A time gating system has been constructed that is capable of recording high quality Raman spectra of highly fluorescing biological samples while operating below the photodamage threshold. Using a collinear gating geometry and careful attention to power conservation, we have achieved all-optical switching with a one picosecond gating time and 5% peak gating efficiency. The energy per pulse in this instrument is more than 3 orders of magnitude weaker than previous reports. Using this system we have performed proof-of-concept experiments on a sample composed of perylene dissolved in toluene, and the stem of a Jasminum multiflorum plant, the latter case being particularly important for the study of plants used in production of cellulosic biofuels. In both cases, a high SNR spectrum of the high-wavenumber region of the spectrum was recorded in the presence of an overwhelming fluorescence background.
©2010 Optical Society of America
Raman spectroscopy has been a focus of intense research as a tool for addressing biomedical problems for the past several years. For example, Raman spectra have been shown to provide exquisite diagnostic value for discriminating between cancerous and non-cancerous cells and tissues [1–5]. Raman spectroscopy has also been used to classify and quantify bacteria and bacterial cultures [6–8], to assess bone health , and as a reagentless assay for biofluid analysis . However, despite these encouraging results, Raman spectroscopy has not found widespread application beyond the confines of the research lab. One barrier to more widespread utility of Raman spectroscopy is that the weak Raman emission of most biological samples is dwarfed by a significant fluorescence background. Although several methods have been proposed to date to robustly eliminate the lineshape of the fluorescence background [11–14], none of these methods address the more fundamental problem of signal to noise that arises when trying to detect a small signal riding on top of a much larger background.
Because the signal to noise of Raman signals from tissue are often limited by the shot noise from the fluorescence, any scheme to eliminate the fluorescence background should necessarily block the fluorescence photons from ever reaching the detector. More than a decade ago, Matousek et al. demonstrated such a device that utilized an ultrafast Kerr shutter to pass promptly arriving Raman photons while blocking late-arriving fluorescence photons [15, 16]. While this device has demonstrated the ability to record fluorescence free Raman and Resonance-Raman spectra of non-living samples , its applicability to biological samples has been limited to analysis of tissues in vitro [18–20]. Applicability to living samples has been hampered by the use of high peak-power lasers operating at low repetition rates where nonthermal ablation of the tissue can result [21, 22]. Reducing the pulse power at the sample would result in prohibitively long acquisition times due to the low repetition rates of the reported systems.
In this paper, we present a time-gating system with a novel collinear design that utilizes low peak power pulses operating at high repetition rates. The resulting peak and average energies at the sample are biologically safe, and fluorescence-free Raman spectra can be acquired. A discussion of the system and its construction are followed by proof-of-concept results obtained on non-biological and biological samples.
2. Materials and Methods
2.1. Kerr-gated Raman system
The time-gating system is shown schematically in Fig. 1(a).
At the heart of this instrument is a collinear Kerr gate, shown in Fig. 1(b). Transform-limited 140 fs, 30 nJ pulses, centered at 808 nm, are emitted by a mode-locked Ti:Sapphire laser system operating at 80 MHz (Chameleon, Coherent Systems, Santa Clara, CA), giving an average intensity of 2.4 Watts. Overall power in the system is controlled by a half-wave plate placed before a Faraday isolator. The light is then passed through an ultra-narrowband filter (FWHM = 0.8 nm, Andover Corporation, Salem, NH), with the filtered light being frequency doubled by a β-barium borate (BBO) crystal (CASIX, Fuzhou, Fujian, P.R. China). The resulting 404 nm beam has a temporal width of approximately 1.65 ps and is then coupled into an Olympus IX-71 inverted microscope (Olympus, Center Valley, PA) fitted with a 40× 1.35 NA oil immersion objective. Approximately 62 pJ of energy per pulse reaches the sample (average intensity 5 mW). Independent control over the strength of the SHG beam is obtained by a half-wave plate placed after the optical isolator. By rotating the polarization of the light entering the BBO crystal with respect to the crystal axis, the efficiency of the SHG process can be adjusted. Raman scattered light is collected by the objective and separated from the excitation light by a dichroic filter (Semrock, Rochester, NY) and polarized by a Glan-Thompson polarizer (Thorlabs, Newton, NJ). Because of dispersion in the optical elements downstream of the sample, the Raman scattered light becomes slightly temporally broadened. Meanwhile, the portion of the 808 nm pulse that does not pass through the narrowband filter is sent through an adjustable delay line, a half-wave plate and Glan-Thompson polarizer (Thorlabs, Newton, NJ). The waveplate-polarizer combination is used to ensure maximum transmission through the 45° oriented polarizer, after which the pulse has a temporal width of 500 fs.
The 808 nm pump pulse and Raman probe pulse are recombined at a dichroic filter (Semrock, Rochester, NY), and the 404 nm Raman excitation light is filtered from the beam by a long-pass filter (Semrock, Rochester, NY) that passes both the Raman scattered light as well as the 808 nm pump beam. This is to prevent the residual 404 nm light from exciting any Raman scattering within the nonlinear medium. The pump and Raman beams are then focused by a 35 mm focal length achromatic doublet into a 1 cm pathlength quartz cuvette (Starna Cells, Atascadero, CA) containing the nonlinear medium. In these experiments, we use CS2 as our nonlinear medium. CS2 has a high nonlinear response (n 2 = 3.1 × 10−18 m2W), with two temporal components: a fast electronic response and a slower rotational (τ ~ 2 ps) response. As shown in Fig. 1(b), the Raman beam experiences nonlinear birefringence in the CS2 induced by the pump beam when the pulses are spatially and temporally overlapped. Temporal overlap is accomplished by careful tuning of the delay line by translating a stage with ≤ 25µm (85 fs) resolution. Spatial overlap is accomplished by passive piezo-electric tip/tilt controls on the pump-beam steering mirrors. Both the Raman and pump beams are focused to a spot size of approximately 5 µm in the CS2. The induced birefringence of the pump beam rotates the polarization of the Raman beam when the two are overlapped. A detailed description of the power requirements for optical switching is given below in Section 2.3. Because the Raman beam experiences dispersion, not all of the spectrum can be gated at one time by our pump pulse. As a proof-of-concept, we choose to focus on the high-wavenumber region of the spectrum where the signals are strongest. Additionally the high-wavenumber region may be important for research into plant-derived biofuels [23,24].
By contrast with earlier experiments, we operate our gate with no angle between the k-vectors of the Raman and pump beams. Although this presents a greater challenge in adequately attenuating the 808 nm pump before the collection system, it also allows us to take advantage of a greater interaction length between the Raman and pump light than earlier systems. Maximizing the overlap of the two beams through the CS2 is essential since our system has at least 3 orders of magnitude less energy per pulse available to operate the nonlinear gate compared with previous reports. Therefore we utilize a combination of absorption and interference filters, with a combined OD of 10 at 808 nm, to remove the 808 nm pump beam after the pulses have been recollimated and analyzed by a second Glan-Thompson polarizer (Thorlabs, Newton, NJ). The frequency- and temporally-filtered signal is then focused into an optical fiber and sent into a spectrometer (SP2300i, Princeton Instruments, Trenton, NJ) and dispersed onto a thermoelectrically cooled CCD (Pixis 100B, Trenton, NJ) operating at -70 degrees Celsius.
2.2. Data processing
For each spectrum shown, data were acquired over several short exposures that together comprise the total integration time. From each exposure, a dark-noise background has been subtracted. Because of the relatively long integration times discussed in this paper, removal of cosmic rays is essential. This is accomplished by comparing all exposures from a particular sample on a pixel-by-pixel basis, and replacing values that fall outside of three median deviations with the median value for that pixel across all exposures. Additionally, for spectra taken when the pump beam is on, a spectrum where only the pump beam was on has been subtracted to correct for the small background arising from the presence of the pump beam. Next, all exposures from a particular acquisition are averaged together. Finally, the spectra are smoothed with a 7-point, 3rd-order Savitsky-Golay filter, where the filter order and window size was chosen to be below the spectral resolution of our spectrograph to avoid broadening the Raman features. All processing was done using in-house scripts running in MATLAB (The MathWorks, Natick, MA).
2.3. Power requirements for optical switching
In order to determine the feasibility of using low-peak-power laser pulses for our instrument, we calculate the phase shift Δ that can be achieved when a laser beam with average power density I propagates through a nonlinear medium with a nonlinear index n 2 and length L by the following equation:
In order to achieve complete switching (i.e. for the polarization of the Raman light to be rotated by 90°), a phase shift of π must be achieved. Using Eq. (1), we can calculate the energy per pulse needed to achieve the required π phase shift within the nonlinear medium :
where d is the diameter of the laser beam within the medium and T is the duration of the pulse. From Eq. (2) it can be seen that for laser pulses with a certain pulse duration, the energy requirement can be lowered by reducing either the diameter of the laser beam, by increasing the interaction length, or by using a material with a very large n 2. However, due to diffraction, there are limitations to the minimum d and maximum L that can be achieved with a given lens. Since the nonlinear interaction is intensity dependent, we can approximate the interaction length to be the Rayleigh length, i.e. L = zR = πd 2/4λ. Thus, decreasing d increases the power density at the beam waist (thereby increasing the nonlinear response), but at the cost of reducing the interaction length. Therefore in Eq. (2), the quantity d 2/L is approximately a constant equalling 4λ/π. The required energy per pulse can then be written as follows,
For laser pulses with λ ≈ 800 nm, T ≈ 1 ps, and CS2 as the nonlinear medium (n 2 = 3.1 × 10−18 m2W), the required energy per pulse is 100 nJ, about 10 times larger than the energy per pulse we have available in our pump beam at the nonlinear medium to drive the gate. Therefore, we expect that our overall gate efficiency will be much reduced compared to earlier reports, but still large enough to observe a sizeable effect.
3. Results and Discussion
3.1. Efficiency and Temporal Width of the Low-Power Kerr Gate
For this experiment, the experimental setup was slightly modified. The bandpass filter before the BBO crystal was removed and the unconverted 808 nm light through the crystal was used as the pump beam. The SHG beam at 404 nm was reflected off of a mirror placed in the sample plane of the microscope and sent to the detection arm. This was done to ensure that measurements of gating efficiency were done with the briefest probe pulse possible given our experimental setup. The temporal width of both the pump and SHG pulses prior to entering the Kerr gate were approximately 500 fs. Fig. 2 shows a plot of the efficiency of the gating of the SHG signal by the pump pulse versus position of the temporal delay stage.
Efficiency in this case is defined by comparing the area under the 404 nm peak at various delays to the area when the gate is held open, i.e.:
By comparing the width of the efficiency curve, we can get an estimate of how long the gate is held open. As can be seen in Fig. 2, the maximum efficiency of the gate is 5.5%, while the gate width is approximately 700 fs, indicating that we are exciting only the electronic component of the CS2 [25, 26].
3.2. Perylene Dissolved in Toluene
For this and following experiments, the original version of the setup, discussed in Section 2.1 was used. The top panel of Fig. 3 shows the spectra taken from a dilute solution of perylene, a potent fluorophore with a lifetime of 5 ns , dissolved in toluene.
For these experiments, spectra were acquired with 20 minute acquisition times. The red curve shows the spectrum taken with the gate held open by rotating the analyzer to maximize through-put in the absence of a pump pulse. The black curve shows the spectrum taken through the Kerr gate (with the analyzer aligned to minimize throughput in the abscence of a pump pulse). The green curve is the spectrum with only the pump beam on, showing the level of background arising from the residual attenuated pump beam. Finally, the blue curve shows the spectrum taken through the Kerr gate in the absence of a pump pulse. Note that the upper curve has been scaled to 0.1% of its original value so that the curves can be plotted on the same y-axis. In order to compare the quality of Raman peaks obtainable from the two spectra, the ungated and gated spectra were background corrected with a 5th order polynomial determined using the method of Lieber and Mahadevan-Jansen . Those results are shown in the bottom panel of Fig. 3, with the open-gate spectrum scaled to 0.025% of its original value. The characteristic high-wavenumber peaks of toluene are clearly observable in the gated spectrum  (blue line), with the observed spectrum matching closely to a spectrum taken from a pure toluene sample (shown in black). These features are masked by noise and artifacts in the ungated spectrum, shown in red.
3.3. Stem of a Star Jasmine Plant
Study of lignin, cellulose, and related compounds in plant biology is an area of increasing interest due to the recent emergence of cellulosic biomass as an alternative fuel source [29, 30]. Cellulosic materials residing within plant cell walls can be converted to biofuels only after extraction from a dense network of pectin and lignin polymers. Efforts to modify this network to permit efficient extraction of biomass is an area of highly active research , however efforts to study relevant plant species directly with high-resolution Raman microscopy have been hampered by overwhelming autofluorescence, requiring either prohibitive integration times or use of nonfluorescing senescent plant samples .
Here we present the Raman spectroscopic study of a green plant stem in the presence of strong autofluorescence arising from lignin, chlorophyll, and other molecules within the plant. The top panel of Fig. 4 shows spectra taken from the stem of a star jasmine (Jasminum multiflorum) plant, showing the characteristic plant autofluorescence .
As before, the spectra were integrated over a 20 minute acquisition time. The red curve is the spectrum with the gate held open, the black curve is the gated spectrum, the green curve is the background due to the pump beam, and the blue curve is the spectrum with the gate held closed. Once again, the curve with the gate held open has been scaled to 0.1% of its original value. Comparing the gated and ungated spectra after background subtraction (shown in the bottom panel of Fig. 4), we can see the characteristic high-wavenumber peak of cellulose  clearly in the gated curve (blue), while it is entirely lost within the noise of the ungated spectrum (red).
Assuming that the peak shown in the lower panel of Fig. 4 represents purely Raman-scattered photons, we can compute the number of Raman photons per second emitted by the sample, compared with the number of fluorescence photons emitted (calculated from the ungated curve in the upper panel of Fig. 4). This can give us an estimate of the SNR of the cellulose peak versus integration time in an ungated experiment assuming Poisson noise, shown in Fig. 5.
Computing the shot-noise associated with our gated cellulose peak, we arrive at a SNR in the gated experiment of 118. In order to get the same signal-to-noise in an ungated experiment, we would have to integrate for 540 minutes, 27 times longer than in the gated experiment. It should be noted that this analysis takes into account the reduced efficiency of Raman collection through the Kerr gate, meaning that despite having only a few percent transmission through our gate the SNR improvement due to fluorescence rejection is still significant.
We have shown that our low-power time-gated system is capable of meaningfully improving the SNR of Raman spectra taken from highly fluorescing biological samples using pulse energies below the damage threshold. Our gating system operates with a timescale of approximately 1 picosecond and at 5% efficiency. Using this we have extracted Raman signals of toluene and cellulose in the presence of overwhelming fluorescence from perylene and plant autofluorophores, respectively, with the latter spectrum having applications in biofuel development. Additionally, we have used high-repetition laser pulse energies 3 orders of magnitude weaker than discussed in previous reports, avoiding both thermal and nonthermal damage thresholds  while maintaining reasonable integration times. Although we are at present limited to a low gating efficiency, our system can still reduce integration times by a factor of 27 in the case of plant autofluorescence. Additionally, novel nonlinear materials and gating designs have the potential to improve this efficiency and these are directions our group is actively pursuing.
This work was funded by NSF award DBI 0852891. Part of this work was also funded by the Center for Biophotonics Science and Technology, a designated NSF Science and Technology Center managed by the University of California, Davis, under Cooperative Agreement No. PHY0120999.
References and links
1. M. Gniadecka, P. A. Philipsen, S. Sigurdsson, S. Wessel, O. F. Nielsen, D. H. Christensen, J. Hercogova, K. Rossen, H. K. Thomsen, R. Gniadecki, L. K. Hansen, and H. C. Wulf, “Melanoma diagnosis by Raman spectroscopy and neural networks: Structure alterations in proteins and lipids in intact cancer tissue,” J. Invest. Dermatol. 122, 443–449 (2004). [CrossRef] [PubMed]
2. A. Nijssen, T. C. B. Schut, F. Heule, P. J. Caspers, D. P. Hayes, M. H. A. Neumann, and G. J. Puppels, “Discriminating basal cell carinoma from its surrounding tissue by Raman spectroscopy,” J. Invest. Dermatol. 119, 64–69 (2002). [CrossRef] [PubMed]
3. C. A. Lieber, S. K. Majumder, D. Billheimer, D. L. Ellis, and A. Mahadevan-Jansen, “Raman microspectroscopy for skin cancer detection in vitro,” J. Biomed. Opt. 13, 024013 (2008). [CrossRef] [PubMed]
4. K. Chen, Y. Qin, F. Zheng, M. Sun, and D. Shi, “Diagnosis of colorectal cancer using Raman spectroscopy of laser-trapped single living epithelial cells,” Opt. Lett. 31, 2015–2017 (2006). [CrossRef] [PubMed]
5. J. W. Chan, D. S. Taylor, S. M. Lane, T. Zwerdling, J. Tuscano, and T. Huser, “Nondestructive identification of individual leukemia cells by laser trapping Raman spectroscopy,” Anal. Chem. 80, 2180–2187 (2008). [CrossRef] [PubMed]
6. Q. Y. Zhu, R. G. Quivey, and A. J. Berger, “Measurement of bacterial concentration fractions in polymicrobial mixtures by Raman microspectroscopy,” J. Biomed. Opt. 9, 1182–1186 (2004). [CrossRef] [PubMed]
7. P. Rösch, M. Harz, M. Schmitt, K.-D. Peschke, O. Ronneberger, H. Burkhardt, H.-W. Motzkus, M. Lankers, S. Hofer, H. Thiele, and J. Pöpp, “Chemotaxonomic identification of single bacteria by micro-Raman spectroscopy: Application to clean-room-relevant biological contaminations,” Appl. Environ. Microb. 71, 1626–1637 (2005). [CrossRef]
8. T. J. Moritz, D. S. Taylor, C. R. Polage, D. Krol, S. M. Lane, and J. W. Chan, “Raman spectroscopic signatures of the metabolic states of escherichia coli cells and their dependence on antibiotics treatment,” Biophys. J. 98, 742a (2010). [CrossRef]
9. K. A. Dehring, N. J. Crane, A. R. Smukler, J. B. McHugh, B. J. Roessler, and M. D. Morris, “Identifying chemical changes in subchondral bone taken from murine knee joints using Raman spectroscopy,” Appl. Spectrosc. 60, 1134–1141 (2006). [CrossRef] [PubMed]
10. D. Qi and A. J. Berger, “Chemical concentration measurement in blood serum and urine samples using liquidcore optical fiber Raman spectroscopy,” Appl. Spectrosc. 46, 1726–1734 (2007).
11. A. P. Shreve, N. J. Cherepy, and R. A. Mathies, “Effective rejection of fluorescence interference in raman spectroscopy using a shifted excitation difference technique,” Appl. Spectrosc. 46, 707–711 (1992). [CrossRef]
14. A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010). [CrossRef]
15. P. Matousek, M. Towrie, A. Stanley, and A. W. Parker, “Efficient rejection of fluorescence from Raman spectra using picosecond Kerr gating,” Appl. Spectrosc. 53, 1485–1489 (1999). [CrossRef]
16. P. Matousek, M. Towrie, C. Ma, W. M. Kwok, D. Phillips, W. T. Toner, and A. W. Parker, “Fluorescence suppression in resonance Raman spectroscopy using a high-performance picosecond Kerr gate,” J. Raman Spectrosc. 32, 983–988 (2001). [CrossRef]
17. J. Dyer, W. J. Blau, C. G. Coates, C. M. Creely, J. D. Gavey, M. W. George, D. C. Grills, S. Hudson, J. M. Kelly, P. Matousek, J. J. McGarvey, J. McMaster, A. W. Parker, M. Towrie, and J. A. Weinstein, “The photophysics of fac-[Re(CO)3(dppz)(py)]+ in CH3CN: a comparative picosecond flash photolysis, transient infrared, transient resonance Raman and density functional theoretical study,” Photoch. Photobio. Sci. 2, 542–554 (2003). [CrossRef]
18. M. D. Morris, P. Matousek, M. Towrie, A. W. Parker, A. E. Goodship, and E. R. C. Draper, “Kerr-gated time-resolved Raman spectroscopy of equine cortical bone tissue,” J. Biomed. Opt. 10, 014014 (2005). [CrossRef]
19. R. Baker, P. Matousek, K. L. Ronayne, A. W. Parker, K. Rogers, and N. Stone, “Depth profiling of calcifications in breast tissue using picosecond Kerr-gated Raman spectroscopy,” The Analyst 132, 48–53 (2007). [CrossRef]
20. V. V. Yakovlev, “Time-gated confocal Raman microscopy,” Spectroscopy 22, 41–45 (2007).
22. F. H. Loesel, J. P. Fischer, M. H. Götz, C. Horvath, T. Juhasz, F. Noack, N. Suhm, and J. F. Bille, “Non-thermal ablation of neural tissue with femtosecond laser pulses,” Appl. Phys. B: Lasers O. 66, 121–128 (1998). [CrossRef]
24. M. Schmidt, A. M. Schwartzberg, A. Carroll, A. Chaibang, P. D. Adams, and P. J. Schuck, “Raman imaging of cell wall polymers in Aradopsis thaliana,” Biochem. and Bioph. Res. Co. 395, 521–523 (2010). [CrossRef]
25. R. L. Sutherland, D. G. McLean, and S. Kirkpatrick, Handbook of Nonlinear Optics (Marcel Dekker, New York, NY, 2003). [CrossRef]
26. R. A. Ganeev, A. I. Ryasnyanskii, and H. Kuroda, “Nonlinear optical characteristics of carbon disulfide,” Opt. Spectrosc. 100, 108–118 (2006). [CrossRef]
27. A. G. Vitukhnovsky, M. I. Sluch, J. G. Warren, and M. C. Petty, “The fluorescence of perylene-doped langmuir—blodgett films,” Chem. Phys. Lett. 173, 425–429 (1990). [CrossRef]
28. J. K. Wilmshurst and H. J. Bernstein, “The infrared and Raman spectra of toluene, toleuene-α-d3, m-xylene, and m-xylene-αα’-d16,” Can. J. Chemistry 35, 911–925 (1957). [CrossRef]
30. Z. Li, L.-Q. Chu, J. V. Sweedler, and P. W. Bohn, “Spatial correlation of confocal Raman scattering and secondary ion mass spectrometric molecular images of lignocellulosic materials,” Anal. Chem. 82, 2608–2611 (2010). [CrossRef] [PubMed]
32. A. Castellan and R. S. Davidson, “Steady-state and dynamic fluorescence emission from abies wood,” J Photoch. Photobio. A 78, 275–279 (1994). [CrossRef]
33. U. P. Agarwal and S. A. Ralph, “FT-Raman spectroscopy of wood: identifyin contributions of lignin and carbohydrate polymers in the spectrum of black spruce (Picea mariana),” Appl. Spectrosc. 51, 1648–1655 (1997). [CrossRef]