Raman Spectroscopy-Based Metabolomics for ... - ACS Publications

Daniel P. Cherney,†,‡ Drew R. Ekman,† David J. Dix,§ and Timothy W. Collette*,†. U.S. Environmental Protection Agency, Office of Research and...
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Anal. Chem. 2007, 79, 7324-7332

Raman Spectroscopy-Based Metabolomics for Differentiating Exposures to Triazole Fungicides Using Rat Urine Daniel P. Cherney,†,‡ Drew R. Ekman,† David J. Dix,§ and Timothy W. Collette*,†

U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Athens, Georgia 30605, and U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711

Normal Raman spectroscopy was evaluated as a metabolomic tool for assessing the impacts of exposure to environmental contaminants, using rat urine collected during the course of a toxicological study. Specifically, one of three triazole fungicides, myclobutanil, propiconazole, or triadimefon, was administered daily via oral gavage to male Sprague-Dawley rats at doses of 300, 300, or 175 mg/kg, respectively. Urine was collected from all three treatment groups and also from vehicle control rats on day six, following five consecutive days of exposure. Spectra were acquired with a CCD-based dispersive Raman spectrometer, using 785-nm diode laser excitation. To optimize the signal-to-noise ratio, urine samples were filtered through a stirred ultrafiltration cell with a 500 nominal molecular weight limit filter to remove large, unwanted urine components that can degrade the spectrum via fluorescence. However, a subsequent investigation suggested that suitable spectra can be obtained in a high-throughput fashion, with little or no Raman-specific sample preparation. For the sake of comparison, a parallel 1H NMR-based metabolomic analysis was also conducted on the unfiltered samples. Results from multivariate data analysis demonstrated that the Raman method compares favorably with NMR in regard to the ability to differentiate responses from these three contaminants. Metabolomics involves the use of advanced analytical instrumentation and multivariate data analysis tools to profile changes in levels of endogenous metabolites in a test organism’s tissues and biofluids. The application of metabolomics to study the impacts of exposure to toxic chemicals is particularly useful, and hence, this is now a rapidly expanding field.1,2 Commonly, rodents are used for these studies, and urine is a convenient and informative biofluid to monitor.3,4 * To whom correspondence should be addressed. E-mail: [email protected]. Phone: 706-355-8211. Fax: 706-355-8202. † National Exposure Research Laboratory. ‡ Current address: Baytown Technology & Engineering Complex, ExxonMobil Chemical Co., Baytown, TX 77520. § National Center for Computational Toxicology. (1) Keun, H. C. Pharmacol. Ther. 2006, 109, 92-106. (2) Lin, C. Y.; Viant, M. R.; Tjeerdema, R. S. J. Pestic. Sc. 2006, 31, 245-251. (3) Connor, S. C.; Wu, W.; Sweatman, B. C.; Manini, J.; Haselden, J. N.; Crowther, D. J.; Waterfield, C. J. Biomarkers 2004, 9, 156-179.

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Historically, NMR spectroscopy has been the most commonly used analytical technique in metabolomics when applied to toxicology, and its advantages for this application are undeniable.5,6 However, a number of other analytical techniques are now being used, or have been evaluated for use, in metabolomic studies of toxic chemical exposures. Most notably, hyphenated mass spectrometric techniques (e.g., LC-MS, GC/MS, etc.) are proving their value for this application, in large part because of the excellent sensitivity and selectivity that is afforded.7 However, there is considerable interest in the community in evaluating the potential for a still wider range of analytical tools that may offer various advantages or that may confirm or complement the information from NMR and MS.7,8 Here we report our investigation of normal Raman spectroscopy with near-infrared laser excitation as a viable metabolomic tool for investigating the impact of exposure of rats to chemical contaminants, using urine. Raman is an information-rich spectroscopic technique that is not hindered significantly by the presence of water. Hence, it offers an ease-of-use advantage over most analytical techniques for the analysis of aqueous-based samples. Like NMR, the Raman approach provides a rapid “snapshot” of the metabolite profile since no separation step is involved. But, since the techniques are based on different molecular properties, it may be possible to obtain information from Raman spectroscopy that is complementary or confirmatory to that of NMR. Also, it is important to note that instrumentation for normal Raman spectroscopy is now small and portable, costing only a fraction as much as an NMR spectrometer. In addition, modern Raman instruments can be operated efficiently and inexpensively by nonexperts. The potential to profile metabolites in tissues and body fluids with Raman spectroscopy has long been recognized9,10 but not yet fully exploited for metabolomic studies. For example, there (4) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Discovery 2002, 1, 153-161. (5) Lindon, J. C.; Nicholson, J. K.; Everett, J. R. Annu. Rep. NMR Spectrosc.1999, 38, 1-88. (6) Miller, M. G. J. Proteome Res. 2007, 6, 540-545. (7) Dunn, W. B.; Bailey, N. J. C.; Johnson, H. E. Analyst 2005, 130, 606-625. (8) Harrigan, G. G.; LaPlante, R. H.; Cosma, G. N.; Cockerell, G.; Goodacre, R.; Maddox, J. F.; Luyendyk, J. P.; Ganey, P. E.; Roth, R. A. Toxicol. Lett. 2004, 146, 197-205. (9) Clarke, S.; Goodacre, R. In Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analsys; Harrigan, G. G., Goodacre, R., Eds.; Kluwer: Boston, 2003. (10) Ellis, D. I.; Goodacre, R. Analyst 2006, 131, 875-885. 10.1021/ac070856n CCC: $37.00

© 2007 American Chemical Society Published on Web 08/25/2007

Figure 1. Structures of the three triazole fungicides used to evaluate Raman-based metabolomics.

are reports of various Raman techniques being used to observe components in brain tissue,11 blood serum,12 and urine.13-18 However, reported Raman studies with urine have typically involved targeted analysis of only a few metabolites, and these are frequently added artificially to illustrate the potential for quantitative analysis. Instead, we report here the demanding application of comprehensive Raman-based metabolomics, using rat urine samples directly from a laboratory-controlled chemical toxicological exposure. Note that FT-IR, which is a more-common and complementary form of vibrational spectroscopy, has already emerged in recent years as a viable metabolomic technique.19 For example, an FT-IR-based metabolomic application to a toxicological study using rat urine has recently been published.8 Many of the same benefits we discuss here have motivated the development of FT-IR-based metabolomics (e.g., low cost, simplicity, etc.). While FT-IR and Raman spectroscopy share some common benefits, they also have their own unique advantages and limitations. For example, in comparison to Raman, the much more intense water band is a serious drawback for FT-IR. To test the feasibility of Raman-based metabolomics for application to toxicological studies using urine, samples were acquired from rat exposures to three agricultural triazole fungicidessmyclobutanil, triadimefon, and propiconazole. (See Figure 1.). Triazole fungicides are widely used agriculturally for the protection of crops and pharmaceutically in the treatment of topical and systemic infections. Heavy usage has created concern over the impact these compounds may have through environmental exposure to humans and other organisms.20,21 Moreover, these three specific compounds were chosen as a proof-of-concept (11) Hanlon, E. B.; Manoharan, R.; Koo, T. W.; Shafer, K. E.; Motz, J. T.; Fitzmaurice, M.; Kramer, J. R.; Itzkan, I.; Dasari, R. R.; Feld, M. S. Phys. Med. Biol. 2000, 45, R1-R59. (12) Berger, A. J.; Koo, T. W.; Itzkan, I.; Horowitz, G.; Feld, M. S. Appl. Opt. 1999, 38, 2916-2926. (13) Qi, D. H.; Berger, A. J. J. Biomed. Opt. 2005, 10. (14) Dou, X.; Yamaguchi, Y.; Yamamoto, H.; Doi, S.; Ozaki, Y. Vib. Spectrosc. 1996, 13, 83-89. (15) Dou, X. M.; Yamaguchi, Y.; Yamamoto, H.; Doi, S.; Ozaki, Y. Biospectroscopy 1997, 3, 113-120. (16) Premasiri, W. R.; Clarke, R. H.; Womble, M. E. Lasers Surg. 2001, 28, 330334. (17) McMurdy, J. W.; Berger, A. J. Appl. Spectrosc. 2003, 57, 522-525. (18) Wang, T. L.; Chiang, H. K.; Lu, H. H.; Peng, F. Y. Opt. Quantum Electron. 2005, 37, 1415-1422. (19) Dunn, W. B.; Ellis, D. I. TrAC-Trends Anal. Chem. 2005, 24, 285-294.

case for this study because they have been the subject of extensive toxicity and transcriptional profiling studies.20-27 These and other studies have shown that, although these three compounds are all triazole-based fungicides and the antifungal activity of all three is based on inhibiting lanosterol 14R-demethylase (CYP51), their toxic and biological response profiles each have distinct features in rats. The first and primary goal of the work reported here was to determine whether the Raman method would be capable of differentiating responses of rats exposed (individually) to these three contaminants, via metabolomic analysis of urine. Studies to make this determination were designed to maximize the chance of success. For example, urine samples were filtered using a stirred ultrafiltration cell with a 500 nominal molecular weight limit (NMWL) filter to remove large, unwanted urine components that degrade the Raman spectrum via fluorescence. Also, relatively large sample volumes and long spectral acquisition times were employed. The Raman method was evaluated by comparison to parallel NMR analysis of the unfiltered samples, using conditions that are typical within the field of NMR-based metabolomics. When this phase of the Raman work met with success, a secondary goal was pursued to determine whether the Raman method could be implemented in a more high-throughput manner. Specifically, we tested, in a preliminary way, the feasibility of using a 10-µL sample of unfiltered urine (with no Raman-specific sample preparation) in a microcuvette, with a data acquisition time of 2 min. Results from both phases of this work are reported here. EXPERIMENTAL SECTION Reagents and Materials. Myclobutanil (CAS No. 88671-890; 95.8% purity) was obtained from Dow AgroSciences (Indianapolis, IN). Propiconazole (CAS No. 60207-90-1; 94.2% purity) was obtained from Syngenta Crop Protection (Greensboro, NC). Triadimefon (CAS No. 43121-43-3; 96.7% purity) was obtained from Bayer CropScience (Research Triangle Park, NC). The vehicle for gavage dosing, Alkamuls EL-620 (CAS No. 61791-126), which is a castor oil ethoxylate formulation, was obtained from Rhodia Inc. (West Point, GA). All metabolite chemicals, other than urea, were purchased from Sigma-Aldrich (St. Louis, MO). Urea was purchased from Fisher Scientific (Fairlawn, NJ). These chemicals were used without further purification. All other organic chemicals were certified ACS (20) Ekman, D. R.; Keun, H. C.; Eads, C. D.; Furnish, C. M.; Murrell, R. N.; Rockett, J. C.; Dix, D. J. Metabolomics 2006, 2, 63-73. (21) Rockett, J. C.; Narotsky, M. G.; Thompson, K. E.; Thillainadarajah, I.; Blystone, C. R.; Goetz, A. K.; Ren, H.; Best, D. S.; Murrell, R. N.; Nichols, H. P.; Schmid, J. E.; Wolf, D. C.; Dix, D. J. Reprod. Toxicol. 2006, 22, 647658. (22) Barton, H. A.; Tang, J.; Sey, Y. M.; Stanko, J. P.; Murrell, R. N.; Rockett, J. C.; Dix, D. J. Xenobiotica 2006, 36, 793-806. (23) Goetz, A. K.; Ren, H. Z.; Schmid, J. E.; Blystone, C. R.; Thillainadarajah, I.; Best, D. S.; Nichols, H. P.; Strader, L. F.; Wolf, D. C.; Narotsky, M. G.; Rockett, J. C.; Dix, D. J. Toxicol. Sci. 2007, 95, 227-239. (24) Hester, S. D.; Wolf, D. C.; Nesnow, S.; Thai, S. F. Toxicol. Pathol. 2006, 34, 879-894. (25) Martin, M. T.; Brennan, R.; Hu, W.; Ayanoglu, E.; Lau, C.; Ren, H.; Wood, C. R.; Corton, J. C.; Kavlock, R. J.; Dix, D. J. Toxicol. Sci. 2007, 97, 595613. (26) Tully, D. B.; Bao, W. J.; Goetz, A. K.; Blystone, C. R.; Ren, H. Z.; Schmid, J. E.; Strader, L. F.; Wood, C. R.; Best, D. S.; Narotsk, M. G.; Wolf, D. C.; Rockett, J. C.; Dix, D. J. Toxicol. Appl. Pharmacol. 2006, 215, 260-273. (27) Wolf, D. C.; Allen, J. W.; George, M. H.; Hester, S. D.; Sun, G. B.; Moore, T.; Thai, S. F.; Delker, D.; Winkfield, E.; Leavitt, S.; Nelson, G.; Roop, B. C.; Jones, C.; Thibodeaux, J.; Nesnow, S. Toxicol. Pathol. 2006, 34, 895-902.

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reagent grade, and were used without further purification. All water used in the experiments was filtered with a Barnstead (Boston, MA) NANOpure Infinity water system. A low-volume (∼3 mL) stirred, ultrafiltration cell assembly (Micron model 8003) and 500 NMWL filters (Amicon Ultrafilters Type YC05) were purchased from Millipore (Billerica, MA). Centrifugal filter devices (Microcon YM-3), with a 3000 NMWL, and capable of filtering volumes of 10-500 µL, were also purchased from Millipore. Animals and Treatment. Ten-week-old, male SpragueDawley rats were obtained from Charles River Laboratories (Hollister, CA) and acclimated for ∼5-9 days prior to dosing. Animals were housed individually in metabolic study cages (1 animal/cage). Lab Diet Certified Rodent Chow No. 5002 was available ad libitum via food hoppers or jars, and the animals were not fasted. Water (purified, reverse osmosis) was available ad libitum via an automatic watering device or water bottles. Rats were randomly assigned to treatment groups, and the mean body weights were within 10% of the control group mean. The animals were divided into groups of five individuals; each group was treated with a different toxicant formulation (or control) by oral gavage at a volume of 10 mL/kg for five consecutive days. Each animal was dosed using a disposable syringe with a stainless steel intubation needle. The volume of formulated control and toxicant materials given each animal was based on the animal’s most recent daily body weight. Myclobutanil, propiconazole, and triadimefon groups received 300, 300, and 175 mg kg-1 day-1, respectively. The urine samples used for this work were part of a larger toxicological study in which urine was collected twice every 24 h concurrent with five consecutive days of exposure. The samples described in this report were aliquots of the final collection on day six. Bacterial growth was controlled throughout the collection times by maintaining urine at 0 °C and by the addition of 1 mL of a 1% sodium azide solution. One of the five samples from the animals that were dosed with propiconazole was not used due to a low collection volume. Further experimental details of the rats in this study have been previously published.21 Raman Analysis. For the Raman metabolomic study, ∼1 mL of urine was centrifuged at 10 600 rcf for 3 min to remove solids prior to filtration. The resulting supernatant was transferred to a stirred ultrafiltration cell assembly and passed through a prewashed 500 NMWL filter using 70 psi of nitrogen. For the highthroughput feasibility testing, 10 µL of the supernatant from urine that had been centrifuged (but not filtered) was transferred directly into a microcuvette for analysis. Note that the pH of urine samples for Raman analysis was not controlled. This choice was made to avoid the possibility of Raman bands from common buffers interfering with the bands of metabolites. Rat urine pH has been reported to vary only over a narrow range of about pH 7-8,28 which is consistent with our own findings. We have found that the Raman spectrum of rat urine does not vary appreciably over this narrow range (data not shown). Raman spectra of urine for the metabolomic study were acquired with a Kaiser Optical Systems (Ann Arbor, MI) HoloProbe, using excitation from an Invictus 785-nm laser. This type

of Raman instrument has been fully described elsewhere.29 Briefly, ∼130 mW of laser light was delivered to a standard cuvette, which held ∼1 mL of the sample, via a 50-µm core silica fiber-optic cable. The laser light from the fiber optic was brought to focus at ∼7.5 cm beyond the end of a filtered probe head assembly with an objective using f/2.0 collection. Raman scattered photons were collected along the same path as the excitation laser beam (i.e., 180° backscattering geometry) and were coupled to a separate fiber-optic cable (100 µm core) for delivery to the f/1.8 axial transmissive-type spectrograph. A holographic notch filter was placed along the collection path such that only inelastically scattered light was input into the fiber. The Raman scattered light was then directed through a holographic grating with double transmission layers. This configuration permitted the acquisition of the entire Raman spectrum with a useable Stokes Raman shift of ∼3280-95 cm-1 with ∼5-cm-1 spectral resolution. The detector, a Princeton CCD-1024EHRB backilluminated, deep-depletion, nearinfrared-optimized camera system, was thermoelectrically cooled to -65 °C and contained 1024 × 256 pixels. The same system was used for the high-throughput feasibility testing study, except that the standard cuvette was replaced with a microcuvette with 10-µL capacity. Raman spectra of pure metabolite solutions prepared individually in deionized water were acquired with a Kaiser Optical Systems HoloProbe, using excitation from a Coherent (Santa Clara, CA) Verdi V Series frequency-doubled YVO4 laser emitting 532-nm laser excitation. This spectrometer is very similar in design to the 785-nm system described above, but contains a backilluminated CCD detector (Andor Technology model DU420) appropriate for 532-nm excitation. This configuration permitted the acquisition of the entire Raman spectrum with a useable Stokes Raman shift of ∼4450-100 cm-1 with ∼5-cm-1 spectral resolution. A Kaiser Optical Systems, Inc. HoloLab calibration accessory was used with both spectrometers to correct both the frequency and intensity of Raman spectra. This allowed direct comparison of urine sample spectra (acquired with the 785-nm system) and pure metabolite solution spectra (acquired with the 532-nm system). In addition, frequency correction was improved postacquisition by shifting each spectrum until the atmospheric nitrogen band, which was observed in all spectra, was aligned to a common frequency. For the metabolomic study, spectra were collected with CCD exposure settings and spectrum averaging settings that resulted in an acquisition time of ∼30 min/sample. For the high-throughput feasibility testing, these settings were modified to result in an acquisition time of 2 min/sample. A spectrum of dionized water was collected and subtracted from each urine spectrum. Also, a spectrum of urea (collected and processed under similar conditions) was subtracted from each urine spectrum using an interactive subtraction routine until bands from urea were no longer visible. Next, a spectrum of sodium azide was subtracted from each urine spectrum in the same manner. Urine spectra were then truncated such that only the region of 1705-467 cm-1 remained. The baseline of these truncated spectra was leveled and set to zero, and then the spectra were normalized to unit total

(28) Nicholson, J. K.; Timbrell, J. A.; Sadler, P. J. Mol. Pharmacol. 1985, 27, 644-651.

(29) Collette, T. W.; Williams, T. L.; D’Angelo, J. C. Appl. Spectrosc. 2001, 55, 750-766.

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intensity. All spectral processing was done using the GRAMS/AI 7.00 software package (Thermo Galactic, Waltham, MA). Spectra were then converted to ASCII files without data reduction, and imported into Microsoft Excel (Microsoft Corporation, Redmond, WA). NMR Analysis. For comparing metabolite profiles in filtered versus unfiltered urine by NMR, ∼200 µL of urine (either filtered or unfiltered) from one of the control animals was added to 350 µL of 0.1 M sodium phosphate-buffered deuterium oxide containing 37 µM sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4) (TSP) as a chemical shift reference. (Note that urine filtration was accomplished as described in the Raman Analysis section, using the stirred ultrafiltration cell assembly and the 500 NMWL filter.) Each sample was then centrifuged at 10 600 rcf for 10 min at 4 °C to remove solids, and the resulting supernatant was pipetted into a standard 5-mm NMR tube. Preparation of urine samples for the NMR metabolomic study was as follows: 250 µL of 0.1 M sodium phosphate-buffered deuterium oxide (pH 7.4) containing 3 mM sodium azide and 1 mM TSP was added to 400 µL of urine for each animal. Each sample was then centrifuged at 10 600 rcf for 10 min at 4 °C to remove solids, and the resulting supernatant was pipetted into a standard 5-mm NMR tube. One-dimensional (1D) 1H NMR spectra for comparing filtered and unfiltered urine metabolite profiles were acquired at 25 °C on a Varian Inova 600 NMR spectrometer (1H, 599.76 MHz) using a standard triple-resonance probe. For each spectrum, 256 transients were collected using a standard 1D NOESY pulse sequence. Saturation pulses were used to suppress the water resonance prior to the NOESY sequence and during the mixing time (100 ms). Other acquisition parameters include a spectral width of 6600 Hz and an acquisition time of 1.0 s. Both spectra were processed with zero-filling to 24K points and application of 0.3-Hz line broadening. 1D 1H NMR spectra for all urine samples for the metabolomic study were acquired at 25 °C on a Varian Inova 800 NMR spectrometer (799.73 MHz, 1H) using a cryogenic triple-resonance probe. All spectra were acquired using a standard 1D pulse sequence with presaturation of the water resonance. Acquisition parameters for these spectra include a spectral width of 10 000 Hz and an acquisition time of 2 s. Sixty-four transients were collected for each spectrum. Processing included zero filling to 64K points and application of 0.5-Hz line broadening. To confirm metabolite resonance assignments, 1H-1H correlation spectroscopy (COSY) was also used.30 All COSY data were acquired at 25 °C on a Varian Inova 600 NMR spectrometer (599.76 MHz) using a standard triple-resonance probe. Acquisition parameters include the following: a 1 s presat, 6 kHz spectral window, 1024 data points, 128 increments, and n-type for the indirect dimension. All 1H chemical shifts were internally referenced to TSP. Peak assignments were made according to previously reported values.5,31,32 For the metabolomic study, all NMR spectra were phased, baseline corrected, and referenced to TSP. Then, the following regions were omitted from the spectra due to the occurrence of (30) Aue, W. P.; Bartholdi, E.; Ernst, R. R. J. Chem. Phys. 1976, 64, 2229-2246. (31) Fan, T. W. M. Prog. NMR Spectrosc. 1996, 28, 161-219. (32) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C. Anal. Chem. 1995, 67, 793-811.

resonances from the carrier vehicle: 0.8-2.5, 3.5-3.9, 4.1-4.35, and 4.7-6.35 ppm (which also includes the water and urea resonances). The remaining spectral data from 0.5 to 9.5 ppm were reduced by segmenting into bins of 0.04 ppm in width and then normalized to unit total intensity. All spectral processing and binning was done using the ACD/SpecManager software package (Advanced Chemistry Development, Toronto, Canada). The reduced data set was then imported into Microsoft Excel. Multivariate Data Analysis. Both Raman and NMR data sets were imported from Excel into SIMCA-P+ 11.0.0.0 (Umetrics Inc., Umea, Sweden). First, a Raman model and an NMR model were built using principal component analysis (PCA) with all four classes. For these two models, both Raman and NMR data sets were pretreated using an approach that is commonly employed in metabolomics. Specifically, both spectral data sets were mean centered and scaled using Pareto scaling. (Note that variable scaling is beneficial when applying metabolomics for biological class differentiation because, when using unscaled data, tools such as PCA will be overly dependent on highly concentrated metabolites, which may or may not be most biologically relevant. Pareto scaling, which uses the square root of the standard deviation as the scaling factor, is very commonly employed.) Next, each of the exposed classes were compared to the control class in a pairwise fashion, using a series of partial least-squares discriminant analysis (PLS-DA) models. For both the Raman and NMR pairwise PLS-DA models, which were used solely to identify metabolites affected by the exposure, the data sets were mean centered only (i.e., no scaling or transformation was employed). RESULTS AND DISCUSSION Filtration of Urine To Remove Nontarget Components that Fluoresce. A principal concern for Raman-based metabolomics using urine is the possibility that fluorescence (and the accompanying shot noise) from the sample will significantly degrade the Raman spectrum. However, note that in metabolomics we are interested in profiling the levels of relatively low molecular weight endogenous metabolites, which do not emit significant fluorescence when excited at 785 nm. Indeed, much of the sample fluorescence will be due to higher molecular weight components of the urine that are not targets of the analysis. For this reason, and to maximize the chance of success of this investigation, all of the urine samples were filtered prior to metabolomic analysis with a 500 NMWL stirred ultrafiltration cell. Traces A and B in Figure 2 are spectra of a control rat urine sample before and after filtration, respectively, collected using the same acquisition parameters. Only the region of 1705-475 cm-1 is shown because most of the useful metabolite bands fall within this range. Note in Figure 2A that spectral bands from metabolites in the unfiltered sample, while clearly observable, exhibit considerably more shot noise due to fluorescence than those in the filtered sample (Figure 2B). While filtering significantly improves the S/N of Raman spectra of urine, there is some concern that filtering may change the sample in ways that are not desirable. Even though all metabolites of interest have molecular masses below 500 Da, some may still be removed by the filter. For example, the loss of citrate (MW 189), which is known to form trimeric species in aqueous solutions,33 was expected. We were concerned that issues of this (33) Malone, S. A.; Cooper, P.; Heath, S. L. Dalton Trans. 2003, 4572-4573.

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Figure 2. (A) Region of the water-subtracted Raman spectrum (785nm laser excitation) of urine from a control rat. (B) The same type of spectrum as shown in (A), collected after filtering the sample through a stirred ultrafiltration cell with a 500 nominal molecular weight limit filter. (C) Region of the one-dimensional 600-MHz 1H NMR spectrum of the same sample before filtration and (D) after filtration.

type might not be limited just to citrate. Therefore, we sought to determine experimentally the impact that filtration would have on sample integrity. For a number of reasons, this determination is more easily made by comparing NMR spectra of a urine sample collected before and after filtering, which are depicted in Figure 2C and D, respectively. As expected, the percent removal of citrate is very high. Also, trans-aconitate (which, like citrate, is a tricarboxylate) is almost completely removed. The only other metabolite that we found to be significantly removed is oxoglutarate (another tricarboxylate), but the removal in this case was only ∼45%. Although filtering does, indeed, change the urine samples in ways that are not desirable, it was assumed that the change would be essentially the same for all samples. If true, we believe this should not pose a significant problem for the use of multivariate analysis of Raman spectra to differentiate responses of rats exposed to chemical contaminants. Interestingly, in a recent NMRbased metabolomic study with human urine, the citrate peak region was omitted from multivariate models because citrate interacts with boric acid, which was used as a preservative.34 Removing the citrate peak region was found to not impair data classification using multivariate techniques. (34) Dumas, M. E.; Maibaum, E. C.; Teague, C.; Ueshima, H.; Zhou, B. F.; Lindon, J. C.; Nicholson, J. K.; Stamler, J.; Elliott, P.; Chan, Q.; Holmes, E. Anal. Chem. 2006, 78, 2199-2208.

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In addition to investigating specific metabolite removal rates upon filtration, the NMR spectra in Figure 2C and D are interesting in other respects as well. For example, note that filtration provides some baseline flattening; however, this improvement is not dramatic. Also, note in Figure 2C that peaks due to the carrier vehicle (used in dosing the rats) are observed in the NMR spectra. These components experience about a 35% reduction upon filtering. However, the carrier vehicle resonance at 3.71 ppm is still the largest resonance observed. (The confounding effects of carrier vehicles in NMR-based metabolomic studies have recently been described in some detail.35) Interestingly, there are no bands observed in the Raman spectra of these urine samples that we can attribute to the carrier vehicle. We believe this is because bands from the carrier vehicle are relatively weak and broad (compared to bands from metabolites), contributing mainly to the background, which is removed in spectral preprocessing. Our Raman spectra of the pure carrier vehicle (not shown) appear to support this assumption. Spectral Resolution of Raman and NMR. The change in urine sample integrity upon filtering is more clearly observed in high-field NMR spectra, in large part, because they are much more highly resolved than our Raman spectra. Note that there are Raman spectrometers available with significantly higher resolution than the one used for these experiments. However, using a higher resolution spectrometer would likely not improve the situation dramatically. Since most metabolites exist as charged species in urine, the natural line widths of Raman bands are quite large due to extensive molecular interactions. This results in broad overlapping bands. This is not the case with NMR, and this is a major advantage for identifying endogenous metabolites in urine. However, it should be noted that NMR spectra are deresolved, often severely, before they are submitted to multivariate analysis in metabolomic studies. For this deresolution step, which is called “binning” or “bucketing”, the NMR spectrum is first divided into segments (or bins) of equal widths. Then, all intensity values that fall within a given bin are summed. Binning is commonly considered beneficial for multivariate analysis in NMR-based metabolomic studies, because it mitigates any X-axis imprecision, such as peak shifts due to pH changes.7 Also, it is a significant data reduction step (e.g., from 16k to less than 2k points), which can expedite multivariate analysis. Although a variety of bin widths have been utilized in published NMR-based metabolomics studies, 0.04 ppm bins appear to be most common.3,34,35 (However, bin widths as narrow as 0.01 ppm are employed with some regularity.36,37) Figure 3A depicts a 600-MHz NMR spectrum of urine from a control rat used in this study. (Note that, for ease of comparison, only the region from 1.8 to 4.2 ppm is displayed. This region represents about the same fraction of the total useful NMR spectrum as is used for the Raman spectra displayed herein.) Figure 3B displays the binned data from this spectrum as a bar graph, using 0.04 ppm bins. The height of a given bar is a measure of the integrated NMR intensity that falls within that bin. Figure (35) Beckwith-Hall, B. M.; Holmes, E.; Lindon, J. C.; Gounarides, J.; Vickers, A.; Shapiro, M.; Nicholson, J. K. Chem. Res. Toxicol. 2002, 15, 1136-1141. (36) Bundy, J. G.; Osborn, D.; Weeks, J. M.; Lindon, J. C.; Nicholson, J. K. FEBS Lett. 2001, 500, 31-35. (37) Samuelsson, L. M.; Forlin, L.; Karlsson, G.; Adolfsson-Eric, M.; Larsson, D. G. J. Aquat. Toxicol. 2006, 78, 341-349.

Figure 3. (A) Region of the one-dimensional 600-MHz 1H NMR spectrum of urine from a control rat. (B) Bar graph of data from binning the spectrum above, using 0.04 ppm bins. (C) Binned data plotted as a spectrum.

3C shows the binned data replotted as a spectrum. (To plot the binned data set as a spectrum, the midpoint of each bin was used for the X-value.) This spectrum, which is not of significantly higher resolution than typical Raman spectra, is therefore representative of NMR data that are commonly submitted to multivariate analysis for metabolomics studies. (Note that we found no advantage or need to deresolve Raman spectra for multivariate analysis.) This strongly suggests that the limited resolution of Raman spectra should not be a disadvantage for classification with multivariate analysis when compared to conventional NMR-based metabolomics. Effect of Subtracting the Urea Spectrum from Raman Spectra of Urine. Prior to conducting metabolomics studies, standards of ∼25 metabolites that occur abundantly in rat urine were acquired.28 We then collected a Raman spectrum of each of these metabolites individually as a pure component in aqueous solution. This small library of metabolite spectra was useful during experimental design, and it also was used as an aid when making metabolite assignments in rat urine spectra. Figure 4A displays an overlay of individual spectra from 15 of these metabolites that are among the most abundant. These spectra were collected using individual solutions prepared at physiologically relevant concentrations. Figure 4B is the spectrum of a filtered urine sample from a control rat. Note that bands from urea strongly dominate the rat urine spectrum because of the high concentration and strong Raman scattering of urea. For this reason, the urea spectrum was digitally subtracted from all rat urine spectra as a data processing step, prior to conducting multivariate analysis. The urine spectrum in Figure 4B is displayed again in Figure 4C after the urea pure-component spectrum was

Figure 4. (A) Overlay of a region of the water-subtracted Raman spectra of 15 of the most abundant urinary metabolites, each dissolved individually in deionized water. Spectra are Y-axis scaled to reflect physiologically relevant levels. (B) Region of the filtered water-subtracted Raman spectrum of control rat urine. (C) Spectrum of rat urine shown in (B), following digital subtraction of the urea spectrum.

subtracted. Note that several more bands from other metabolites are observable after the spectrum of urea is removed. For example, the largest band from hippurate (at ∼1002 cm-1) is easily observed after urea subtraction, but entirely obscured by the much larger band from urea at ∼1004 cm-1 in the urine spectrum. The spectrum in Figure 4C is representative of those submitted to multivariate analysis for the Raman-based metabolomics feasibility phase of this work. (Also, note that peaks from urea are frequently removed from NMR spectra in the course of NMR-based metabolomics studies with rodent urine.38) Raman and NMR PCA Modeling of All Classes. PCA was used to assess the ability of Raman spectroscopy to differentiate responses of rats to the three triazole fungicides, using filtered urine samples. As a benchmark for comparison, a similar PCA model was built using 0.04 ppm binned NMR spectra that were acquired on the unfiltered urine samples. Panels A and B in Figure 5 are two-dimensional scores plots obtained from the Raman and NMR PCA models, respectively. Class separation in both models is reasonably good, particularly considering that this is a nonsupervised model of four classes, each of which contains only four or five members. In fact, performance of the Raman model may be, in some ways, preferable to that of NMR for this data set. For example, 37.9% of the variation in the Raman data was captured in the first principal component (PC1), whereas only 25.1% was captured in PC1 for the NMR data. (38) Robertson, D. G. Toxicol. Sci. 2005, 85, 809-822.

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Figure 5. (A) Scores plot (first two components) from a PCA model built from water- and urea-subtracted Raman spectra of filtered urine collected from rats on day 6 of a daily exposure to triazole fungicides. (B) The same scenario as in (A), but using one-dimensional 1H NMR spectra of the unfiltered urine samples. Ellipses are the 95% confidence regions based on the Hotelling’s T2 distribution.

Some information from the Raman and NMR PCA models is consistent (perhaps useful as confirmation), but some appears to be complementary. For example, for both models, only one outlier is observed using the Hotelling’s T2 test at the 95% confidence level. This sample is from rat 26, which was exposed to myclobutanil. The source of this outlier behavior was found by Raman, and then confirmed by NMR, to be due to a high level of acetate in the urine of this rat. Also, note that the sample from rat 57, which was exposed to triadimefon, is not clustered with the triadimefon exposure group in the Raman model but is, instead, indistinguishable from control samples. The situation for this sample is not so severe in the NMR model. Finally, note in the Raman model that the triadimefon group is most clearly distinguished from the other treatment groups (i.e., the myclobutanil and propiconazole groups are more similar). Interestingly, gene expression profiles that were generated from rat livers collected in this study also showed that, among treatment classes, there was greater similarity between the myclobutanil- and propiconazole-exposed classes.25 Greater similarity of the myclobutanil and propiconazole treatment groups (relative to triadimefon) is not so clearly observed in the NMR model. These findings suggest that the Raman- and NMR-based metabolomic methods can, upon further development and analysis, offer information on impacts of chemical exposures that is complementary. Weight Loading Vectors from Pairwise Raman PLS-DA Models. In addition to modeling all four of the classes simultaneously, each of the exposed classes was also compared to the control class in a pairwise fashion (e.g., control vs triadimefon class), using a series of Raman-based PLS-DA models. (Note that the outlier sample from rat 26 was removed prior to this analysis.) Plots of the first weight loading vectors from these three pairwise models (Figure 6) were used to determine some of the metabolites that are responsible for class separation. Positive-going peaks in these loading plots can be attributed to metabolites that increase 7330 Analytical Chemistry, Vol. 79, No. 19, October 1, 2007

as a result of the exposure, and negative-going peaks can be attributed to those that decrease. Note in Figure 6 that there are some metabolite changes that are common, both in extent and in direction, among the three weight loading vectors. These changes might be candidate markers for effects that are common to these three triazoles. In addition, note that each weight loading vector exhibits some features that are unique. These features might prove useful as markers for assessing individual chemical exposures. Several of these potential markers of exposure, which are consistent with observations from analogous NMR-based PLS-DA models, are labeled in Figure 6. High-Throughput Feasibility Testing. After the Raman approach was found to offer promise as a metabolomic technique, we conducted an additional and preliminary study to assess the feasibility of implementing the method in a high-throughput fashion. Toward that end, the normal cuvette in the Raman spectrometer was replaced with a microcuvette and filled with 10 µL of an unfiltered sample of urine from one of the control rats. The acquisition time was shortened from 30 to 2 min for this analysis. The spectrum of this sample is displayed (after water and urea spectrum subtraction) in Figure 7A. This spectrum does exhibit significantly greater noise than the spectrum of the filtered sample that was collected during the metabolomic phase using much more volume and longer data acquisition time (Figure 4C). However, note that all peaks are, indeed, still clearly observed in the spectrum of Figure 7A. Based on some limited investigation, it appears that most of this additional noise is due to the presence of high molecular weight fluorescing components (that were removed in the case of samples that were filtered), as opposed to the use of small volumes or short data acquisition times. If the noise level for the spectrum in Figure 7A was deemed too high for multivariate analysis, note that it can be reduced postacquisition by digital smoothing. To demonstrate, we subjected the

Figure 6. Weight loading vectors from the first component of three PLS-DA models built using water- and urea-subtracted Raman spectra of filtered urine collected from rats on day 6 of a daily exposure to triazole fungicides: (A) myclobutanil, (B) propiconazole, and (C) triadimefon. Positive-going peaks are due to endogenous metabolites that are increased (relative to the controls) upon exposure, while negative-going peaks are due to those that are decreased upon exposure. Note that all three loadings plots are displayed using the same Y-axis scale.

spectrum in Figure 7A to a Savitzky-Golay smoothing routine (using five points in a second degree algorithm) and obtained the spectrum displayed in Figure 7B. This smoothed spectrum of the unfiltered sample exhibits S/N that is comparable to that of the filtered sample spectrum that was used in the metabolomic phase (Figure 4C). However, note that we have not confirmed that all 19 samples used in this study will yield spectra of a quality comparable to that in Figure 7A under these high-throughput conditions. Indeed, we have found that the amount of fluorescence in rat urine spectra does vary from sample to sample; but the variation does not appear to be extreme. For example, we analyzed a total of three samples using high-throughput conditions, and in each case, a spectrum suitable for metabolomic analysis was obtained. But, to be conservative, the spectrum in Figure 7A should be considered the best-case scenario for this data set and for these highthroughput settings. If these particular conditions were later found to yield unacceptable spectra for metabolomic analysis for a given data set, a number of remedies that would still accommodate relatively high throughput could be employed. For example, one could use a laser that emits further into the near-IR. Indeed, it is possible that considerably less fluorescence would be observed with spectrometers employing longer wavelength lasers (such as an 830-nm diode laser), without an unacceptable decrease in Raman signal (which is approximately proportional to the inverse of the fourth power of the excitation wavelength).

Figure 7. (A) Region of the water- and urea-subtracted Raman spectrum of 10 µL of unfiltered control rat urine. Spectral acquisition time was 2 min. (B) The same spectrum as shown in (A), after applying a Savitzky-Golay digital smoothing routine.

Another potential remedy would be to employ centrifugal filter devices that quickly remove molecules above 3000 Da. These filters accommodate sample volumes of 10-500 µL, and they can be employed in batch mode (1 filter/sample). Some preliminary work in our laboratory (data not shown) demonstrates that the extent of fluorescence in rat urine can be reduced significantly with these devices. While this reduction in fluorescence is not as great as with the 500 NMWL ultrafiltration cells, the sample integrity is much better preserved, because virtually no removal of metabolites is observed. In fact, the use of these centrifugal 3000 NMWL filter devices may be an excellent compromise between the very high-throughput approach that led to the spectrum in Figure 7A and the optimal S/N method (employing the 500 NMWL ultrafiltration cell) that led to the spectrum in Figure 4C. CONCLUSIONS These results demonstrate that normal Raman spectroscopy (with near-infrared laser excitation) is a viable metabolomic tool for investigating the exposure of rodents to environmental chemicals under typical toxicological study conditions, using urine. In fact, the Raman data proved to be as useful for distinguishing between classes with PCA analysis as the NMR data that were generated under typical conditions. After the Raman metabolomic approach proved successful, a separate and preliminary study was conducted to test the feasibility of high-throughput implementation. This study suggested that the Raman technique could be employed for rapid classification in a fashion that compares favorably to NMR, but at a fraction of the instrumentation cost. On the other hand, specific metabolite changes can be more readily identified with high-field NMR because of the large (and overlapping) widths of metabolite bands in Raman spectra of urine. Analytical Chemistry, Vol. 79, No. 19, October 1, 2007

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Thus, we do not intend to imply that Raman should replace NMR or any other technique that is already established as a metabolomic tool (e.g., MS, GC, etc.). Instead, we assert that Raman is suitable to join this arsenal of tools, each of which offers confirmatory and complementary information that can be brought to bear on this expanding area of research. ACKNOWLEDGMENT This paper has been reviewed in accordance with the U.S. Environmental Protection Agency’s peer and administrative

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review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. D.P.C. thanks the National Research Council for supporting this work.

Received for review April 26, 2007. Accepted July 15, 2007. AC070856N