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Gelified biofluids for HRMAS H NMR analysis: the case of urine Panteleimon G. Takis, Leonardo Tenori, Enrico Ravera, and Claudio Luchinat Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b04318 • Publication Date (Web): 03 Jan 2017 Downloaded from http://pubs.acs.org on January 3, 2017
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Gelified biofluids for HRMAS 1H NMR analysis: The case of urine Panteleimon G. Takis,† Leonardo Tenori,‡,¥ Enrico Ravera,¥ and Claudio Luchinat*,†,¥,§ †
Giotto Biotech S.R.L., Via Madonna del Piano 6, 50019 Sesto Fiorentino (FI), Italy. ‡Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy. ¥Magnetic Resonance Center (CERM), University of Florence and Interuniversity Consortium for Magnetic Resonance of Metalloproteins (CIRMMP), Via L. Sacconi 6, 50019 Sesto Fiorentino (FI), Italy. §Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino (FI), Italy. ABSTRACT: In this letter we propose an alternative, effective protocol for metabolomic characterization of biofluids based on their gelification and subsequent application of high resolution magic angle spinning (HRMAS) 1H nuclear magnetic resonance (NMR). The sample handling is very rapid and reproducible, and much less than 40 µl of neat urine are needed to obtain a sample. Our results indicate that the HRMAS spectra of gelified urine encompass all metabolites in the NMR fingerprint, as observed by solution NMR. The proposed approach can be efficiently integrated into the NMR based metabolomics analyses routines: multivariate statistical analysis of both solution and HRMAS data produced very similar statistical models, with high classification accuracy. One of the key advantages offered by the gelification approach is the improved short term (up to 24 hours) preservation of nonfrozen HRMAS NMR gel urine samples compared to the solution samples, which could lead to an alternative way for transportation or domestic collection of biofluids, without the need of cold-storage and reducing the risks of leakage. NMR spectroscopy is an emerging clinical tool for studying metabolism in detail,1 allowing for early diagnosis of several diseases.2,3 NMR based metabolomics has high reproducibility and intrinsic "untargetedness", which provide high precision and accuracy for quantification and structure elucidation of many metabolites in biofluids.4 The vast majority of NMR based metabolomics studies is carried out by solution 1H NMR, focusing on urine and serum/plasma.1 It is to be stressed that NMR-based metabolomics requires standardized sample preparation protocols to achieve reproducibility.2,5,6 The use of HRMAS NMR yields access to solid or semisolid specimens, such as tissues7–10 (e.g. tumor biopsies). We here introduce a new possibility offered by HRMAS: the study of gelified biofluids, proving that gelification enhances 24-hours stability and simplifies sample transportation, without impacting significantly on resolution and sensitivity. To demonstrate this approach, we mixed human urine with a specific amount of fumed silica gel particles, obtaining ready-to-use gel samples for HRMAS. Our approach satisfies all the requirements for performing metabolomics studies (i.e. reproducibility and analytical sensitivity), produces similar statistical/metabolic profiling results to solution NMR, and improves storage up to 24 hours. This strategy complements the standard procedures of NMR-based metabolomics, either in laboratory settings for instance in case of low biofluid availability, as it may be the case for metabolomics on small animals; or in clinical settings, for instance urine transportation or domestic collection/short term storage devices.
RESULTS AND DISCUSSION Urine gelification and HRMAS 1H NMR analysis. In the following, it is implied that urine is already mixed with the common buffer for urine NMR metabolomics (see Supporting Information). To achieve gelification of the biofluid for analytical purposes, the gelifying agent should not produce any 1H NMR signals that can disturb the profile of the biofluid, or anyway significantly modify the pattern of detectable metabolites. We selected fumed silica particles (SiO2, CAS number: 112945-52-5) with size less than 0.007 µm, which are widely used in industry as thickening agents.11 These non-porous silica particles are convenient because of their low surface to volume ratio, which minimizes the absorption of metabolites and the subsequent perturbation of the NMR fingerprint. Moreover, these silica
Figure 1. The final mixture of fumed silica gel particles and urine with the ratio 1(mg):18-20(μl), respectively, inside a 50 ml capacity falcon depicted (A) vertically and (B) horizontally placed. In the horizontal position, the 3 different zones from
MATERIALS AND METHODS Details of NMR apparatus/experiments, reagents and chemicals used for the study as well as the statistical analysis methods followed, are described in the Supporting Information.
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geneity of the mixture are highlighted.
Figure 2. (A) Example of the ideal silica:urine ratio (s:u) samples NMR spectra; HRMAS and solution 1D NOESY 1H NMR human urine profiles (spectral region: 6.85–9.15 ppm), are compared, indicating that all resonances are present in the HRMAS experiments. The rest NMR spectral regions comparison are reported in Figure S2. (B) Disappearance of 3-methyl-L-histidine resonances at 1(mg):15(μl) s:u.
mal urinary density (i.e. maximum durine = 1.030 g/l, see Supporting Information for further details). This outcome is a substantial advantage in cases of low biofluid abundance. The calculated
particles are not listed as carcinogenic, as opposed to larger silica particles, by the Occupational Safety and Health Administration (OSHA) and International Agency for Research on Cancer,12 facilitating the use of a future fumed silica based device for domestic urine collection/immobilization and samples transportation. A relatively large amount of silica is required to thicken the solution so as to make the processing as reproducible as possible; however, the amount of silica must not cause any loss of information about the urine metabolites. We found the optimal ratio to be 1(mg):18-20(µl) silica:urine (s:u). As shown in Figure 1, the silica gel/urine mixture is thick enough to avoid liquid leakage, but it can be still easily transferred in an insert for HRMAS by use of a conventional pipette, and the urine NMR profile obtained by HRMAS is fully comparable with the solution one (Figure 2A, S2). Only metabolites containing imidazole rings (e.g. histamine, histidine and their derivatives), exhibit shifting of imidazole proton resonances (Figure 2A), which could imply changes of pH and/or ionic content upon addition of silica. We have found that the pH of the samples was not decreased sizeably upon gelification (0.2 lower for the gel, e.g.: 7.1 to 6.9), and therefore imidazole rings are still largely neutral, thus other interactions must be occurring. No additional molecules were added to prevent the interaction as their effect on the urine NMR profile would be larger than that of these interactions. Of note, imbalances with respect to the 1(mg):18-20(µl) s:u, lead either to gel malformation or to loss of some peaks, probably due to a stronger interaction with the silica particles. Figure 2B shows the disappearance of 3-methyl-L-histidine signals when 15 μl of urine were mixed with 1 mg of silica, leading to more compact gelification. Assuming the homogeneity of the gel (vide infra), the same amount of urine is analyzed for each sample, requiring no further normalization procedures for the statistical analysis of NMR, as it is required for tissues and other bio-specimen.8
signal-to-noise ratio (S/N) of the HRMAS spectra (24 μl of urine) was 2.5 times less than that of the solution NMR spectra (450 µl of urine), and for their acquisition the same delay time (d1) period was used (d1 = 4 s). The homogeneity of gel samples is a crucial aspect for the reproducibility of the NMR fingerprint. Three gelified urine samples were prepared in three 50ml falcons (Figure 1A) from the same urine batch. Subsequently, three HRMAS rotor inserts were filled with material taken from the bottom, the middle and the surface of the first, second and third gel urine falcon, respectively, as depicted in Figure 1B. For each sample one dimensional (1D) NOESY13 (Nuclear Overhauser Effect Spectroscopy) and CPMG14 (Carr, Purcell, Meiboom, and Gill ) spectra (see Supporting Information for further details) were acquired. This same procedure was followed for urine batches from 6 subjects. By this approach, we were able to validate both the homogeneity of the gel and the reproducibility of the preparation procedure. Figure S3 shows an example of HRMAS spectra of three gel urine samples from the same urine batch, each one taken from a different zone from a different vial. The superimposition of the spectra (Figure S3) clearly demonstrates the reproducibility of the method. The same holds for all urine batches. Application to metabolomics and metabolic fingerprinting studies by NMR. We collected one urine sample per day for 3 days from 4 subjects (2 male and 2 female), and their HRMAS (gel urine) and solution 1D NOESY 1H NMR spectra were acquired. Recent studies have demonstrated how the individual phenotype is reflected in the urine NMR profiles.15–17 We thus tried to test the individual phenotyping from both urine and gel urine NMR spectra. Spectra were normalized to total intensity, and the usual data reduction was performed through bucketing, followed by Principal Component Analysis (PCA) and Orthogonal Partial Least Squares–Discriminant Analysis (OPLS-DA) (see Material and Methods in the Supporting Information for statistical data normalization details). Herein, we focus upon the OPLS-DA results (Figure 3, Table S1), because the supervised statistical analysis of OPLS-
All HRMAS samples consisted of 32 mg gelified urine (~40 μl volume, containing 27 ± 1 μl of urine, see Supporting Information for NMR acquisition details and estimation of urine volume in a gel urine sample). The urine volume of each HRMAS sample was estimated by considering the density of the gel. The latter is definitely higher than the maximum nor-
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DA is capable of maximizing the independence of the variables, for higher discrimination of individual metabolic profiles.18
We have found that both the solution (Figure 3A) and the gel (Figure 3B) NMR data lead to discrimination of the individual phenotypes with high accuracy (Table S1). In particular, HRMAS-based classification of subjects P and S is even more
Figure 3. The OPLS-DA statistical analysis score and first latent variables (colored according to the most weighted variables-metabolites, indicated by asterisks) plots of the 4 subjects classified urine 1H NMR profiles by (A), (C) solution and (B), (D) HRMAS NMR spectra. Each group consists of 3 NMR spectra from three urine samples of each subject, collected for 3 days. Both score and latent variables plots clearly demonstrate the same statistical and classification results, with almost the same classification cross-validated accuracy (Table S1).
accurate than that obtained by the conventional solution NMR spectra. The produced score plots (Figure 3A, B) depict almost the same patterns of classified groups (individuals). We notice, however, that the OPLS-DA first latent variables component of solution NMR spectra captures slightly more variance (78.21 %) than the one produced from the HRMAS spectra (67.68 %). This could be attributed to the lower S/N of HRMAS spectra. However, the same weighted variables (i.e. metabolites) are extracted from the latent variables plots in both solution (Figure 3C) and HRMAS (Figure 3D) based models. In addition to this standard evaluation of the NMR profile, the gelification method also allows for absolute quantification of metabolites. To prove this point, we have per-
formed several spiking experiments (Figure S4), to extract the concentration of three metabolites, namely betaine, guanidoacetic acid and N,N-dimethylglycine in one gel urine sample. This was an additional confirmation that the approach based on HRMAS of gelified biofluids can integrate well with the existing methodologies in metabolomics. Short term storage of gelified and normal urine samples. Gelification allows for easy biofluid transportation, minimizing the risk of urine leakage. To exploit this possibility, one would also need better short term storage of the bio-specimens as compared to neat urine in non-frozen state. To test stability, we analyzed 6 gel and 6 solution urine samples from 6 different
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Figure 4. The common changes detected in the urine 1H NMR profile of 3 subjects NMR samples (1H NMR and HRMAS 1H NMR for solution and gel urine, respectively) during zero (0 h) to 24 hours (24 h), stored at 277 Κ (domestic fridge temperature). Arrows (↑, ↓) indicate the increase or decrease of metabolites concentration. For (A) we observe shifting (∆δ) of glutamine and citrate resonances, and succinate concentration increase; for (B) there is a clear perturbation of a very crowded region where several resonances are observed (sugars, nucleot(s)ides, etc.); for (C) the acetate concentration increases. HRMAS spectra show no differences in the concentrations of urine metabolites, and few NMR signals exhibit insignificant or no changes as compared to the solution NMR spectra.
subjects. HRMAS and solution 1H NMR spectra were acquired every 2 hours up to 24 hours (i.e. 6 NMR spectra for each gel and 6 for each solution sample). During the 24 hours, each sample was stored in fridge at 277 K, whereas the NMR acquisition (~10 min) took place at 300 K. Not surprisingly, significant changes in the urine NMR profiles were observed during the 24 hours period. These changes include alterations in the concentration of some metabolites (e.g. increase of creatine, succinate and acetate, etc.) (Figure 4) and shifting of the resonances of some metabolites (Figure 4A). Such changes could be attributed to chemical reactions or enzymatic activities occurring in the samples and/or alteration of pH and ions concentration. These phenomena have been reported in the past5 and their explanation is beyond the scope of the present work. Figures 4 and S5 summarize some of the common changes observed in the majority of solution samples, and compare them with the corresponding (gel) HRMAS spectra. The comparison clearly demonstrates that, over a 24h time period, gel samples experience fewer changes than the corresponding solution samples. In particular, the concentration of metabolites in the gel remained constant (Figure 4, S5), although shifting of some resonances is observed (e.g. citrate NMR peaks, Figure S5C). This indicates a better preservation of urine as gel compared to a solution over a short time period, without freezing. On this basis, it appears that gelification of urine could be used for safe transportation/preservation for about 24 hours, without loss of metabolomic information, and lifting the requirement for cryoprotecting agents. These properties are particularly attractive for domestic collection. HRMAS also offers the possibility of analyzing complex mixtures19 by "NMR chromatography".20,21 In this approach, a chromatographic stationary phase is used to enhance the "contrast" of diffusion coefficients as measured by pulsed field gradient (PFG) diffusion ordered spectroscopy (DOSY), and to deconvolute the content of mixtures,22 focusing on the identification of small molecules in complex mixtures.23 In the present preparations, the silica particles have been selected according to safety considerations and also on a low surfaceto-volume ratio, which makes them not ideal as stationary phase;23 nevertheless, a clear differential reduction of the diffusion coefficient is present, increasing the “contrast” of the DOSY measurement (See Figure S6). The presence of silica in
the preparation intrinsically offers this further opportunity in biofluids HRMAS NMR fingerprinting. We also note that the silica particles can harbor radicals for dynamic nuclear polarization.24 Obviously, further implementation work is needed along both lines.
CONCLUSIONS In the present study we introduced a sample preparation method for NMR-based metabolomics, relying on the gelification of biofluids and subsequent analysis by HRMAS 1H NMR spectroscopy. The proposed urine gelification protocol is rapid and reproducible. In addition, it allows for individual phenotyping with the same accuracy as obtained from solution NMR. Each HRMAS sample contains more than an order of magnitude less volume than the corresponding solution samples. Although gelified urine in HRMAS has 2.5 times lower S/N compared to solution NMR, yet all the resonances are present. It was enough to acquire twice the number of scans (~10 min HRMAS NMR acquisition for 1D NOESY, 64 scans) with respect to those routinely used in solution NMR (~7.0 min standard solution NMR acquisition for 1D NOESY, 32 scans) to be able to assign as many metabolites as via solution NMR. Finally, our gelification technique could be useful for safe biofluid transportation in non-frozen state, without any perturbation of the NMR profile up to 1 day; and could represent a first step towards domestic, portable devices for biofluids short term storage, with ready samples for clinical analysis.
ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Spectroscopic experiments/apparatus, details of statistical analyses, HRMAS and solution NMR spectra comparison, and an example of a DOSY HRMAS spectrum are included in the Supporting Information (PDF).
AUTHOR INFORMATION Corresponding Author
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Analytical Chemistry (7) Doty, F. D.; Entzminger, G.; Yang, Y. A. Concepts Magn. Reson. 1998, 10, 239–260. (8) Cacciatore, S.; Hu, X.; Viertler, C.; Kap, M.; Bernhardt, G. A.; Mischinger, H. J.; Riegman, P.; Zatloukal, K.; Luchinat, C.; Turano, P. J. Proteome Res. 2013, 12, 5723–5729. (9) Renault, M.; Shintu, L.; Piotto, M.; Caldarelli, S. Sci. Rep. 2013, 3, 3349. (10) Tripathi, P.; Somashekar, B. S.; Ponnusamy, M.; Gursky, A.; Dailey, S.; Kunju, P.; Lee, C. T.; Chinnaiyan, A. M.; Rajendiran, T. M.; Ramamoorthy, A. J. Proteome Res. 2013, 12, 3519–3528. (11) Flörke, O. W.; Graetsch, H. A.; Brunk, F.; Benda, L.; Paschen, S.; Bergna, H. E.; Roberts, W. O.; Welsh, W. A.; Libanati, C.; Ettlinger, M.; Kerner, D.; Maier, M.; Meon, W.; Schmoll, R.; Gies, H.; Schiffmann, D. In Ullmann’s Encyclopedia of Industrial Chemistry; Wiley-VCH Verlag GmbH & Co. KGaA, 2000. (12) Steenland, K.; Ward, E. CA. Cancer J. Clin. 64, 63–69. (13) Mckay, R. T. Concepts Magn. Reson. Part A Bridg. Educ. Res. 2011, 38 A, 197–220. (14) Carr, H. Y.; Purcell, E. M. Phys. Rev. 1954, 94, 630–638. (15) Assfalg, M.; Bertini, I.; Colangiuli, D.; Luchinat, C.; Schäfer, H.; Schütz, B.; Spraul, M. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 1420–1424. (16) Ghini, V.; Saccenti, E.; Tenori, L.; Assfalg, M.; Luchinat, C. J. Proteome Res. 2015, 14, 2951–2962. (17) Takis, P. G.; Oraiopoulou, M.-E.; Konidaris, C.; Troganis, A. N. Food Funct. 2016, 7, 4104–4115. (18) Worley, B.; Powers, R. Curr. Metabolomics 2012, 1, 92– 107. (19) Viel, S.; Ziarelli, F.; Caldarelli, S. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 9696–9698. (20) Caldarelli, S. Magn. Reson. Chem. 2007, 45, 48–55. (21) Lucena Alcalde, G.; Anderson, N.; Day, I. J. Magn. Reson. Chem. 2016, DOI: 10.1002/mrc.4464. (22) Pemberton, C.; Hoffman, R.; Aserin, A.; Garti, N. J. Magn. Reson. 2011, 208, 262–269. (23) González-García, T.; Margola, T.; Silvagni, A.; Mancin, F.; Rastrelli, F. Angew. Chem. Int. Ed. Engl. 2016, 55, 2733–2737. (24) Gitti, R.; Wild, C.; Tsiao, C.; Zimmer, K.; Glass, T. E.; Dorn, H. C. J. Am. Chem. Soc. 1988, 110, 2294–2296.
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Author Contributions The manuscript was written through contributions of all authors.
Notes The authors declare no competing financial interest.
ACKNOWLEDGEMENTS This work was partly supported by the European Commission funded H2020 projects PROPAG-AGEING (634821), PhenoMeNal (654241), FIRC (17491) and iNEXT (653706), and INSTRUCT, part of the EU-ESFRI through its Core Centre CERM, Italy. All anonymous volunteers are acknowledged for providing urine samples to accomplish the present study.
REFERENCES (1) Beckonert, O.; Keun, H. C.; Ebbels, T. M. D.; Bundy, J.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Nat. Protoc. 2007, 2, 2692–2703. (2) Emwas, A.-H. M.; Salek, R. M.; Griffin, J. L.; Merzaban, J. Metabolomics 2013, 9, 1048–1072. (3) DeFeo, E. M.; Wu, C.-L.; McDougal, W. S.; Cheng, L. L. Nat Rev Urol 2011, 8, 301–311. (4) Gallo, V.; Intini, N.; Mastrorilli, P.; Latronico, M.; Scapicchio, P.; Triggiani, M.; Bevilacqua, V.; Fanizzi, P.; Acquotti, D.; Airoldi, C.; Arnesano, F.; Assfalg, M.; Benevelli, F.; Bertelli, D.; Cagliani, L. R.; Casadei, L.; Cesare Marincola, F.; Colafemmina, G.; Consonni, R.; Cosentino, C.; Davalli, S.; De Pascali, S. A.; D’Aiuto, V.; Faccini, A.; Gobetto, R.; Lamanna, R.; Liguori, F.; Longobardi, F.; Mallamace, D.; Mazzei, P.; Menegazzo, I.; Milone, S.; Mucci, A.; Napoli, C.; Pertinhez, T.; Rizzuti, A.; Rocchigiani, L.; Schievano, E.; Sciubba, F.; Sobolev, A.; Tenori, L.; Valerio, M. Anal. Chem. 2015, 87, 6709–6717. (5) Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. J. Biomol. NMR 2011, 49, 231–243. (6) Emwas, A.-H.; Roy, R.; McKay, R. T.; Ryan, D.; Brennan, L.; Tenori, L.; Luchinat, C.; Gao, X.; Zeri, A. C.; Gowda, G. A. N.; Raftery, D.; Steinbeck, C.; Salek, R. M.; Wishart, D. S. J. Proteome Res. 2016, 15, 360–373.
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Figure 1. The final mixture of fumed silica gel particles and urine with the ratio 1(mg):18-20(µl), respectively, inside a 50 ml capacity falcon depicted (A) vertically and (B) horizontally placed. In the horizontal position, the 3 different zones from where 3 different NMR samples were prepared to test the homogeneity of the mixture are highlighted. We found the optimal ratio to 42x21mm (300 x 300 DPI)
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Figure 2. (A) Example of the ideal silica:urine ratio (s:u) samples NMR spectra; HRMAS and solution 1D NOESY 1H NMR human urine profiles (spectral region: 6.85–9.15 ppm), are compared, indicating that all resonances are present in the HRMAS experiments. The rest NMR spectral regions comparison are reported in Figure S2. (B) Disappearance of 3-methyl-L-histidine resonances at 1(mg):15(µl) s:u. is fully comparable with the s 42x13mm (300 x 300 DPI)
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Figure 3. The OPLS-DA statistical analysis score and first latent variables (colored according to the most weighted variables-metabolites, indicated by asterisks) plots of the 4 subjects classified urine 1H NMR profiles by (A), (C) solution and (B), (D) HRMAS NMR spectra. Each group consists of 3 NMR spectra from three urine samples of each subject, collected for 3 days. Both score and latent variables plots clearly demonstrate the same statistical and classification results, with almost the same classification crossvalidated accuracy (Table S1). The produced score plots 96x82mm (300 x 300 DPI)
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Figure 4. The common changes detected in the urine 1H NMR profile of 3 subjects NMR samples (1H NMR and HRMAS 1H NMR for solution and gel urine, respectively) during zero (0 h) to 24 hours (24 h), stored at 277 Κ (domestic fridge temperature). Arrows (↑, ↓) in-dicate the increase or decrease of metabolites concentration. For (A) we observe shifting (∆δ) of glutamine and citrate resonances, and succinate concentration increase; for (B) there is a clear perturbation of a very crowded region where several resonances are observed (sugars, nucleot(s)ides, etc.); for (C) the acetate concentration increases. HRMAS spectra show no differences in the concentrations of urine metabolites, and few NMR signals exhibit insignificant or no changes as compared to the solution NMR spectra. These changes include alterati 43x11mm (300 x 300 DPI)
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Graphical Abstract 47x26mm (300 x 300 DPI)
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