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Review
. 2012 Jul;57(3):272-9.
doi: 10.1016/j.ymeth.2012.03.024. Epub 2012 Apr 4.

Single Cell Analysis Using Surface Enhanced Raman Scattering (SERS) Tags

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Review

Single Cell Analysis Using Surface Enhanced Raman Scattering (SERS) Tags

John P Nolan et al. Methods. .
Free PMC article

Abstract

Fluorescence is a mainstay of bioanalytical methods, offering sensitive and quantitative reporting, often in multiplexed or multiparameter assays. Perhaps the best example of the latter is flow cytometry, where instruments equipped with multiple lasers and detectors allow measurement of 15 or more different fluorophores simultaneously, but increases beyond this number are limited by the relatively broad emission spectra. Surface enhanced Raman scattering (SERS) from metal nanoparticles can produce signal intensities that rival fluorescence, but with narrower spectral features that allow a greater degree of multiplexing. We are developing nanoparticle SERS tags as well as Raman flow cytometers for multiparameter single cell analysis of suspension or adherent cells. SERS tags are based on plasmonically active nanoparticles (gold nanorods) whose plasmon resonance can be tuned to give optimal SERS signals at a desired excitation wavelength. Raman resonant compounds are adsorbed on the nanoparticles to confer a unique spectral fingerprint on each SERS tag, which are then encapsulated in a polymer coating for conjugation to antibodies or other targeting molecules. Raman flow cytometry employs a high resolution spectral flow cytometer capable of measuring the complete SERS spectra, as well as conventional flow cytometry measurements, from thousands of individual cells per minute. Automated spectral unmixing algorithms extract the contributions of each SERS tag from each cell to generate high content, multiparameter single cell population data. SERS-based cytometry is a powerful complement to conventional fluorescence-based cytometry. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector, allowing for higher levels of multiplexing and multiparameter analysis.

Figures

Fig. 1
Fig. 1
Schematic of nanoparticle-based SERS tags and their characteristic spectra. (A) SERS tags are typically comprised of a plasmonic nanoparticle, Raman tag, surface coating, and targeting entity. (B) The SERS tag excitation is determined largely by the plasmon resonance wavelength of the plasmonic nanoparticle, which can be tuned by altering the composition, size, and shape of the nanoparticle. (C) Gold nanorods 25 nm × 50 nm have a plasmon resonance in the red. (D) The Raman tag is adsorbed to the nanoparticle surface and gives each SERS tag its characteristic SERS spectrum.
Fig. 2
Fig. 2
Schematic of a Raman flow cytometer. Excitation is provided by a solid state laser (660 nm, 400 mW) and forward angle light scatter is collected on a photodiode. Ninety degree light scatter is collected from one side of the flow cell via an optical fiber and detected with a photomultiplier tube (PMT). SERS signals are collected from the opposite side of the flow cell into and optical fiber and delivered to an imaging spectrograph coupled to a CCD detector. Particles in the probe volume are detected by forward and side scatter, which trigger the acquisition of individual particle spectra by the CCD.
Fig. 3
Fig. 3
Schematic of the Raman flow cytometry data analysis workflow. Raman flow cytometry produces both conventional flow cytometry measurements such as forward and ninety degree light scatter (Sample.fcs), as well as complete SERS spectra from each particle (Sample.asc), which are bundled in a container file (Sample.fal). Data from beads labeled with a single tag (Ref_tagA.fal and Ref_tagB.fal) are analyzed to give reference spectra (Ref_tagA.txt and Ref_tagB.txt) that are used for spectral unmixing of the unknown sample spectra (Sample.txt). Spectral unmixing estimates the contribution of each tag to the unknown sample spectra for each particle. The amount of each tag on each particle in the sample (Sample.umx) is then saved as part of the Sample.fal file for analysis as a conventional flow cytometry parameter.
Fig. 4
Fig. 4
Generation of SERS flow cytometry reference spectra. To generate SERS reference spectra for use in spectral unmixing, light scatter gating (A) is used to identify single beads stained with SERS tag, allowing the spectra of individual beads to be inspected (B). The total integrated spectral emission can then be gated to remove outliers (C) and the average spectra determined (D).
Fig. 5
Fig. 5
SERS intensity calibration. Microspheres bearing different amounts of neutravidin were prepared and stained with saturating concentrations of biotin-phycoerythrin or biotin SERS tag and analyzed by conventional flow cytometry or SERS flow cytometry, respectively. (A) Fluorescence intensity histograms of beads bearing different amounts of neutravidin stained with b-PE. (B) SERS intensity histograms of SERS tag stained beads. (C) Calibration curves of fluorescence (open circles) and SERS (closed circles) median intensity as a function of bead binding capacity.
Fig. 6
Fig. 6
Cell surface immunostaining with antibody-coupled SERS tags. A cultured cell line (SupT1) was stained with (A) and anti-CD4 SERS tag, (B) and anti-CD8 SERS tag, or (C) both, and analyzed by Raman flow cytometry. Presented are light scatter histograms with single cell gating, individual and average spectra of gated cells, and single parameter histograms of the contributions of each SERS tags after spectral unmixing.
Fig. 7
Fig. 7
Particle encoding with multiple SERS tags. Avidin beads were stained with different combinations of four different SERS tags and analyzed by Raman flow cytometry. (A) Beads stained with NB SERS. (B) Beads stained with NB SERS and QSY SERS. (C) Beads stained with NB SERS, QSY SERS, and BHQ SERS. (D) Beads stained with NB SERS, QSY SERS, BHQ SERS, and MG SERS. Presented are the average SERS spectra and histograms of the population distributions of unmixed contributions of each tag.

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