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High-resolution spectral analysis of individual SERS-active nanoparticles in flow.

ABSTRACT: Nanoparticle spectroscopic tags based on surface enhanced Raman scattering (SERS) are playing an increasingly important role in bioassay and imaging applications. The ability to rapidly characterize large populations of such tags spectroscopically in a high-throughput flow-based platform will open new areas for their application and provide new tools for advancing their development. We demonstrate here a high-resolution spectral flow cytometer capable of acquiring Raman spectra of individual SERS-tags at flow rates of hundreds of particles per second, while maintaining the spectral resolution required to make full use of the detailed information encoded in the Raman signature for advanced multiplexing needs. The approach allows multiple optical parameters to be acquired simultaneously over thousands of individual nanoparticle tags. Characteristics such as tag size, brightness, and spectral uniformity are correlated on a per-particle basis. The tags evaluated here display highly uniform spectral signatures, but with greater variability in brightness. Subpopulations in the SERS response, not apparent in ensemble measurements, are also shown to exist. Relating tag variability to synthesis parameters makes flow-based spectral characterization a powerful tool for advancing particle development through its ability to provide rapid feedback on strategies aimed at constraining desired tag properties. Evidence for single-tag signal saturation at high excitation power densities is also shown, suggesting a role for high-throughput investigation of fundamental properties of the SERS tags as well.

SUBMITTER: Goddard G

PROVIDER: S-EPMC2861152 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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High-resolution spectral analysis of individual SERS-active nanoparticles in flow.

Goddard Gregory G Brown Leif O LO Habbersett Robb R Brady Christina I CI Martin John C JC Graves Steven W SW Freyer James P JP Doorn Stephen K SK

Journal of the American Chemical Society 20100501 17

Nanoparticle spectroscopic tags based on surface enhanced Raman scattering (SERS) are playing an increasingly important role in bioassay and imaging applications. The ability to rapidly characterize large populations of such tags spectroscopically in a high-throughput flow-based platform will open new areas for their application and provide new tools for advancing their development. We demonstrate here a high-resolution spectral flow cytometer capable of acquiring Raman spectra of individual SER ...[more]

PMID: 20143808

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Project description:The accurate identification of microorganisms belonging to vaginal microflora is crucial for establishing which microorganisms are responsible for microbial shifting from beneficial symbiotic to pathogenic bacteria and understanding pathogenesis leading to vaginosis and vaginal infections. In this study, we involved the surface-enhanced Raman spectroscopy (SERS) technique to compile the spectral signatures of the most significant microorganisms being part of the natural vaginal microbiota and some vaginal pathogens. Obtained data will supply our still developing spectral SERS database of microorganisms. The SERS results were assisted by Partial Least Squares Regression (PLSR), which visually discloses some dependencies between spectral images and hence their biochemical compositions of the outer structure. In our work, we focused on the most common and typical of the reproductive system microorganisms (Lactobacillus spp. and Bifidobacterium spp.) and vaginal pathogens: bacteria (e.g., Gardnerella vaginalis, Prevotella bivia, Atopobium vaginae), fungi (e.g., Candida albicans, Candida glabrata), and protozoa (Trichomonas vaginalis). The obtained results proved that each microorganism has its unique spectral fingerprint that differentiates it from the rest. Moreover, the discrimination was obtained at a high level of explained information by subsequent factors, e.g., in the inter-species distinction of Candida spp. the first three factors explain 98% of the variance in block Y with 95% of data within the X matrix, while in differentiation between Lactobacillus spp. and Bifidobacterium spp. (natural flora) and pathogen (e.g., Candida glabrata) the information is explained at the level of 45% of the Y matrix with 94% of original data. PLSR gave us insight into discriminating variables based on which the marker bands representing specific compounds in the outer structure of microorganisms were found: for Lactobacillus spp. 1400 cm−1, for fungi 905 and 1209 cm−1, and for protozoa 805, 890, 1062, 1185, 1300, 1555, and 1610 cm−1. Then, they can be used as significant marker bands in the analysis of clinical subjects, e.g., vaginal swabs.

Dataset Information

High-resolution spectral analysis of individual SERS-active nanoparticles in flow.

Publications

High-resolution spectral analysis of individual SERS-active nanoparticles in flow.

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