Supporting information
1
H NMR Metabolomics Study of Metastatic
Melanoma in C57BL/6J Mouse Spleen
Xuan Wang1, 2, Mary Hu1, Ju Feng1, Maili Liu2, Jian Zhi Hu1*
(1) Pacific Northwest National Laboratory, Richland, WA 99352, USA
(2) Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR
China.
* To whom correspondence should be addressed:
Jian Zhi Hu; Email: Jianzhi.Hu@pnnl.gov; Phone: (509) 371-6544; Fax: (509) 371-6546
1
Fig. S1
Light microscopy images (20X) of spleen tissue from the tumor cell treated (left) and the control (right) mice.
Melanocarcinoma masses were characterized by the abnormal size, shape and morphology of the nuclei
Fig. S2
PCA scores plots of spleen tissue extracts from the control (blue dots) and tumor (green dots) groups: (a), data
derived from binning results of 1H NMR spectra of hydrophilic extracts; (b), metabolites concentrations obtained by
spectral deconvolution of hydrophilic extracts; (c), data derived from binning results of 1H NMR spectra of
hydrophobic extracts
2
Table S1
Solvents-Tissue ratio for extraction
MeOH
H2O
CHCl3
CHCl3
H2O
(ml)
(μl)
(ml)
(ml)
(ml)
1g
4
850
2
2
2
0.5 g
2
425
1
1
1
0.25 g
1
213
0.5
0.5
0.5
125 mg
0.5
106
0.25
0.25
0.25
62.5 mg
0.25
53
0.125
0.125
0.125
31.3 mg
0.25
53
0.125
0.125
0.125
10.0 mg
0.25
53
0.125
0.125
0.125
Tissue
Table S2
Correlation coefficients of all metabolites used for OPLS analysis
Key
Metabolites
Correlation coefficients
1
Isoleucine
0.563
2
Alloisoleucine
0.370
3
Leucine
0.736
4
2-Aminobutyrate
0.670
5
Valine
0.536
6
Isobutyrate
0.579
7
3-Hydroxyisobutyrate
0.197
8
Ethanol
0.065
9
3-Hydroxybutyrate
-0.274
10
Fucose
0.154
11
Lactate
0.404
12
Threonine
0.643
13
Lysine
0.604
3
14
Alanine
0.817
15
Arginine
0.233
16
Thymidine
0.832
17
Acetate
0.122
18
dTTP
0.311
19
Glutamate
-0.911
20
Glutamine
0.282
21
Methionine
0.514
22
Glutathione
0.743
23
dCTP
0.874
24
Malate
0.897
25
2-Oxoglutarate
0.564
26
Isocitrate
-0.504
27
2'-Deoxyguanosine
0.854
28
Citrate
0.617
29
Aspartate
-0.870
30
Asparagine
0.771
31
Trimethylamine
0.679
32
Tyramine
0.323
33
Histamine
0.936
34
Creatine phosphate
0.596
35
Creatinine
-0.554
36
Creatine
-0.581
37
Tyrosine
0.462
38
Phenylalanine
0.616
39
Histidine
0.585
40
Ethanolamine
0.275
41
Choline
0.430
4
42
O-Phosphocholine
0.248
43
O-Phosphoethanolamine
-0.967
44
sn-Glycero-3-phosphocholine
-0.020
45
π-Methylhistidine
0.773
46
Trimethylamine N-oxide
0.185
47
Glucose
0.168
48
Taurine
-0.892
49
Betaine
0.498
50
myo-Inositol
-0.076
51
Tryptophan
0.501
53
UDP-glucuronate
0.132
54
Glycine
0.772
55
Glycerol
-0.252
56
UDP-galactose
0.149
57
Uridine
0.232
58
Cytidine
-0.240
59
Adenosine
0.383
60
Inosine
-0.186
61
Serine
0.606
62
AMP
0.775
63
ATP
-0.936
64
ADP
-0.107
65
GTP
0.924
66
Uracil
0.621
67
Fumarate
0.611
68
Benzoate
-0.138
69
Niacinamide
-0.925
70
Xanthine
0.931
5
71
Hypoxanthine
0.012
72
Oxypurinol
-0.046
73
Formate
-0.433
*
Unsigned (δ7.68)
0.562
Broadline filtering strategy for spectral deconvolution
Currently, there are no commonly accepted methods for treating the broadline features in a high resolution 1H
NMR metabolite spectrum. Many researchers use baseline correction methods to filter out the broadline features
before performing spectral deconvolution of metabolites that have narrow line widths. The shortcoming of baseline
correction is that it heavily depends on the skill of the individual who conducts the fitting and the method is not
reproducible. This will affect the quantitation of the metabolites, in particular in cases where metabolites with low
intensity spectral peaks are of high importance. To better fit the spectra, in this work a new strategy is proposed,
where broad peaks corresponding to lipoprotein signals and compounds exhibiting slow reorientation such as high
density lipoprotein (HDL) and low density lipoprotein (LDL), are first built in the Compound Builder module of
Chenomx using the peak-based method introduced in the software tutorial. The compound signatures for the HDL
and LDL are based on published literatures (Coen et al. 2003; Lindon et al. 1999; Nicholson et al. 1995). These
customized broad peaks are then added to the Library Manager module of Chenomx as new compounds so that they
can be used to construct the broaline features associated with a high resolution liquid state 1H NMR metabolite
spectrum. Firstly, the broadline feature in a selected spectrum from one sample type of one tissue type is
constructed by utilizing a group of customized spectral peaks and by adjusting both the intensities and the linewidths
of the customized spectral peaks so that the overall envelop fits the envelop of the broadline features in the
experimental spectrum. Once the broaline feature spectrum associated with a particular sample type and tissue type
is built, it can be effectively utilized to fit other spectra of the tissue type in a series of experiments with only minor
adjusting of the intensities of the customized peaks. This new strategy in addressing the broadline features takes the
advantages of Chenomx where optimization of peak positions and intensities is realized using the Compound
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Builder module of Chenomx, resulting in high flexibility and reproducibility. The method is demonstrated in Figure
S3, where a typical 1H NMR spectrum of hydrophilic extracts from δ 0.5 to δ 1.15 is shown. Figure S3 (a) shows
individual broadline features (blue) that have been constructed using the Compound Builder module of Chenomx in
the corresponding spectral region. Figure S3 (b) shows the best overall fitting quality, demonstrating that the
experimental spectrum (black) is well deconvoluted as is evident by the difference spectrum (green). An overfit will
show a negative residual peak (green) while an underfit will leave a positive residual peak in the difference spectrum
(green). The red line, i.e., the summed spectrum, displays the combined shape of all the profiled metabolites.
Deconvolution of the other spectral regions is conducted using the same strategy demonstrated in Figure S3 and the
overall fitting quality of an entire experimental spectrum is illustrated in Figure S4. As shown in the figure, the
original spectrum is well fitted except for the unfitted residual water peak. Once the first overall model for the
broadline feature spectrum is constructed and saved in the Compound Builder module of Chenomx, all the
remaining 9 spectra from both the control and the tumor groups can be easily fit by calling out the model and by
optimizing the intensities of the individual broad peaks. In this way, all the ten spectra of hydrophilic extracts from
both the control and the tumor groups are fitted with consistent strategies for handling broadline features and with
reproducible fitting results.
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Fig. S3
Illustration of the deconvolution strategy: (a), individual broadline features (blue) in the corresponding spectral
region constructed using the Compound Builder module of Chenomx: 1, albumin; 2, HDL-CH3; 3, VLDL/LDLCH3; 4, lipids-CH3CH2; 5, a broadline feature; (b), the overall fitting quality of the spectral region: experimental
spectrum (black), best fitted spectrum (red), fitting error/difference spectrum (green)
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Fig. S4
A representative 1H NMR Spectrum of hydrophilic extracts from the tumor group. The bottom trace spectrum is the
entire spectrum ranged from -0.5 ppm to 9.5 ppm accompanied by the upper three traces of expanded regions giving
the detailed fitting results
9
Reference
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Lindon, J. C., Nicholson, J. K., & Everett, J. R. (1999). NMR spectroscopy of biofluids. Annual Reports on Nmr
Spectroscopy, Vol 38, 38, 1-88.
Nicholson, J. K., Foxall, P. J. D., Spraul, M., Farrant, R. D., & Lindon, J. C. (1995). 750-Mhz H-1 and H-1-C-13
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