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An optimized buffer system for NMR-based urinary metabonomics witheffective pH control, chemical shift consistency and dilution minimization†

Chaoni Xiao,ab Fuhua Hao,a Xiaorong Qin,c Yulan Wang*a and Huiru Tang*a

Received 23rd October 2008, Accepted 26th January 2009

First published as an Advance Article on the web 23rd February 2009

DOI: 10.1039/b818802e

NMR-based metabonomics has been widely employed to understand the stressor-induced

perturbations to mammalian metabolism. However, inter-sample chemical shift variations for

metabolites remain an outstanding problem for effective data mining. In this work, we systematically

investigated the effects of pH and ionic strength on the chemical shifts for a mixture of 9 urinary

metabolites. We found that the chemical shifts were decreased with the rise of pH but increased with the

increase of ionic strength, which probably resulted from the pH- and ionic strength-induced alteration

to the ionization equilibrium for the function groups. We also found that the chemical shift variations

for most metabolites were reduced to less than 0.004 ppm when the pH was 7.1–7.7 and the salt

concentration was less than 0.15 M. Based on subsequent optimization to minimize chemical shift

variation, sample dilution and maximize the signal-to-noise ratio, we proposed a new buffer system

consisting of K2HPO4 and NaH2PO4 (pH 7.4, 1.5 M) with buffer–urine volume ratio of 1 : 10 for

human urinary metabonomic studies; we suggest that the chemical shifts for the proton signals of

citrate and aromatic signals of histidine be corrected prior to multivariate data analysis especially when

high resolution data were employed. Based on these, an optimized sample preparation method has been

developed for NMR-based urinary metabonomic studies.

Introduction

NMR-based metabonomics has been widely employed as

a powerful tool to probe the systemic metabolic responses to

perturbations resulting from chemical toxicity,1–4 diseases,5–7

stresses,8 aging9 and nutritional interventions.10–13 However,

inter-sample chemical shift variations can lead to spurious clus-

tering and false interpretation for multivariate data analysis

especially in the cases of urinalysis. Traditionally, the 1H NMR

spectra were bucketed into bins with 0.04 ppm to increase

tolerance for chemical shift variation14–16 and reduce the

computation load. But such large buckets will inevitably

combine the different metabolite signals with the opposite

changes in the same bin, which may obscure the metabolic

changes and overshadow the contributions of low-concentration

metabolites. When smaller buckets17–19 (e.g. 0.004 ppm) or full

resolution NMR data20–22 were employed to maximize informa-

tion output, even small chemical shift variations may cause

noticeable problems in data analysis. Although some post-

acquisition data processing methods for peak alignments23–25

have been proposed during recent years which can reduce

aState Key Laboratory of Magnetic Resonance and Atomic and MolecularPhysics, Wuhan Centre for Magnetic Resonance, Wuhan Institute ofPhysics and Mathematics, The Chinese Academy of Sciences, Wuhan430071, P. R. China. E-mail: [email protected]; [email protected]; Fax: +86-(0)27-87199291; Tel: +86(0)27-87198430bGraduate School of the Chinese Academy of Sciences, Beijing 100049,P.R. ChinacCollege of Chemical Engineering and Technology, Wuhan University ofScience and Technology, Wuhan 430081, P.R. China

† Electronic supplementary information (ESI) available: some 1H NMRspectra for metabolites. See DOI: 10.1039/b818802e

916 | Analyst, 2009, 134, 916–925

chemical shift inconsistency to some extent, these methods are

not effective for the severely overlapped signals and unknown

peaks in 1H NMR spectra. The best strategy is to eliminate such

inconsistency from the sources (e.g. during sample preparation

and data acquisition). Therefore, it is essential to understand

how the factors such as pH and salt concentration affect the

chemical shifts so as to minimize the induced chemical shift

inconsistency.

The pH has a strong impact on the chemical shifts of urinary

metabolites with ionizable groups. Normally, the pH values for

human urine samples26,27 vary from 5.5 to 6.5 and may extend to

4.6–8.0 depending on dietary intakes, xenobiotic treatments and

health status. Urine samples are often composed of carboxylic

acids such as citrate and hippurate, organic amines such as

dimethylamine (DMA) and trimethylamine (TMA), and amino

acids such as glycine, taurine and histidine.28,29 The chemical

shifts of these metabolites are dependent on the sample pH due to

ionization of carboxyl or amino groups, and such dependence

was described previously by the Henderson–Hasselbalch equa-

tion.30 Although the proton chemical shift variation has already

been investigated in previous studies for some urine metabo-

lites29,31,32 including citrate, hippurate, creatinine and glycine, the

studied pH range was limited and organic bases such as DMA

and TMA were not considered.

In addition, urine contains a variable amount of ionic

species27,33 such as Na+ (90–240 mmol/L), K+ (34–68 mmol/L),

Ca2+ and Mg2+ (1–10 mmol/L), which may also affect the

metabolite chemical shifts due to both ionic strength and bind-

ings. For example, the inter-sample chemical shift variation

for citrate may result from the difference of the divalent

cation concentrations34 apart from pH variation. A recent study

This journal is ª The Royal Society of Chemistry 2009

showed35 that the addition of about 4.2 mM EDTA to urine

reduced chemical shift changes for some metabolites such as

citrate and alanine, resulting from the stronger bindings of

EDTA with Ca2+ and Mg2+. However, signals of EDTA and its

complexes with Ca2+ and Mg2+ (about 6 peaks) will be intro-

duced, which may overlap with other metabolite signals in NMR

spectra; EDTA will not easily remove the chemical shift changes

induced by other ions such as Na+ and K+ (i.e. ionic strength).

Therefore, some systematic investigations remain to be per-

formed to understand the effects of ionic strength on urinary

signal variability.

In order to reduce the chemical shift variation for the

metabonomics studies, the pH consistency was controlled by

adjusting the sample pH to a fixed value or using buffer. For

example, hydrochloric acid was used to adjust the urine pH to

about 2.5 in some studies of inborn error of metabolism.36,37

Although this method was effective to reduce the inter-sample

pH variation, it was labor-intensive and not suited for high

throughput studies especially when hundreds of samples were

involved. In addition, such pH is close to the pKa values of most

amino acids (e.g. pKa z 1.5–2.6) and their proton chemical shifts

will be sensitive to any minor pH changes; such strong acidity can

also cause the degradation of some metabolites (e.g. hydrolysis).

In the other method, NaH2PO4/Na2HPO4 buffer38,39 was

employed to stabilize the urine pH to about 7.4 though a few

studies40–42 were carried out at pH 5.0, 7.1 and 7.3. However, high

concentration buffer cannot be employed, due to low water

solubility of Na2HPO4$12H2O, limiting the buffer capacity. In

addition, Na2HPO4$12H2O often precipitates even at moderate

buffer concentration (e.g. 0.2 M) during low temperature

storage, causing the changes of buffer composition and capacity.

Recently, attempts have been made to optimize buffer systems

for metabonomic urinalysis: a final buffer concentration of 0.3 M

was recommended for normal human urine and 1 M for

concentrated samples.26 Although this method will undoubtedly

bring the sample pH to a constant, such a high salt concentration

may conceivably cause potential difficulties for tuning and

matching of the probe circuits and adverse effects on the signal-

to-noise ratio (SNR). Sample dilution will further reduce the

SNR with the recommended buffer–urine volume ratio of 1 : 2.

Therefore, buffer optimization is still required to comprehen-

sively consider the pH control, dilution minimization and SNR.

In this work, we systematically investigated the effects of pH

and ionic strength on 1H NMR chemical shifts using a mixture

solution of 9 typical urinary metabolites including acetate, hip-

purate, citrate, DMA, TMA, creatinine, glycine, histidine and

urea. The dependence of chemical shifts on pH and ionic strength

was discussed and the optimal pH and ionic strength were

determined. Based on verification with 10 human urine samples,

we proposed a K2HPO4/NaH2PO4 buffer system and recom-

mended a new urine preparation method for the NMR-based

metabonomic studies.

Experimental

Chemicals

Acetate, hippurate, citrate, dimethylamine (DMA), trimethyl-

amine (TMA), creatinine, glycine, histidine, urea, NaCl, NaOH,


HCl, K2HPO4, Na2HPO4$12H2O and NaH2PO4$2H2O were all

purchased as analytical grade from Guoyao Chemical Co. Ltd.

(Shanghai, China) and used without further treatments. Deute-

rium oxide (D2O, 99.9% D) and sodium 3-trimethly-

silyl[2,2,3,3-2H4] propionate (TSP) were purchased from

Cambridge Isotope Laboratories, Inc. (MA, USA). Double

distilled water purified on a Millipore system was used for

preparing all the solutions.

Urine sample collection

Spot urine samples were collected from 5 male and 5 female

healthy volunteers aged between 28 and 63 years old, without

diet restriction, and were used immediately after collection.

pH measurements

The pH values were measured using a Mettler Toledo pH meter

(Delta 320) equipped with a Mettler Toledo combination glass

electrode at room temperature (25–26 �C). The pH meter was

calibrated using commercially available two-point standard

buffers (pH 4.01 and 7.00) before measurements. All the pH

measurements were performed three times to ensure accuracy

before NMR experiments.

Sample preparation for NMR measurements

A series of solutions containing various NaCl concentrations

were prepared with a mixture of 9 typical urinary metabolites

including acetate, hippurate, citrate, creatinine, DMA, TMA,

glycine, histidine (about 1.0 mM each) and urea (30 mM). Each

of the solutions (pH, �6.0) was then divided into 22 aliquots, the

aliquot (0.55 mL) was added by 0.05 mL D2O containing TSP

(0.1%, m/v), where D2O was used as a field lock and TSP as an

internal chemical shift reference. The aliquots were titrated with

either 0.2 M HCl or 0.2 M NaOH solution to obtain the pH

range from 1.5 to 12.7 with about 0.5 unit intervals. The NaCl

concentrations in the titrated solutions were 0, 0.1, 0.2, 0.5 and

1.0 M, respectively. A total of 110 solutions so prepared were

subjected to 1H NMR analysis to obtain chemical shifts of those

metabolites at different pH values and ionic strengths.

Two buffer systems were prepared in D2O containing TSP

(0.05%, m/v) for pH control of the human urine samples, namely,

sodium phosphate buffer solution (0.2 M, pH 7.4) consisting of

Na2HPO4$12H2O and NaH2PO4$2H2O (molar ratio of 4 : 1),

and potassium sodium buffer solutions (0.2, 0.5, 1.0, 1.5 and

2.0 M, pH 7.4) with K2HPO4 and NaH2PO4$2H2O (molar ratio

of 4 : 1).

Two human urine samples, A (pH 5.8) and B (pH 6.3), were

employed to optimize buffer concentration and the buffer-to-

urine volume ratio (VBuffer/VUrine). The aliquot urines (4 mL)

were added with the variable amount of K2HPO4/NaH2PO4

buffer (0.2, 0.5, 1.0, 1.5 and 2.0 M, pH 7.4), from 0.0 to 2.0 mL

with 0.2 or 0.4 mL intervals, respectively. For the validation

of the optimized buffer system, 10 human urine samples (pH,

5.8–8.0) were mixed with K2HPO4/NaH2PO4 buffer (0, 0.5, 1.5

and 2.0 M) at VBuffer/VUrine (1 : 10) corresponding to the final

buffer concentrations (CFB) of 0, 0.045, 0.136 and 0.181M,

respectively. For the purpose of comparison, the same 10 urine

samples were prepared with Na2HPO4/NaH2PO4 buffer in the

Analyst, 2009, 134, 916–925 | 917

Fig. 1 The structures of some urinary metabolites.

traditional way (0.2 M; pH 7.4; VBuffer/VUrine 1 : 2) giving a CFB

of 0.067 M. The buffered urine samples (0.6 mL) were then

transferred into 5 mm diameter NMR tubes, respectively,

following agitation and centrifugation (10 000 rpm, 10 min), for

NMR analysis.

NMR measurements

The 1H NMR spectra were acquired at 298 K on a Bruker AVII

500 MHz spectrometer operating at 500.13 MHz equipped with

an inverse triple resonance probe (TXI) with a shielded Z-

gradient. The standard one-dimensional pulse sequence (RD–

90�–t1–90�–tm–90�–acquisition) was employed with a weak

continuous wave irradiation equivalent to 50 Hz during the

recycle delay (RD, 2 s) and mixing time (tm, 0.1 s) to suppress

water signal. t1 was set to 4 ms and the 90� pulse length was

adjusted to about 10 ms for each sample individually. For all the

spectra, 32 transients were accumulated with 32k data points and

a spectral width of 20 ppm. An exponential line-broadening

factor of 1 Hz was applied to all free induction decays and zero-

filled to 64k prior to Fourier transformation. Chemical shifts of

the proton resonances were recorded relative to an internal

reference (TSP, 0.0 ppm).

Titration data analysis

The pH dependence of 1H NMR chemical shifts of the tested

metabolites in the mixture was fitted into the modified Hender-

son–Hasselbalch equation30 (eqn (1)),

dobs ¼ dmin þXi¼1; n

�Di =

1þ 10ðpH�pKaiÞ

�(1)

where dobs is the observed chemical shift at a given pH, dmin is

chemical shift of a metabolite in the fully ionized form; pKai and

Di denote the apparent ionization constant and the chemical shift

difference between fully ionized and unionized forms for the ith

equilibrium process, respectively; n is the number of ionization

processes in a molecule. The ionization constant has a relation-

ship with ionic strength (I) as described by the following equa-

tion43 (eqn (2)),

pKa � pKa0 ¼ xI1/2 � jI (2)

where pKa and pKa0 are ionization constants with a given ionic

strength and no salts, respectively; x and j are two constants

associated with ion interactions in the system studied.

Results and discussion

The effects of pH and ionic strength on 1H chemical shifts of

urinary metabolites

A mixed solution of acetate, hippurate, citrate, DMA, TMA,

creatinine, glycine, histidine and urea (the structures of them

were shown in Fig. 1) was employed to consider the most

abundant urinary metabolites representing carboxylic acids

(acetate, hippurate and citrate), amines (DMA and TMA), and

amino acids (glycine and histidine); the majority of these

metabolites exhibit high sensitivity to chemical shifts against pH

variations due to their ionizable groups. Fig. 2 shows the

918 | Analyst, 2009, 134, 916–925

dependence of 1H NMR chemical shifts on pH (1.5–12.7) of the

solutions containing different NaCl concentrations (0, 0.1, 0.2,

0.5 and 1.0 M). The open symbols were the measured data points

and solid lines represented the theoretically calculated data from

the Henderson–Hasselbalch equation (eqn (1))30 at a given NaCl

concentration. The pKa and D values were obtained by fitting the

dependence of chemical shifts on pH at 5 salt concentrations and

are tabulated in Table 1; the reported errors in these values were

standard errors generated from the fitting processes. To the best

of our knowledge, this is the first time that the impacts of pH and

ionic strength on the chemical shifts of all these 9 urinary

metabolites are investigated systematically in such broad ranges

of pH and salt concentration (ionic strength).

The titration profiles for all the tested metabolites (Fig. 2)

clearly showed dependence of the chemical shifts on the sample

pH and salt concentration. Visual inspection noticed that the

chemical shifts of citrate, creatinine, DMA and TMA had a high

sensitivity on ionic strengths whereas those of other metabolites

such as glycine and histidine only exhibited a mild salt sensi-

tivity. At a given salt concentration, 1H chemical shifts for the

metabolites decreased with the increase of pH (as also shown in

Fig. S-1 in the ESI†). Similar results were also observed for

citrate, creatinine, hippurate and glycine in a previous study31

though in a much narrower pH range (3.8–7.9). Such chemical

shift changes were originated from the pH-induced alteration in

ionization process of the functional groups and thus the electron

density around the nuclei concerned. For carboxyl and amino

groups, the increase of pH leads to the increase of RCOO� or

the decrease of RNH3+ concentration which enhances field-

shielding effects on the protons in the molecules, and thus

moving the proton peaks to upper field (or smaller chemical

shift values).

In addition, the pH titration curves for all metabolite chemical

shifts went through some transitions associated with the


Fig. 2 The pH dependence of the 1H NMR chemical shifts for some urinary metabolites in the model solutions containing NaCl of 0 M (,), 0.1 M (B),

0.2 M (O), 0.5 M (P) and 1.0 M (>); salt concentration increased from blue to red lines; the inserts showed regional expansions; open symbols were the

measured data points and the solid curves were calculated data from eqn (1).

ionization processes of relevant functional groups (Fig. 2). For

example, only one transition was observed for acetate, hippurate,

DMA and TMA at pH 4.76, 3.62, 11.38 and 10.15, respectively,

which coincided with pKa values of their only functional groups;

a single transition at pH 4.89 for creatinine is also in reasonable

agreement with the ionization of the –CONH group (reported

pKa z 4.8);44 for glycine, two transitions at pH 2.33 and 10.01

coincided with the pKa values of its carboxyl and amino groups.

In theory, citrate ought to have three transitions owing to its

three carboxyl groups (pKa, 3.13, 4.7 and 6.4).44 Our experi-

mental data, however, only showed one broad transition at pH

3–6 which was probably because the ionization constants for

three carboxyl groups were too close to each other. For histidine,

apart from two transitions at pH ca. 1.50 and 9.48 associated

with the carboxyl and amino groups, another transition was

clearly observable at pH z 6.1 from 2-CH, 5-CH and b-CH2

which resulted from the ionization of the NH group in the

imidazole ring (reported pKa 6.04).44


Furthermore, the maximum chemical shift changes resulting

from the pH-induced transitions differed markedly for different

metabolites as indicated by the D values in Table 1. In general,

ionization of a carboxyl group led to the D values of 0.16–0.36

ppm (e.g. CH3 of acetate, CH2 of hippurate and a-CH2 of

glycine), whereas ionization of an amino group showed the D

values of 0.37–0.69 ppm (a-CH2 of glycine, CH3 of DMA and

TMA). This is probably related to the difference of ionizable

groups (–COOH, –NH2 and N–H) or charge distributions.

Moreover, different proton resonances of the same metabolite

also showed the different D values. For example, the ionization

of the carboxyl group in hippurate resulted in much greater

chemical shift changes for CH2 (0.22 ppm) than for aromatic

protons (�0.01 ppm); the ionization of –CONH in creatinine led

to larger chemical shift changes for CH2 (0.23 ppm) than for CH3

(0.08 ppm). This is not surprising and can be explained by

proximity effects of the ionizable groups on the observed

protons, which is in reasonable agreement with previous findings

Analyst, 2009, 134, 916–925 | 919

Table 1 The pKa and D values of the metabolites in the mixture with different NaCl concentrations

Compound NaCl (M) pKa1a Db pKa2 D pKa3 D

d1H(pH z 7.4)

Acetate (CH3) (pKa 4.76)c 0 4.76 � 0.00 0.16 � 0.01 1.9220.1 4.62 � 0.00 0.16 � 0.010.2 4.58 � 0.00 0.16 � 0.010.5 4.53 � 0.00 0.16 � 0.011.0 4.47 � 0.00 0.16 � 0.01

Hippurate (CH2) (pKa 3.62) 0 3.62 � 0.01 0.22 � 0.00 3.9740.1 3.45 � 0.01 0.22 � 0.000.2 3.41 � 0.02 0.23 � 0.000.5 3.37 � 0.01 0.23 � 0.001.0 3.28 � 0.02 0.23 � 0.00

Hippurate (o-ArH) 0 5.00 � 0.01 0.01 � 0.00 7.8460.1 4.84 � 0.01 0.01 � 0.000.2 4.79 � 0.01 0.01 � 0.000.5 4.73 � 0.01 0.01 � 0.001.0 4.85 � 0.05 0.01 � 0.00

Hippurate (p-ArH) 0 2.43 � 0.97 0.009 � 0.007 7.6400.1 2.08 � 1.47 0.009 � 0.010.2 1.90 � 2.25 0.009 � 0.0270.5 — —1.0 — —

Citrate (CH) pKa

(3.13,4.7,6.4)0 3.15 � 0.18 0.13 � 0.01 4.73 � 0.25 0.09 � 0.01 6.30 � 0.08 0.09 � 0.01 2.6430.1 2.91 � 0.12 0.14 � 0.01 4.32 � 0.14 0.08 � 0.01 5.74 � 0.04 0.09 � 0.010.2 2.95 � 0.09 0.15 � 0.01 4.25 � 0.15 0.09 � 0.01 5.49 � 0.03 0.09 � 0.010.5 2.94 � 0.04 0.12 � 0.01 4.41 � 0.07 0.10 � 0.01 5.45 � 0.04 0.12 � 0.011.0 2.48 � 0.13 0.17 � 0.01 3.83 � 0.15 0.09 � 0.01 5.01 � 0.04 0.10 � 0.01

Citrate (CH0) 0 3.13 � 0.05 0.10 � 0.01 4.63 � 0.08 0.12 � 0.01 6.17 � 0.05 0.16 � 0.01 2.5520.1 2.92 � 0.02 0.10 � 0.01 4.28 � 0.03 0.13 � 0.01 5.69 � 0.02 0.15 � 0.010.2 2.94 � 0.03 0.09 � 0.01 4.25 � 0.07 0.13 � 0.01 5.49 � 0.04 0.16 � 0.010.5 2.89 � 0.01 0.07 � 0.00 4.26 � 0.02 0.14 � 0.00 5.44 � 0.02 0.17 � 0.001.0 2.62 � 0.04 0.09 � 0.00 3.91 � 0.08 0.15 � 0.01 5.03 � 0.05 0.15 � 0.01

DMA (CH3) (pKa 10.8) 0 11.38 � 0.03 0.45 � 0.01 2.7280.1 11.58 � 0.03 0.47 � 0.010.2 11.59 � 0.04 0.48 � 0.010.5 11.60 � 0.05 0.49 � 0.011.0 11.67 � 0.04 0.50 � 0.01

TMA (CH3) (pKa 9.8) 0 10.15 � 0.02 0.69 � 0.01 2.9000.1 10.41 � 0.03 0.69 � 0.010.2 10.55 � 0.02 0.71 � 0.010.5 10.58 � 0.04 0.71 � 0.011.0 10.78 � 0.02 0.73 � 0.01

Creatinine (CH3)(pKa 4.83, 9.2)

0 4.89 � 0.01 0.08 � 0.01 3.0460.1 4.95 � 0.01 0.08 � 0.010.2 5.01 � 0.01 0.08 � 0.010.5 5.04 � 0.01 0.08 � 0.011.0 5.06 � 0.01 0.08 � 0.01

Creatinine (CH2) 0 4.92 � 0.01 0.23 � 0.01 4.0600.1 4.96 � 0.01 0.23 � 0.010.2 5.00 � 0.01 0.23 � 0.010.5 5.02 � 0.01 0.24 � 0.011.0 5.03 � 0.01 0.24 � 0.01

Glycine (CH2)(pKa 2.34, 9.6)

0 2.33 � 0.05 0.36 � 0.02 10.01 � 0.02 0.37 � 0.00 3.5640.1 2.30 � 0.08 0.36 � 0.03 10.06 � 0.03 0.37 � 0.000.2 2.38 � 0.07 0.35 � 0.02 10.10 � 0.03 0.37 � 0.000.5 2.36 � 0.08 0.35 � 0.02 10.14 � 0.04 0.37 � 0.011.0 2.30 � 0.06 0.36 � 0.02 10.16 � 0.03 0.38 � 0.01

Histidine (a-CH)(pKa 1.82, 6.04, 9.33)

0 0.88 � 0.63 1.88 � 2.49 6.30 � 0.20 0.03 � 0.00 9.48 � 0.01 0.48 � 0.00 3.9850.1 1.41 � 0.32 0.74 � 0.41 6.28 � 0.32 0.04 � 0.00 9.45 � 0.02 0.47 � 0.010.2 1.72 � 0.17 0.49 � 0.11 6.46 � 0.27 0.05 � 0.00 9.49 � 0.03 0.47 � 0.010.5 1.75 � 0.15 0.48 � 0.09 6.54 � 0.29 0.05 � 0.00 9.49 � 0.03 0.48 � 0.011.0 1.74 � 0.13 0.48 � 0.07 6.93 � 0.25 0.07 � 0.00 9.55 � 0.04 0.46 � 0.01

Histidine (b-CH2) 0 1.22 � 0.45 0.28 � 0.24 6.09 � 0.02 0.13 � 0.00 9.48 � 0.01 0.27 � 0.00 3.2110.1 1.23 � 1.06 0.30 � 0.59 6.12 � 0.06 0.13 � 0.00 9.43 � 0.03 0.27 � 0.000.2 1.44 � 0.82 0.24 � 0.34 6.17 � 0.07 0.13 � 0.00 9.45 � 0.03 0.27 � 0.000.5 1.53 � 0.41 0.20 � 0.13 6.32 � 0.06 0.14 � 0.00 9.47 � 0.03 0.27 � 0.001.0 1.72 � 0.36 0.13 � 0.05 6.40 � 0.08 0.15 � 0.00 9.52 � 0.04 0.27 � 0.00

Histidine (2-CH) 0 6.11 � 0.01 0.91 � 0.00 7.7900.1 6.12 � 0.01 0.89 � 0.000.2 6.14 � 0.03 0.92 � 0.000.5 6.23 � 0.01 0.90 � 0.001.0 6.23 � 0.02 0.91 � 0.01

920 | Analyst, 2009, 134, 916–925 This journal is ª The Royal Society of Chemistry 2009

Table 1 (Contd. )

Compound NaCl (M) pKa1a Db pKa2 D pKa3 D

d1H(pH z 7.4)

Histidine (5-CH) 0 6.14 � 0.01 0.13 � 0.00 9.49 � 0.02 0.33 � 0.00 7.0640.1 6.17 � 0.01 0.13 � 0.00 9.43 � 0.04 0.33 � 0.000.2 6.17 � 0.04 0.13 � 0.01 9.50 � 0.11 0.34 � 0.000.5 6.27 � 0.02 0.14 � 0.00 9.43 � 0.04 0.34 � 0.001.0 6.29 � 0.03 0.13 � 0.00 9.51 � 0.07 0.34 � 0.00

a The fitted data of pKa. b The chemical shift difference between the ionized and unionized forms. c The pKa values in ref. 44; —: pKa was not obtainedfrom the fitted process.

Fig. 3 The effects of pH on the 1H NMR data for some urinary

metabolites without added salt (the solid lines were for visual guidance

only): (A) the chemical shift changes relative to that at pH 7.4 (Dd ¼ dpH

� d7.4, dpH and d7.4 were chemical shifts at a given pH and at pH 7.4,

respectively); the insert showed the regional expansion for pH 6.7–7.8; the

keys for symbols: histidine 5-CH (C) and 2-CH (B), citrate CH (,) and

CH0 (-), glycine (�), TMA (O), DMA (:), acetate ( ), creatinine (P)

and hippurate (*); (B) the intensity of urea signal relative to TSP as

a function of pH.

from the pH effects on 13C chemical shifts for short chain fatty

acids.45,46 It is interesting to note that ionization of a functional

group will induce small changes to the chemical shift of a given

proton when it is positioned six bonds away from the group. For

instance, 2-CH of histidine is more than six bonds away from the

amino and carboxyl groups and shows no chemical shift transi-

tions from the ionization of these two groups. Similarly, the

ionization of the carboxyl group induced small chemical shift

changes (D < 0.01 ppm) for aromatic protons of hippurate which

are located at more than seven bonds from the group.

The above discussion indicated that maximum chemical shift

changes occurred when pH was close to pKa. However, when the

pH was more than two units away from the pKa value (i.e. pH <

pKa � 2, pH > pKa + 2) of a given ionizable group, the proton

chemical shifts will no longer be sensitive to the ionization of the

group, which is clearly illustrated by the data of acetate (Fig. 2).

For all tested metabolites, their chemical shifts showed less pH

dependence at the extreme pH (i.e. pH < 1.5 and pH > 12) and

perhaps at neutral pH as well. The extreme acidic and basic

conditions are not suitable for urinary metabonomics studies

because such harsh conditions may lead to chemical degradation

such as hydrolysis and be drastically different from the normal

physiological conditions (urine pH z 4.8–8.0). Therefore, only

the neutral pH range seems to be the best choice and deserves

more detailed analysis.

Fig. 3A displayed the changes of chemical shifts relative to that

at pH 7.4 with no added salt. Within the pH range of 7.1–7.7 (as

shown in the inserted expansion), most metabolite resonances

had a chemical shift variation of 0.002 ppm or less with the

exception noted only for the proton resonances of citrate (<0.008

ppm) and histidine (<0.02 ppm). At such a pH region, it is also

beneficial to extract information about exchangeable protons.

This was clearly illustrated in Fig. 3B, where the urea signal

intensity relative to TSP (d 0.0) showed little changes in the pH

range of 7.1–7.7, but significantly diminished in the mild acid (pH

< 5) and base (pH > 9) probably due to the enhanced exchange

rate of exchangeable protons (–NH2).

To understand how ionic strength affects the chemical shifts at

the optimized pH region (7.1–7.7), the 1H chemical shift varia-

tions of the metabolite mixture were plotted as a function of the

added NaCl concentrations at a fixed pH (7.40 � 0.05) (the 1H

NMR spectra were also shown in Fig. S-2 of the ESI†). Fig. 4A

showed that an increase of NaCl resulted in a clear increase of

chemical shifts except for one signal of citrate. When the salt

concentration was lower than 0.15 M, the proton chemical shift

variations was less than 0.004 ppm for most metabolites.


The exception was clearly observable for the high field doublet of

citrate (about 0.005 ppm) and two imidazole protons (about

0.008 ppm) in histidine. It was noted that two protons of citrate

behaved differently, the exact reason for such a difference

remains unknown even though it may be related to their spatial

Analyst, 2009, 134, 916–925 | 921

Fig. 4 The effects of ionic strength on the NMR and pKa data for some

urinary metabolites at pH z 7.4: (A) the chemical shift changes as

a function of NaCl (Dd ¼ dI � d0, where dI and d0 represented chemical

shifts at a given salt concentration and at no added salt, respectively); the

solid lines were for visual guidance only; (B) the pKa changes as a func-

tion of NaCl (pKa and pKa0 were the calculated ionization

constants based on eqn (2) at the given salt concentration and at no added

salt, respectively); (C) the signal-to-noise ratio of the NMR signals

normalized against that for salt-free solution as a function of NaCl

concentration; the solid lines were for visual guidance only; the keys for

symbols: histidine 5-CH (C) and 2-CH (B), citrate CH (,) and CH0

(-), glycine (�), TMA (O), DMA(:), acetate ( ), creatinine (P) and

hippurate (*).

positioning. The effects of ionic strength on chemical shifts were

observed for amino acid residues of some proteins and explained

as the electrostatic effects47 or pKa dependence of salts.48 For all

metabolites studied here, the salt-induced pKa changes were

922 | Analyst, 2009, 134, 916–925

fitted to the empirical relationship described in eqn (2) (Fig. 4B).

Over the salt concentration of 0–1.0 M, the pKa values of the

carboxyl groups in acetate, citrate and hippurate decreased with

the increase of ionic strength whereas those of amino groups in

DMA, TMA and creatinine showed opposite trends. However,

glycine and histidine showed broad increases of pKa for both

amino and carboxyl groups, which were also observed in the

potentiometric titration studies of some other amino acids

reported previously.43 Therefore, our results indicated that the

salt dependence of chemical shifts was probably due to alteration

of ionization constants. Furthermore, the signal-to-noise ratio

(SNR) for the same signals at different salt concentrations was

calculated against the area of the same baseline region (�2 to �5

ppm). Fig. 4C clearly showed that the normalized SNR for each

metabolite decreased with the rise of salt concentration, and the

SNR reduction was less than 15% when the salt concentration

was below 0.15 M compared with the salt-free solution.

All these observations from the metabolite mixture suggested

that, when the small buckets (e.g. 0.004 ppm) were employed in

multivariate data analysis for NMR-based urinary metabonomic

studies, the pH range of 7.1–7.7 and the added salt concentration

of 0.15 M should be targeted to minimize the chemical shift

variation. However, it is still necessary to optimize the buffer

conditions for real urine samples.

Optimizing urine sample preparation

Phosphate salt (H2PO4�/HPO4

2�) has a pKa value49 of 6.8 and

thus good buffer capacity in the pH range of 5.8–7.8, which is

well suited for the targeted urine pH control (7.1–7.7). In tradi-

tional metabonomics studies, NaH2PO4/Na2HPO4 buffer was

often used to control the pH of urine samples. However, high

concentration buffer cannot be allowed due to the low solubility

of Na2HPO4$12H2O (4.2 g/100g water, 20 �C), and thus a large

amount of buffer has to be added to urine, which will inevitably

cause sample dilution. Furthermore, Na2HPO4$12H2O often

precipitates even with moderate buffer concentration (e.g. 0.2 M)

during low temperature storage, resulting in changes of buffer

composition. It is therefore desirable to seek alternative buffer

systems with high buffer capacity to overcome the above short-

comings.

We chose K2HPO4 and NaH2PO4 salts as the buffer pair

because both of them have high solubility (K2HPO4, 167g/100g

water; NaH2PO4$2H2O, 15.6g/100g water; 20 �C). Two of 10

human urine samples, designated A (4 mL, pH 5.8) and B (4 mL,

pH 6.3), were titrated with K2HPO4/NaH2PO4 buffer (pH 7.4) at

5 different concentrations (0.2, 0.5, 1.0, 1.5 and 2.0 M). The

results (Fig. 5A and 5B) showed that, for both urine samples,

their pH values were responsive to the volume and concentration

of buffer added. To reach the optimal pH range (7.1–7.7) dis-

cussed in the earlier section, sample A required the buffer–urine

ratios (VBuffer : VUrine) to be greater than 1 : 2 for 0.2 M buffer

and about 1 : 10, 1 : 20 for 1.5 and 2.0 M buffers, respectively. As

for sample B with a higher initial pH, the ratios were merely 1 : 2,

1 : 20, less than 1 : 20 for 0.2, 1.5 and 2.0 M buffers, respectively.

It is clear that the urine pH was only determined by the final

buffer concentration CFB (Fig. 5C). For the traditional urine

preparation method (0.2 M buffer, pH¼ 7.4, VBuffer : VUrine¼ 1 :

2, CFB ¼ 0.067 M), samples A and B had about 30% dilution and


Fig. 5 Optimization of the buffer system (the solid lines were for visual guidance only): (A) the pH values of human urine sample A (4 mL, pH 5.8) as

titrated with buffer concentration of 0.2 M (B), 0.5 M (P), 1.0 M (O), 1.5 M (,), 2.0 M ( ); (B) the pH values of human urine sample B (4 mL, pH 6.3)

as titrated with buffer concentration of 0.2 M (C), 0.5 M (;), 1.0 M (:), 1.5 M (-) and 2.0 M (�); (C) the urine pH values as a function of the

final buffer concentration (CFB) for sample A (lower curve) and B (upper curve); (D) the signal-to-noise ratio (SNR) as a function of CFB for sample B

NMR spectra (relative to that without buffer); the keys for symbols: histidine 5-CH (C), citrate CH (,), glycine (�), DMA (:), creatinine (P) and

hippurate (*).

their pH values can only be adjusted to 7.00 and 7.20. In contrast,

when 1.5 M buffer (pH ¼ 7.4, VBuffer : VUrine ¼ 1 : 10, CFB ¼0.136 M) was used, both urine samples had only less than 10%

dilution and their pH values reached to 7.20 and 7.35, respec-

tively. Similarly, the use of 2.0 M buffer (pH ¼ 7.4, VBuffer :

VUrine¼ 1 : 10, CFB¼ 0.181 M) can make both samples reach pH

7.23 and 7.40, respectively. Therefore, the higher concentration

buffer is beneficial to control the sample pH to the optimal level

with much less sample dilution.

However, a high salt concentration has some adverse effects

on the signal-to-noise ratio (SNR). Fig. 5D showed that the

SNR for some metabolites decreased with the increase of final

buffer concentrations. For example, with CFB values of 0.136

and 0.181 M, the SNR of creatinine was reduced for about 15%

and 20% respectively and such a reduction was about 5% (CFB,

0.136 M) and 13% (CFB, 0.181 M) for glycine and DMA. More

seriously, when a CFB of 0.181 M was employed, obvious

difficulties were experienced in tuning and matching of probe

circuits. It is worth noting that urine samples normally contain

more than 0.1 M salts already and the amount of salt added

ought to be minimized. In contrast, when traditional sample

preparation was employed (CFB, 0.067 M), the SNR had more

than 30% reduction for all metabolites tested, probably due to

salt effects and sample dilution. Based on the above discussion,

we concluded that the 1.5 M K2HPO4/NaH2PO4 buffer (pH

7.4) with the buffer–urine ratio of 1 : 10 (CFB, 0.136 M) should

be employed for human urine preparation in metabonomics

studies.


Verification of the optimized urine preparation method

To verify the robustness of the above preparation method for

a larger set of samples, 10 human urine samples (pH, 5.8–8.0)

were prepared with K2HPO4/NaH2PO4 buffer having different

concentrations at VBuffer : VUrine (1 : 10), respectively. For the

purpose of comparison, these samples were also prepared in the

traditional way (0.2 M Na2HPO4/NaH2PO4 buffer, VBuffer :

VUrine ¼ 1 : 2). Fig. 6A showed that the pH ranges were signifi-

cantly narrowed with the increase of CFB. For example, when

CFB was 0.067 M, the pH values of the tested urine samples

ranged between 7.0 and 7.7 which was close but failed to achieve

optimal pH target (7.1–7.7). In contrast, when CFB was 0.136 M

or higher, the pH range was narrowed to 7.1–7.5 and met the

demand of targeted pH.

With a CFB of 0.136 M or higher, the chemical shift ranges for

most metabolites were also narrowed considerably (as shown

Fig. S-3 for the citrate and DMA signals in the ESI†). Such

effects were more clearly indicated in Fig. 6B by the standard

deviation (SD) of the chemical shifts as a function of CFB. It was

obvious that the SD for some urine metabolites had a marked

decrease with the increase of CFB from 0 to 0.067 M due to

the minimization of pH variations (from 2.14 to 0.64). When

CFB was greater than 0.067 M, such SDs reached a plateau

(the expanded region in Fig. 6B). For example, when CFB was

0.136 M, the SD was reduced to less than 0.002 ppm for the

signals of acetate, glycine, DMA, TMA and hippurate. The

exception is again observed for the two protons of citrate (SD,

Analyst, 2009, 134, 916–925 | 923

Fig. 6 Verification of the optimized buffer system: (A) the pH distri-

bution for 10 human urine samples as a function of the final buffer

concentration (CFB); (B) standard deviation of the metabolite chemical

shifts for 10 human urine samples as a function of the final buffer

concentration (CFB); the solid lines were for visual guidance only; the

keys for symbols: histidine 5-CH (C) and 2-CH (B), citrate CH (,) and

CH0 (-), glycine (�), TMA (O), DMA (:), acetate ( ), creatinine (P)

and hippurate (*).

0.004 ppm) and the imidazole protons of histidine (SD, 0.009

ppm for 5-CH and 0.025 ppm for 2-CH). It is thus suggested that

the chemical shifts for the two metabolites (a total of four signals)

should be corrected before multivariate data analysis, especially

when high resolution spectral data were employed. Based on

these, we recommend that a CFB of 0.136 M is used in routine

work since such a buffer will give some safety margin in terms of

pH control and chemical shift consistency with respect to the

0.067 M buffer.

Conclusions

Chemical shift variations of urinary metabolite signals clearly

resulted from the alteration of ionization equilibrium induced by

pH and ionic strength. The effects of ionic strength on chemical

shifts can be explained by salt-induced ionization constant

changes for the ionizable groups. By taking chemical shift

consistency, the signal-to-noise ratio, sample dilution effects and

low temperature buffer storage into consideration, we found that

K2HPO4/NaH2PO4 buffer (pH 7.4, 1.5 M) with the buffer–urine

volume ratio of 1 : 10 was optimal for the NMR-based urinary

924 | Analyst, 2009, 134, 916–925

metabonomic studies. With such preparation, the sample pH

values were adequately controlled to pH 7.1–7.7 for normal

human urine, and chemical shifts of most metabolites with

ionizable groups were controlled within 0.002 ppm (1.2 Hz on

600 MHz data) the only exception of that for citrate and histi-

dine. High resolution bucketing and full resolution data analysis

can be carried out with some corrections for the chemical shifts

of citrate resonances and two singlets of histidine in the aromatic

region using already reported peak alignment methods.23–25

Acknowledgements

We thank Mr Xianwang Jiang of Wuhan Institute of Physics and

Mathematics for assistance in data fitting. We acknowledge the

financial supports from the National Basic Research Program of

China (2007CB914701, 2006CB503909 for H. T. and

2009CB118804 for Y. W.), National Natural Science Foundation

of China (20575074, 20825520 for H. T. and 20775087 for Y. W.)

and the Knowledge Innovation Program of the Chinese

Academy of Sciences (KJCX2-YW-W11 for Y. W., KSCX1-

YW-02 for H. T.).

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