Metabolic Profiling of Human Lung Cancer Tissue by 1H High Resolution Magic Angle Spinning (HRMAS) NMR Spectroscopy Cla´udia M. Rocha,† Anto ´ nio S. Barros,‡ Ana M. Gil,† Brian J. Goodfellow,†,§ Eberhard Humpfer,| | Manfred Spraul, Isabel M. Carreira,§,⊥ Joana B. Melo,⊥ Joa˜o Bernardo,§,# Ana Gomes,§,# Vitor Sousa,§,# Lina Carvalho,§,# and Iola F. Duarte*,†,§ CICECO, Department of Chemistry, University of Aveiro, Campus Universita´rio de Santiago, 3810-193 Aveiro, Portugal, QOPNA, Department of Chemistry, University of Aveiro, Campus Universita´rio de Santiago, 3810-193 Aveiro, Portugal, CIMAGO, Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal, Bruker BioSpin GmbH, Silberstreifen, D76287 Rheinstetten, Germany, Cytogenetics Laboratory and CNC, Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal, and Institute of Pathological Anatomy, Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal Received July 23, 2009
This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by 1H HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D 1H spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer. Keywords: lung cancer • NMR spectroscopy • HRMAS • metabolic composition • multivariate analysis
Introduction Lung cancer is one of the most frequently diagnosed cancers, being also the most common cause of cancer death worldwide because of its high case fatality. Although postsurgical mortality has been declining since the 1950s, the 5-year survival rates remain low, being reported as 15% in the U.S.A. and about 10% in Europe.1 One of the main reasons for this is the advanced stage at which lung cancer is usually diagnosed, as there are no general screening methods and early stage tumors often cause no signs or symptoms. Due to late detection, only about 15% of patients have localized tumors that are amenable to be cured by surgery, resulting in poor prognosis for the vast majority of cases. Diagnosis methods include computed to* To whom correspondence should be addressed. Dr. Iola F. Duarte, e-mail:
[email protected], tel: +351 234401424, fax: +351 234401470. † CICECO, Department of Chemistry, University of Aveiro. ‡ QOPNA, Department of Chemistry, University of Aveiro. § CIMAGO, Faculty of Medicine, University of Coimbra. | Bruker BioSpin GmbH. ⊥ Cytogenetics Laboratory and CNC, Faculty of Medicine, University of Coimbra. # Institute of Pathological Anatomy, Faculty of Medicine, University of Coimbra. 10.1021/pr9006574
2010 American Chemical Society
mography (CT), magnetic resonance imaging (MRI), and, more recently positron emission tomography (PET) to detect and stage lung nodules.2 Definitive diagnosis relies on cytology and the histopathological study of tissue biopsies, according to the 2004 World Health Organisation (WHO) Classification of Tumors.3 However, being based on the evaluation of morphological changes, histology gives no information about the altered metabolism in cancer cells, the analysis of which may potentially aid in the more accurate staging of the disease. Also, if detected in preneoplastic lesions for which histopathology is often inconclusive, the altered metabolic signatures may potentially be useful for the early detection of lung cancer. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for analyzing the chemical composition of biological tissues and providing insight into the tumors metabolic profiles. NMR of tissue extracts has been employed to study several types of cancer, such as brain,4,5 breast,6,7 colon,8 and lung cancer.9-11 However, besides being selective for either lipophilic or hydrophilic compounds, extraction methods may cause the loss or chemical modification of cellular components, seriously limiting the information about the real metabolic events taking place in vivo. Journal of Proteome Research 2010, 9, 319–332 319 Published on Web 11/13/2009
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Table 1. Pathology and Sampling case no.
pathology diagnosis
TNM stage
samples analyzed by NMR
neoplastic cells in mirror sections (%)
gender
age
1 2 3 4 5 6 7 8 9 10 11c 12c
Inflammatory myofibroblastic tumor Typical carcinoid Adenocarcinoma - mixed type Adenocarcinoma - mixed type Adenocarcinoma - mixed type Adenocarcinoma - mixed type Pleomorphic carcinoma Lymphoepithelioma-like carcinoma Squamous cell carcinoma Adenocarcinoma - mixed type Typical carcinoid Adenosquamous carcinoma
pT1N0Mx pT2N0Mx pT2N0Mx pT2N1Mx pT2N0Mx pT1N0Mx pT1NxMx pT1N1Mx pT2N1Mx pT1N0Mx pT2N0Mx
N1, T1 N2, T2a, T2ba N3, T3 N4, T4 N5a, N5b, T5a, T5ba T6b N7a, N7ba, T7 N8, T8 N9, T9 N10, T10 N11, T11 N12, T12
>50 >50 >50 n.a. >50 >50 e50 e50 (with necrosis) e50 (with necrosis) >50 n.a. n.a.
M M F F M M F M M M M F
32 66 57 75 71 81 58 79 62 52 36 43
a Samples a and b correspond to different fragments of the same tissue sample. b Sample N6 not available. c Tumor (T) and noninvolved (N) tissue samples from cases 11 and 12 were analyzed for assignment and stability evaluation purposes only and were not included in the multivariate analysis by PCA and HCA; n.a. histology control not available.
High-resolution magic angle spinning (HRMAS) NMR spectroscopy, on the other hand, enables the direct characterization of intact tissues, allowing the simultaneous detection of both lipids and small metabolites with a resolution comparable to that of liquid state NMR. Providing a closer and more realistic insight into their metabolic profiles, HRMAS has been increasingly applied to analyze intact cells and tissues, with particular emphasis on cancer studies, as recently reviewed.12,13 Indeed, this technique has proven to be very powerful for corroborating and complementing noninvasive in vivo magnetic resonance spectroscopy (MRS) studies by aiding assignment and quantification of the in vivo data, generally characterized by poor resolution and signal-to-noise at the clinical magnetic field strengths.14-18 Moreover, much research work has focused on the metabolic differentiation between tumor and healthy tissues, aiming at finding possible biomarkers of the presence and/or grade of different cancers such as breast,19,20 brain,21,22 prostate,23-25 cervical,26-28 colorectal,29 hepatic,30 renal,31,32 and gastric cancer.33 Multivariate statistical methods have been increasingly applied to the spectral data to map the variations in whole profiles, rather than individual metabolites, and to find correlations between metabolic and histological or clinical data, thus aiming at improving adjunct diagnosis and/or prognosis. For instance, the metabolic phenotype of breast cancer tissue has shown to be potentially useful in the prediction of histological grade, hormone status and lymphatic spread in breast cancer patients,20 and the metabolic profile of oligodendrogliomas showed good correlation to malignancy grading and patient prognosis.21 In regards to lung cancer, the work reported to date is mostly based on the analysis of tissue extracts.9-11 Recently, the direct HRMAS analysis of lung cancer tissue from squamous cell carcinoma and adenocarcinoma of the lung has been reported.34 In this work, paired tissue and serum samples from lung cancer patients were analyzed to differentiate between the two cancer types. The present study, on the other hand, is focused on the metabolic differentiation between tumor and noninvolved adjacent lung tissues, which, to our knowledge, has not been previously reported. Moreover, by carrying out detailed spectral assignment of HRMAS data, this study provides a thorough characterization of the metabolic composition of human lung tissue, which we believe constitutes novel and useful information. Paired samples from 12 lung tumors are included in this study. Of these, 2 sample cases were analyzed for assignment and stability evaluation purposes only, while 320
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the tissue metabolic profiles of the other 10 tumors were studied by chemometric methods, namely Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The aim of this approach is to explore the potential of NMRbased metabonomics for characterizing lung cancer metabolic phenotype, from which new biochemical insights as well as markers of malignancy with potential diagnostic value may arise. At this stage, given the limited number of samples from each subgroup of patients, only the general metabolic profile of lung cancer compared to nonmalignant tissue is investigated. A future step in this work, when more samples of each histological type are available, will entail the comparison of the metabolic profiles of different tumor types aiming at identifying distinctive features in their metabolism that may be used to improve the diagnostic/prognostic accuracy of lung cancer types.
Experimental Section Lung Tumors. In total, 12 cases of primary lung cancer (concerning 8 males and 4 females, age range 32-81 years, mean age 59 ( 16 years) were included in this study. The limited number of cases studied was mainly due to the relatively low number of patients treatable by surgery and to the reduced size of some of the excised tumors, which precluded the collection of tissue samples for research. Final diagnoses and TNM staging were established by the histopathological evaluation of surgical specimens. The malignant epithelial tumors characterized were 5 adenocarcinomas mixedtype, 2 typical carcinoids, 1 squamous cell carcinoma, 1 lymphoepithelioma-like carcinoma, 1 pleomorphic carcinoma, and 1 adenosquamous carcinoma; an inflammatory myofibroblastic tumor, classified as a mesenchimal tumor according to the 2004 WHO Classification of Tumours,3 was also included (Table 1). In all cases, the surrounding parenchyma tissue presented histological characteristics of “smoking lung”. Of the 12 cases studied, 10 were considered for the metabonomics approach involving the multivariate statistics of their spectral profiles, whereas the samples of the other 2 cases (11 and 12 in Table 1) were analyzed for assignment and stability evaluation purposes only. Tissue Handling. Lung tumor and noninvolved adjacent parenchyma (control) samples were retrieved from surgical specimens within a maximum of 30 min after surgery, immediately snap-frozen in liquid nitrogen, and stored at -80 °C until NMR analysis. Sampling for diagnosis was routinely
Metabolic Profiling of Lung Cancer by 1H HRMAS NMR performed, and only redundant tissue was included in this study. Mirror sections of the samples stored for NMR were histologically observed, and the percentage of neoplastic cells was determined to be higher or lower than 50% (Table 1). Each sample stored for NMR had approximately 5 mm3, and in cases where larger samples could be collected, two fragments (a and b) were analyzed by NMR, to assess the influence of tissue heterogeneity on the spectral profiles. Sample Preparation. The frozen tissue samples were washed with a few drops of D2O saline (0.9%), and about 40 mg of thawed cold tissue were packed into 50 µL HRMAS rotors with top inserts conferring the samples a cylindrical geometry. Ten microliters of D2O saline containing 0.25% 3-(trimethylsilyl)propionate sodium salt (TSP)-d4 were also added to provide a signal for lock (D2O) and for shimming (TSP), the process of resolution optimization carried out for each sample before starting spectral acquisition. The total rotor contents weighed on average 52 ( 6 mg. NMR Measurements. 1D NMR spectra were acquired on a Bruker Avance DRX-500 spectrometer operating at 500.13 MHz for 1H observation, at 277 K, using a 4 mm HRMAS probe, in which the rotor containing the sample was spun at the magic angle (54.7° relative to the magnetic field) and a spinning rate of 4 kHz. For assessing lung tissue stability over time, standard 1 H NMR spectra with water suppression (“noesypr1d” in Bruker library, SW 6510 Hz, TD 32 K data points, relaxation delay 4 s, 256 scans) were consecutively acquired over 5 h for 2 fragments of the same tumor sample, one analyzed at 293 K and the other at 277 K. Moreover, to verify the reproducibility of the results, this sequential acquisition was repeated at 277 K for 2 other tumor samples. For analyzing paired samples of tumor and noninvolved adjacent tissue, 3 1D experiments were typically acquired for each sample: (a) standard 1H NMR spectrum with water suppression (“noesypr1d” in Bruker library, same parameters as described above), (b) T2-weighted (Carr-PurcellMeiboom-Gill, CPMG) 1H NMR spectrum to provide a clearer representation of low molecular weight metabolites (“cpmgpr” in Bruker library, total spin-spin relaxation 2nτ ) 90 ms), and (c) diffusion-weighted 1H NMR spectrum to select signals of bound or large molecules (“ledbpgp2s1dpr” in Bruker library, square gradients duration 2 ms, gradient strength 48.15 G cm-1, diffusion time 100 ms). All 1D spectra were processed with a line broadening of 0.3 (standard and T2-weighted) or 0.5 Hz (diffusion-weighted) and a zero filling factor of 2, manually phased and baseline corrected. The chemical shifts were referenced internally to the alanine signal at δ 1.48 (this peak was found to be more reliable than the TSP peak due to the sensitivity of the latter to interacting compounds). 2D homonuclear and heteronuclear spectra were registered for selected samples to aid spectral assignment on Bruker Avance DRX-500 and Bruker Avance II 600 spectrometers, operating respectively at 500.13 and 600.13 MHz for 1H observation. Typically, 1H-1H total correlation (TOCSY) spectra were acquired in the phase-sensitive mode using time proportional phase incrementation (TPPI), and the MLEV17 pulse sequence for the spin-lock. 2048 data points with 16 transients per increment and 256 increments were acquired with a spectral width of 7211.5 Hz in both dimensions. The relaxation delay between successive pulse cycles was 2 s and the mixing time of the MLEV spin lock was 30 ms. 1H-13C phase sensitive (echo/antiecho) heteronuclear single quantum correlation (HSQC) spectra were recorded with inverse detection and 13C decoupling during acquisition. A relaxation delay of 4 s was
research articles used between pulses and a refocusing delay equal to 1/41JC-H (1.72 ms) was employed. One-thousand twenty-four data points with 24 scans per increment and 200 increments were acquired with spectral widths of 12335.5 and 28673.7 Hz in the 1H and 13 C dimensions, respectively. For both TOCSY and HSQC spectra, zero filling to 1024 data points and forward linear prediction were used in f1 and multiplication by a shifted sinebell-squared apodization function was applied in both dimensions prior to FT and phasing. 2D homonuclear J-resolved spectra with water presaturation were measured by acquiring 16 K data points with 32 transients per each of 40 increments, using spectral widths of 10000.0 Hz in the f2 dimension and 78.1 Hz in the f1 (J-coupling) dimension. Prior to FT, the FIDs were weighted in both dimensions by a sine-bell function and zero-filled in the f1 dimension to 256 data points. The spectra were tilted by 45° to provide orthogonality of the chemical shift and coupling constant axes and subsequently symmetrized about the f1 axis. Spectral assignment was carried out with the support of Bruker BBiorefcode spectral database, as well as of other existing databases and specific compound standard solutions. Statistical and Quantitative Analysis. A total of 23 samples corresponding to 10 patients have been considered for chemometric and quantitative studies, as described in detail in Table 1. PCA has been applied to three different data matrices built from the standard 1D spectra: (a) δ 0.5 to 4.75 using all the intensity values, (b) δ 0.5 to 4.75 excluding the lactate signals (δ 1.315-1.370 and δ 4.080-4.160), and (c) δ 4.75 to 8.75 using all the intensity values in this region. The δ 0.5 to 4.75 regions of the T2-weighted and diffusion edited spectra have also been computed by PCA. Each spectrum was normalized by adjusting the total area to unity. Outlier assessment was performed by a robustified principal components method.35 The calculations were performed using a program codeveloped at the University of Aveiro and the “AgroParisTech”.36 HCA was applied to the same matrices considered for PCA of the standard 1D spectra. The R language package37 was used to perform agglomerative, hierarchical clustering. The metric used was the Euclidean distance and the agglomeration was based on the Ward minimum variance method. To evaluate the magnitude of variation of some compounds, selected signals in the standard 1D spectra were deconvoluted and integrated using the Amix-Viewer software (Bruker, version 3.9). Deconvolution using an automated variable Gaussian/ Lorentzian peak fitting routine based on the Marquardt algorithm was applied to the region of interest, and the resulting peak fits were improved by manually adjusting the peak picking until the error estimates associated to the intensity, height at half-width, and the Gaussian/Lorentzian proportion were minimized. In the case of less abundant metabolites with signals in the low field region (tyrosine, phenylalanine, and inosine/adenosine), deconvolution could not be employed due to low signal intensity, sometimes close to the noise level, and to the broad underground signal affecting the baseline of most spectra. Instead, the Amix underground removal tool using a 20 Hz filter followed by a baseline offset correction was applied and the metabolite peaks were subsequently integrated. Each area was normalized to the total spectral area (δ 0.5-8.8 excluding the water region δ 4.75-5.15) to enable the comparison between samples. The difference between the two tissue types (tumor and noninvolved) was considered significant when the p value, calculated by the t-Student test,37 was Journal of Proteome Research • Vol. 9, No. 1, 2010 321
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Figure 1. 1D 1H HRMAS NMR spectra (600 MHz, NS 256) of human lung tissue: (a) standard “noesy”, (b) T2-weighted, and (c) diffusionweighted spectra.
lower than 0.05. The boxplot representation was used to visualize the variation in the levels of integrated compounds in control and tumor samples.
Results and Discussion Metabolic Composition of Human Lung Tissue. The typical 1D 1H HRMAS NMR spectra of human lung tissue are shown in Figure 1. The standard “noesy” spectrum (Figure 1a) shows contributions from both sharp signals arising from mobile low molecular weight metabolites and broader signals arising mainly from lipids and oligopeptides, the overall result being a significant degree of spectral overlap and complexity. In the T2-weighted spectrum (Figure 1b), the resonances of fastrelaxing macromolecules are attenuated, and the narrow signals of low molecular weight metabolites are more clearly visible, although some of the signals of fatty acyl chain protons are still present indicating high lipid mobility. On the other hand, the diffusion-weighted experiment selects the signals of slowdiffusing macromolecules and/or metabolites of restricted mobility. The broad profile obtained (Figure 1c) comprises the signals of lipids, including phosphatidylcholine with a characteristic headgroup signal at δ 3.27, oligopeptides and also residual signals of lactate and some amino acids (e.g., alanine, taurine), which have not been totally attenuated at the gradient strength used. 322
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The assignment of signals to specific compounds was based on 2D experiments, namely 1H-1H TOCSY, 1H-13C HSQC, and J-resolved experiments (Figure 2). Table 2 shows the list of compounds identified in human lung tissue and their 1H and 13 C chemical shifts measured in 1D and 2D HRMAS spectra. The broad resonances dominating the high-field region (δ 0-3) arise mainly from lipids, as viewed by the characteristic spin systems of fatty acyl chains found in the TOCSY spectrum (Figure 2a) and confirmed by the 13C chemical shifts in the HSQC spectrum (Figure 2b). Oligopeptides are also thought to contribute to the broader profile due to the observation of several spin systems (identified in the TOCSY spectrum), with patterns only slightly shifted from those of free amino acids; in particular, and in agreement with published data,33 the broad signals centered at δ 4.1-4.4, which show correlations to signals in the high-field region, are assigned to R-CH protons of different amino acid residues bonded in a peptidic chain (Figure 2 and Table 2). Glutathione is unambiguously identified by TOCSY correlations between the R and β protons of composing amino acids and by their characteristic NH chemical shifts. Another broad signal is found in some samples at δ 3.71, probably arising from exogenous polyethylene glycol (PEG), which may originate from the anesthetic formulation administered to patients before surgery. In terms of low molecular weight metabolites, more than 20 amino acids are
Metabolic Profiling of Lung Cancer by 1H HRMAS NMR
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Figure 2. Expansions of 2D HRMAS NMR spectra (600 MHz) of human lung tissue: (a) 1H-1H TOCSY, (b) 1H-13C HSQC, and (c) J-resolved spectra. Some assignments are indicated according to Table 2.
identified, together with some organic acids, glucose, and inositols. Several ethanolamine and choline compounds are also detected, the J-resolved experiment being particularly useful in this respect, since the overlapping signals in the δ 3.2-3.3 region are clearly separated in the J-resolved spectrum (Figure 2c). In the low field region, besides the broad resonances probably arising from NH groups of aminoacids/ oligopeptides, signals of aromatic amino acids (tyrosine and phenylalanine) and nucleosides/nucleotides are identified. In total, over 50 compounds are identified in this work through the direct analysis of intact tissue, thus providing valuable information for interpreting variations in metabolic pathways, within the group of samples under study.
To assess lung tissue metabolic stability over the duration of the NMR experiments conducted in this work, sequential spectra have been acquired for different samples during 5 h at two different temperatures, 277 and 293 K. The scores scatter plot resulting from applying PCA to the spectra of 2 tumor samples (Figure 1a in Supporting Information) shows a dispersion along PC2 that reflects the changes over time. The main variations are highlighted in the PC2 loadings (Figure 1b in Supporting Information) and correspond to decreases in phosphocholine (PC) and glycerophosphocholine (GPC) and to increases in choline and amino acids such as glycine and alanine. The magnitude of these changes has been evaluated by spectral integration and the percentage of variation relative Journal of Proteome Research • Vol. 9, No. 1, 2010 323
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Table 2. Assignment of Resonances in the 500 and 600 MHz H HRMAS NMR Spectra of Human Lung Tumor compound
324
1 2
Acetate Alanine
3
Alanine (bonded)
4
β-Alanine
5
Arginine
6
Ascorbate
7
Asparagine
8
Aspartate
9
Aspartate (bonded)
10
Carnitine
11
Choline
12
Creatine
13
Ethanolamine
14
Fatty acyl chain peaks
15 16 17
Formate Fumarate R-Glucose
18
R-Glucose (1fX)
19
β-Glucose
20
Glutamate
21
Glutamate (bonded)
22
Glutamine
assignment
βCH3 βCH3 RCH βCH3 RCH βCH2 RCH2 γCH2 βCH2 δCH2 RCH CH2(OH) CH(OH) C1H βCH β’CH RCH βCH β′CH RCH βCH β′CH RCH γCH2 RCH2 N(CH3)3 CH2(NH) CH2(OH) CH3 CH2 CH2(OH) CH2(NH2) CH3 -(CH2)nCH2-CH2-CO CHdCH-CH2 CH2-CO CHdCH-CH2-CHdCH CHdCH CH CH C4H C2H C3H C6H C5H C1H C2H C1H C2H C4H C5H C3H C6H C6′H C1H βCH β′CH γCH2 RCH βCH β′CH γCH2 RCH βCH2 γCH2
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δ 1H ppm (multiplicitya)
1.93 (s) 1.48 (d) 3.78 1.41 (d) 4.31 2.56 (t) 3.18 (t) 1.69 1.92 3.24 (t) 3.78 3.75 4.03 4.53 2.87 (dd) 2.95 (dd) 4.01 (dd) 2.69 (dd) 2.82 (dd) 3.90 (dd) 2.69 (dd) 2.89 (dd) 4.27 (dd) 2.44 (m) 3.45 (m) 3.21 (s) 3.53 4.07 3.03 (s) 3.94 (s) 3.82 (t) 3.14 (t) 0.90/0.96 1.29 1.58 2.04/2.08 2.25 2.81 5.32 8.46 (s) 6.52 (s) 3.40 3.54 3.71 3.83 3.85 5.24 (d) 3.62 5.43 3.24 3.41 3.47 3.49 (t) 3.74 3.90 4.65 (d) 2.06 2.13 2.35 3.78 1.95 2.04 2.30 4.30 2.15 2.45
δ
13
C ppm
27.62 19.00 53.41 19.45
26.51 30.68 43.31 57.39 65.38 72.60 81.25 37.35 37.35 54.25 39.40 39.40 55.17
56.75 70.38 58.63 40.05 56.47 59.35 41.91 16.85, 25.48/21.36 25.20, 32.42, 34.55 25.02 27.43 36.43 28.31 131.06
72.48 74.10 75.58 63.33 74.85 94.98
77.09 72.48 78.64 78.24 63.98 63.98 98.78 29.87 29.87 36.16 57.38
29.52 33.65
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Metabolic Profiling of Lung Cancer by 1H HRMAS NMR Table 2. Continued compound
23
Glutathione
24
Glycerol
25
Glycerol (in lipids)
26 27
Glycerophosphoethanol amine (GPE) Glycerophosphocholine (GPC)
28 29
Glycine Histidine
30 31
β-Hydroxybutyrate Hypotaurine
32
Hypoxanthine
33
Inosine/ Adenosine
34
Isoleucine
35
Lactate
36
Leucine
37
Lysine
38
Lysine (bonded)
39
Methionine
40
myo-Inositol
assignment
RCH RCH Glu βCH2 Glu γCH Glu RCH2 Cys βCH2 Cys RCH2 Gly NH Cys NH Gly C1H2/C3H2 C2H C1H2/C3H2 C2H CH2(N) CH2(P) N(CH3)3 β′CH2(N) R′CH2(P) RCH2 C4H, ring C2H, ring γCH3 βCH2 RCH2 C2H C8H C5′,5′′H, ribose C4′H, ribose C3′H, ribose C2′H, ribose C1′H, ribose C2H, ring C8H, ring δCH3 β′CH3 γCH γ′CH βCH RCH βCH3 RCH δCH3 δ′CH3 γCH βCH2 RCH γCH2 δCH2 βCH2 εCH2 RCH γCH2 δCH2 βCH2 εCH2 RCH CH3 βCH β′CH γCH2 RCH C5H C1H, C3H C4H, C6H C2H
δ 1H ppm (multiplicitya)
3.79 3.80 2.18 2.56 4.58 2.97 8.56 8.37 3.56, 3.65 (dd) 3.78 (dd) 4.09, 4.30 5.23 3.30 4.11 3.23 (s) 3.71 4.33 3.56 (s) 7.28 (s) 8.08 (s) 1.20 (d) 2.65 (t) 3.35 (t) 8.18 (s) 8.21 (s) 3.86 4.28 4.44 4.78 (t) 6.10 (d) 8.23 (s) 8.36 (s) 0.94 (t) 1.01 (d) 1.26 1.47 1.99 3.65 1.33 4.12 0.96 (d) 0.97 (d) 1.70 1.72 3.74 (t) 1.47 1.73 1.92 3.03 (t) 3.76 (t) 1.56 1.75 1.83 3.16 4.17 2.13 2.14 2.21 2.64 (t) 3.87 (dd) 3.28 (t) 3.54 3.62 4.06
δ
13
C ppm
57.38
65.29 75.12
56.75 68.80 62.29 44.32
21.49
13.97 17.45 27.06 27.06 38.81 62.40 20.89 71.41 23.64 24.93 27.05 42.65 56.10 24.28 29.20 32.75 42.00 57.34 33.37
16.75 32.54 32.54 31.51 57.21 77.16 74.10 75.30 75.02
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Table 2. Continued compound
a
41
Phenylalanine
42
Phosphocholine (PC)
43
Phosphoethanolamine (PE)
44
Polyamines
45
Proline
46
Proline (bonded)
47 48
scyllo-Inositol Serine
49
Taurine
50
Threonine
51
Threonine (bonded)
52
Tyrosine
53
Uracil
54
Uridine
55
UDP/UTP
56
Valine
57
Valine (bonded)
assignment
βCH β′CH RCH C2H, C6H, ring C4H, ring C3H, C5H, ring N(CH3)3 N-CH2 PO3-CH2 N-CH2 PO3-CH2
γCH2 βCH β′CH δCH δ′CH RCH γCH2 βCH β′CH δCH δ′CH RCH CH RCH βCH β′CH S-CH2 N-CH2 γCH3 RCH βCH γCH3 RCH βCH βCH β′CH RCH C3H, C5H, ring C2H, C6H, ring C5H, ring C6H, ring C4′H, ribose C3′H, ribose C2′H, ribose C1′H, ribose/C5H, ring C6H, ring C1′H, ribose C6H, ring γCH3 γ′CH3 βCH RCH γCH3 βCH RCH
3.13 3.27 3.99 7.32 7.37 7.42 3.22 (s) 3.62 4.19 3.23 4.01 1.79 2.11 3.03 3.14 2.01 2.07 2.35 3.34 3.42 4.13 1.91 2.03 2.32 3.69 3.83 4.44 3.35 (s) 3.85 3.95 (dd) 3.99 (dd) 3.26 (t) 3.43 (t) 1.34 (d) 3.59 (d) 4.26 1.22 (d) 4.26 3.20 (dd) 3.05 (dd) 3.94 6.89 (d) 7.18 (d) 5.80 (d) 7.53 (d) 4.13 4.23 4.35 5.89/5.92 (d) 7.89 (d) 5.97 7.97 0.99 (d) 1.04 (d) 2.28 3.62 (d) 0.94
δ
13
C ppm
39.40 39.40 58.98 132.36 130.42 132.09 56.75 69.09 60.92 57.39 63.89
26.69 31.89 31.89 48.95 48.95 64.10
34.39 65.70
76.70 59.34 63.25 63.25 50.34 38.20 19.81 63.25 68.72
59.26 118.66 133.75
19.45 20.75 31.99 63.34
4.11
(s) singlet, (d) doublet, (dd) double doublet, (t) triplet, (q) quartet.
to the first spectrum (time 0) calculated for several compounds, as shown in Figure 2 in Supporting Information. Both GPC and PC decrease steadily by 10-11% at 293 K but remain ap326
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proximately constant at 277 K. Similarly, choline shows a residual variation at low temperature but increases by 15% after 5 h at room temperature. On the other hand, the variation of
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research articles
Figure 3. Average 1D 1H HRMAS standard spectra (500 MHz) of (a) the noninvolved tissues (n ) 11, cases 1-10, Table 1) and (b) the tumor samples (n ) 12, cases 1-10, Table 1).
some amino acids is considerably larger at both temperatures, with glycine, alanine, valine, and isoleucine showing increases of 25-37% at 293 K and 15-23% at 277 K. The results obtained at 277 K, the temperature chosen for the measurements in this work, were found to be reproducible for 2 other lung tissue samples, both in terms of trend and magnitude. Overall, for the 3 samples at 277 K, the variations of choline, PC, and GPC are residual (
