectrum of the trapped bacteria was acquired, indicating the potential for cDEP-Raman devices to decrease the analysis time of bacteria.

Alternative cDEP Design to Facilitate Cell Isolation for Identification by Raman Spectroscopy.

Dielectrophoresis (DEP) uses non-uniform electric fields to cause motion in particles due to the particles' intrinsic properties. As such, DEP is a well-suited label-free means for cell sorting. Of the various methods of implementing DEP, contactless dielectrophoresis (cDEP) is advantageous as it avoids common problems associated with DEP, such as electrode fouling and electrolysis. Unfortunately, cDEP devices can be difficult to fabricate, replicate, and reuse. In addition, the operating parameters are limited by the dielectric breakdown of polydimethylsiloxane (PDMS). This study presents an alternative way to fabricate a cDEP device allowing for higher operating voltages, improved replication, and the opportunity for analysis using Raman spectroscopy. In this device, channels were formed in fused silica rather than PDMS. The device successfully trapped 3.3 µm polystyrene spheres for analysis by Raman spectroscopy. The successful implementation indicates the potential to use cDEP to isolate and identify biological samples on a single device.

Effect of Principal Component Analysis Centering and Scaling on Classification of Mycobacteria from Raman Spectra.

Raman spectroscopy has been used for decades to detect and identify biological substances as it provides specific molecular information. Spectra collected from biological samples are often complex, requiring the aid of data truncation techniques such as principal component analysis (PCA) and multivariate classification methods. Classification results depend on the proper selection of principal components (PCs) and how PCA is performed (scaling and/or centering). There are also guidelines for choosing the optimal number of PCs such as a scree plot, Kaiser criterion, or cumulative percent variance. The goal of this research is to evaluate these methods for best implementation of PCA and PC selection to classify Raman spectra of bacteria. Raman spectra of three different isolates of mycobacteria ( Mycobacterium sp. JLS, Mycobacterium sp. KMS, Mycobacterium sp. MCS) were collected and then passed through PCA and linear discriminant analysis for classification. Principal component analysis implementation as well as PC selection was evaluated by comparing the highest possible classification accuracies against accuracies determined by PC selection methods for each centering and scaling option. Centered and unscaled data provided the best results when selecting PCs based on cumulative percent variance.

Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform.

Immunoassays are used to detect proteins based on the presence of associated antibodies. Because of their extensive use in research and clinical settings, a large infrastructure of immunoassay instruments and materials can be found. For example, 96- and 384-well polystyrene plates are available commercially and have a standard design to accommodate ultraviolet-visible (UV-Vis) spectroscopy machines from various manufacturers. In addition, a wide variety of immunoglobulins, detection tags, and blocking agents for customized immunoassay designs such as enzyme-linked immunosorbent assays (ELISA) are available. Despite the existing infrastructure, standard ELISA kits do not meet all research needs, requiring individualized immunoassay development, which can be expensive and time-consuming. For example, ELISA kits have low multiplexing (detection of more than one analyte at a time) capabilities as they usually depend on fluorescence or colorimetric methods for detection. Colorimetric and fluorescent-based analyses have limited multiplexing capabilities due to broad spectral peaks. In contrast, Raman spectroscopy-based methods have a much greater capability for multiplexing due to narrow emission peaks. Another advantage of Raman spectroscopy is that Raman reporters experience significantly less photobleaching than fluorescent tags1. Despite the advantages that Raman reporters have over fluorescent and colorimetric tags, protocols to fabricate Raman-based immunoassays are limited. The purpose of this paper is to provide a protocol to prepare functionalized probes to use in conjunction with polystyrene plates for direct detection of analytes by UV-Vis analysis and Raman spectroscopy. This protocol will allow researchers to take a do-it-yourself approach for future multi-analyte detection while capitalizing on pre-established infrastructure.

Rational design of Raman-labeled nanoparticles for a dual-modality, light scattering immunoassay on a polystyrene substrate.

BACKGROUND: Surface-enhanced Raman scattering (SERS) is a powerful light scattering technique that can be used for sensitive immunoassay development and cell labeling. A major obstacle to using SERS is the complexity of fabricating SERS probes since they require nanoscale characterization and optical uniformity. The light scattering response of SERS probes may also be modulated by the substrate used for SERS analysis. A typical SERS substrate such as quartz can be expensive. Polystyrene is a cheaper substrate option but can decrease the SERS response due to interfering Raman emission peaks and high background fluorescence. The goal of this research is to develop an optimized process for fabricating Raman-labeled nanoparticles for a SERS-based immunoassay on a polystyrene substrate. RESULTS: We have developed a method for fabricating SERS nanoparticle probes for use in a light scattering immunoassay on a polystyrene substrate. The light scattering profile of both spherical gold nanoparticle and gold nanorod SERS probes were characterized using Raman spectroscopy and optical absorbance spectroscopy. The effects of substrate interference and autofluorescence were reduced by selecting a Raman reporter with a strong light scattering response in a spectral region where interfering substrate emission peaks are minimized. Both spherical gold nanoparticles and gold nanorods SERS probes used in the immunoassay were detected at labeling concentrations in the low pM range. This analytical sensitivity falls within the typical dynamic range for direct labeling of cell-surface biomarkers using SERS probes. CONCLUSION: SERS nanoparticle probes were fabricated to produce a strong light scattering signal despite substrate interference. The optical extinction and inelastic light scattering of these probes was detected by optical absorbance spectroscopy and Raman spectroscopy, respectively. This immunoassay demonstrates the feasibility of analyzing strongly enhanced Raman signals on polystyrene, which is an inexpensive yet non-ideal Raman substrate. The assay sensitivity, which is in the low pM range, suggests that these SERS probe particles could be used for Raman labeling of cell or tissue samples in a polystyrene tissue culture plate. With continued development, this approach could be used for direct labeling of multiple cell surface biomarkers on strongly interfering substrate platforms.

Nanoparticle properties and synthesis effects on surface-enhanced Raman scattering enhancement factor: an introduction.

Raman spectroscopy has enabled researchers to map the specific chemical makeup of surfaces, solutions, and even cells. However, the inherent insensitivity of the technique makes it difficult to use and statistically complicated. When Raman active molecules are near gold or silver nanoparticles, the Raman intensity is significantly amplified. This phenomenon is referred to as surface-enhanced Raman spectroscopy (SERS). The extent of SERS enhancement is due to a variety of factors such as nanoparticle size, shape, material, and configuration. The choice of Raman reporters and protective coatings will also influence SERS enhancement. This review provides an introduction to how these factors influence signal enhancement and how to optimize them during synthesis of SERS nanoparticles.

Automated interpretation of LIBS spectra using a fuzzy logic inference engine.

Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.