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- LITERATURE CITED
Multiparameter measurements in flow cytometry are limited by the broad emission spectra of fluorescent labels. By contrast, Raman spectra are notable for their narrow spectral features. To increase the multiparameter analysis capabilities of flow cytometry, we investigated the possibility of measuring Raman signals in a flow cytometry-based system. We constructed a Raman Spectral Flow Cytometer, substituting a spectrograph and CCD detector for the traditional mirrors, optical filters, and photomultiplier tubes. Excitation at 633 nm was provided by a HeNe laser, and forward-angle light scatter is used to trigger acquisition of complete spectra from individual particles. Microspheres were labeled with nanoparticle surface enhanced Raman scattering (SERS) tags and measured using the RSFC. Fluorescence and Raman spectra from labeled microspheres were acquired using the Raman Spectral Flow Cytometer. SERS spectral intensities were dependent on integration time, laser power, and detector pixel binning. Spectra from particles labeled with one each of four different SERS tags could be distinguished by either a virtual bandpass approach using commercial flow cytometry data analysis software or by principal component analysis. Raman flow cytometry opens up new possibilities for highly multiparameter and multiplexed measurements of cells and other particles using a simple optical design and a single detector and light source. © 2008 International Society for Analytical Cytology
Flow cytometry is capable of multiparameter measurements of cells and other particles, and is a versatile platform for the study of cell and biomolecular function. Over the past decades, there have been steady incremental increases in the numbers of simultaneous measurements that can be performed with a flow cytometer. These increases have been achieved by increasing the number of light sources and detectors, and of fluorescent probes that can be attached to ligands such as antibodies. Today, instruments capable of simultaneously measuring as many as nine fluorescence signals are commercially available from a number of manufacturers, and specially configured instruments have been used to measure nearly twice that number (1). However, such instruments are complex to operate and further increases in the numbers of parameters are limited by the range of accessible spectral wavelengths with common light sources and detectors, and the spectral widths of the emissions of available fluorescence probes.
One optical signal that is potentially compatible with flow-based measurement but which has not yet been widely exploited is Raman scattering. Raman scattering originates from the interaction of light with chemical bonds in a sample to produce a characteristic spectrum of molecular vibrations. The spectral features of the Raman scattering are much narrower than fluorescence spectra, and contain a wealth of information about the chemical composition of the sample, which makes it a widely used technique in analytical chemistry. Surface-enhanced Raman scattering (SERS), is a special case where the interactions of a Raman-active compound with a metal surface results in orders-of-magnitude enhancements in scattering intensities (2, 3), giving this approach the potential for applications requiring sensitive detection.
Biological applications of SERS-based detection are growing, including those that employ functionalized SERS nanoparticles (4–11). We would like to exploit the features of SERS to expand the multiplexing capability of both cell- and particle-based applications of flow cytometry. Historically, optical signals in flow cytometry have been spectrally separated using interference filters and dichroic mirrors. This is suitable for the separation of a few broad emission spectra, but not for the many narrow features of Raman spectra. To increase spectral resolution, several groups have incorporated prisms or spectrographs to disperse the collected light in fluorescence flow cytometry applications (12–16). Here, we describe a Raman Spectral Flow Cytometer and its application to the detection and discrimination of several SERS-tags as a first step in implementing Raman-based detection in flow cytometry.
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- LITERATURE CITED
We have developed a flow cytometer capable of detecting discrete Raman vibrations, and used it to identify particles bearing different SERS tags. To our knowledge this is the first report demonstrating the measurement of Raman spectra from individual particles in a flowing sample stream and the first report of the use of SERS as a reporter modality in multiparameter flow cytometry.
A key difference between Raman signals and fluorescence signals is the feature density in the optical spectra. Although fluorophores tend to have broad emission spectra that are rather featureless, Raman spectra are characterized by sharp spectral features that correspond to specific classes of molecular vibrations. In many fluorescence measurement applications, including flow cytometry, the breadth of the emission spectra limit the number of different reporter tags that can be used for a particular spectral range. This necessitates the use of multiple light sources, each of which excites a few different tags that can be resolved within a spectral range of a couple of hundred nanometers of the optical spectrum. Recently, semi-conductor quantum dots have gained attention for flow applications (21) because of their broad absorption spectra and emission spectra that are slightly narrower than the emission spectra of small organic fluorophores. However, the spectra are only slightly narrower, and the increase in the number of labels that can be discriminated within a given spectral region is limited. The more information rich-Raman spectra have the potential to be exploited to enable a higher degree of multiplexing for a given spectral range. This is illustrated by comparison of the fluorescence spectra of the Nile Blue-labeled alignment particles shown in Figure 3, with the SERS spectra of the microsphere-bound nanoparticles in Figure 5, which span the same wavelength range.
Another difference between fluorescence emission and Raman scattering is the intensities. Intrinsic Raman scattering from molecules is orders of magnitude less intense than the fluorescence from fluorophores commonly used in biological applications. This necessitates the use of high laser powers and/or long signal integration times. Surface enhanced Raman scattering, or SERS, provides a partial solution to this problem, by offering orders of magnitude enhancement of Raman scattering to many Raman-active compounds in the presence of a metal surface such as gold or silver. The wavelength at which the SERS effect is maximal varies with the composition and dimension of the metal surface. An additive resonance effect arising from coupling of the laser-excited plasmon resonance of the metal with the absorbance maximum of the scattering compound can provide a further enhancement, in an effect termed surface-enhanced resonance Raman (or SERRS). Very frequently, these Raman-active compounds are chromophores, and single molecule detection has been reported under certain conditions.
The potentially very bright signals from surfaces exhibiting SERS or SERRS, including nanoparticles have been employed in a number of different biological detection applications, including immunoassays, nucleic acid detection, and cellular measurements. Their potential for use in flow cytometry, a platform noted for its highly multiparameter and multiplexed applications was one of the motivations for building a flow cytometer with Raman spectral measurement capabilities.
The key challenge to Raman flow cytometry is to resolve and measure the very narrow features in the Raman spectra. While it might be theoretically possible to achieve this using specially designed dichroic mirrors, bandpass filters, and discrete detectors such as PMTs, in a manner analogous to the spectral separation of and detection of multiple fluorescence signals in conventional flow cytometry, cost and light throughput issues make this impractical. A more practical approach is to use dispersive optics, such as a prism or a grating in combination with an array type detector, as has been reported for measuring fluorescence spectra in flow cytometry. We chose a spectrograph with holographic gratings and filters that provide very high light throughput and dispersion and efficient rejection of Rayleigh scattered light at the illumination wavelength. For detection, we chose a thermoelectrically cooled CCD with a readout speed compatible with flow cytometry data acquisition rates. The potential resolution of this combination of spectrograph and detector is greater than required for our use. We traded some of the excess spectrograph resolution for improved signal by operating without a slit, which allowed the maximum amount of light entering the spectrograph through the fiber to reach the detector and the diameter of the fiber to define the output resolution. The output of the spectrograph is dispersed over 1,600 columns of pixels of the detector, resulting in oversampling of the spectrograph output, so we took advantage of the detector's on-chip binning capabilities to bin columns to appropriately sample the coarse resolution of the imaged spectra and maximize signal to noise of the detector output. In most cases, pixels were binned 8× in the horizontal direction, resulting in 200 points for each spectra.
Another challenge of Raman spectral measurement by flow cytometry is acquiring enough signal from a particle while it flows through the probe volume. While microscopy-based approaches have the luxury of integrating signal for seconds or longer as needed to obtain the required signal to noise, flow-based measurements typically involve sample integration times of less than a millisecond, and sometimes much less. Here, by implementing an efficient light collection setup, a high throughput spectrograph, and a sensitive detector, we demonstrate the collection of resolvable Raman spectra with integration times down to 100 μs. Further, by using increased laser power and higher pixel binning, trading resolution for increased signal, we expect to be able to operate with integration times on the order of 10 μs, comparable with conventional commercial flow cytometers.
The ultimate goal of this work is to extend the advantages of Raman scattering discussed above to the analysis of single particles by flow cytometry. While there are a variety of commercial software tools available to the spectroscopist to analyze and interpret spectral data, these are not well suited for the multiparameter analysis of many samples of thousands of spectra each. On the other hand, flow cytometry data analysis software does not have dedicated spectral analysis capabilities. The existing FCS data standard will accommodate large numbers of parameters and several commercial flow cytometry data analysis programs do provide the ability to work with these parameters. To demonstrate the identification of particles bearing distinct SERS tags, we merged the output of the flow cytometry data acquisition (10 parameters) with that of the CCD detector (200 parameters) to make a new FCS data file containing 210 parameters. We used the parameter math capabilities of FCSExpress to calculate new parameters corresponding to the discrete spectral bands highlighted in Figure 7 and to subtract an appropriate background. Histograms displaying these derived parameters were used to set markers and define gates that defined each SERS tag in terms of being positive or negative for each of the spectral features examined as outlined in Table 1. These gates were used to determine the number and intensity for particles bearing each SERS tag. This demonstrates the ability of Raman flow cytometry to distinguish particles bearing one of a set of SERS tags, and for commercial flow cytometry software to analyze this data and report results.
Like fluorescence tags, SERS tags can be envisioned to be used in a number of different contexts in flow cytometry. The simplest use is the enumeration of particles that are positive for a given tag, and has been demonstrated here. A slightly more demanding application is determining how many of a given tag are on each particle. We estimate that there is an average of ∼7,800 SERS tags each on the particles shown in Figure 5, and that we could detect ∼100 on a particle. Converting these reasonable, but still rough, estimates into numbers that would support quantitative analysis in the same way fluorescence flow cytometry measurements do will require further work. It will be necessary to reproducibly make SERS tags of uniform intensity and to accurately measure their concentrations. Additionally, robust methods for the conjugation of the SERS tags to ligands such as antibodies, and for the characterization of those conjugates, must be developed. These are not trivial tasks, but given the wide interest in employing SERS tags on a variety of measurement platforms, should be achievable. Beyond the detection and quantification of a single SERS tag on a particle, many flow cytometry applications require particles to be scored positive or negative for multiple tags, and for the intensities of each of those tags to be determined. The simple “virtual bandpass” approach employed above will not likely meet these requirements, but there are a number of multivariate statistical approaches that should. An example presented in Figure 9 shows the ability of principle component analysis (PCA) to resolve the different tags used here. Spectra from 600 individual particles labeled with one each of the four SERS tags were acquired using the Raman Spectral Flow Cytometer, smoothed, normalized, and the first derivative spectra calculated. The processed spectra were then subjected to PCA, and the scores for the first three principle components are presented in Figure 9, illustrating the ability of this approach to distinguish between the four SERS tags. Coupled with classification and least squares fitting routines, these approaches can enable the identification of components in mixed signals and extraction of the relative intensity contributions to the measured spectra. These capabilities are not part of any commercial flow cytometry software that we know of, but there is no reason why they could not be.
In summary, we have developed a Raman Spectral Flow Cytometer that can measure Raman spectra from individual particles. We measured the spectra of particles labeled with each of several SERS nanoparticle tags, and have shown that these tags could be distinguished on the basis of their spectral features using commercially available flow cytometry data analysis software. The feature-rich spectra of the Raman signals offers the possibility of highly multiparameter measurements using a relatively simple optical detection approach.