Trace Element Profiling Using Inductively Coupled Plasma Mass

Mar 30, 2009 - School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, P.R. China, Shanghai Center for Systems. Biomedicine, Shanghai Jiao...
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Anal. Chem. 2009, 81, 3683–3692

Trace Element Profiling Using Inductively Coupled Plasma Mass Spectrometry and its Application in an Osteoarthritis Study Tie Zhao,† Tianlu Chen,‡ Yunping Qiu,† Xiangyu Zou,§ Xin Li,† Mingming Su,† Chonghuai Yan,§ Aihua Zhao,† and Wei Jia*,| School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, P.R. China, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China, Shanghai Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, P.R. China, and Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081 In this study, a novel method of quantitatively measuring serum trace elements using inductively coupled plasma mass spectrometry (ICP-MS) coupled with multivariate statistical analysis was developed and applied successfully to the study of osteoarthritis (OA). This technology provides potential advantages over conventional targeted elemental analysis in that it achieves high throughput measurement, small sample volume, and simple operational procedure. Such an unbiased method is particularly suitable for large scale discovery research on trace element based biomarkers. The method optimization and validation study involved accuracy and perturbation testing which focused on estimating the ability of the method to resist interferences in ICP-MS analysis, particularly those of mass 10% and p < 0.05.

Mg Mg Mg Al Ca Ca Ca Ti Ti V Cr Cr Mn Fe Fe Co Ni Ni Ni Cu Cu Zn Zn Zn Ga Ga Ge Ge As Se Se Se

element mass RC%a

Fe

Table 3. Results of High Concentration Perturbation Test

Analytical Chemistry, Vol. 81, No. 9, May 1, 2009

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Mg Mg Mg Al Ca Ca Ca Ti Ti V Cr Cr Mn Fe Fe Co Ni Ni Ni Cu Cu Zn Zn Zn Ga Ga Ge Ge As Se Se Se

24 25 26 27 42 43 44 47 49 51 52 53 55 56 57 59 60 61 62 63 65 66 67 68 69 71 72 73 75 77 78 82

element mass

-10.05 -34.56 -0.95 -22.73 0.00 1.06 0.69 3.47 0.65 0.00 5.26 8.04 15.63 -6.07 -2.00 4.49 -2.37

3.85 3.64 1.67 -2.50 0.00 0.00 1.02 -20.10 -25.56 -2.04 -2.09 -5.26 -3.45

RC%

Fe

RC%

p

0.12 3.85 0.12 0.12 0.00 1.00 0.37 0.00 1.00 0.37 -2.50 0.37 1.00 1.00 0.64 0.13 -11.00 0.38 0.15 -23.33 0.18 0.37 -4.08 0.23 0.23 -3.66 0.04 0.12 -7.89 0.01 0.29 0.00 1.00 -1.39 0.37 40.00 0.00 0.34 16.40 0.08 0.26 8.09 0.78 0.91 120.00 0.00 0.64 -30.68 0.56 1.00 -3.23 0.37 0.48 1.77 0.54 0.68 -1.38 0.42 0.28 0.00 1.00 0.64 -1.96 0.29 1.00 13.21 0.30 0.81 -21.05 0.05 0.05 0.89 0.78 0.44 -30.63 0.44 0.60 -4.92 0.23 0.56 -3.00 0.16 0.10 8.43 0.61 0.79 4.63 0.27

p

Ca

7.50 0.90 1.28 2.04 -10.05 -25.56 2.04 1.57 -2.63 4.60 -1.39 0.00 -5.82 -33.09 0.95 -67.27 -3.23 4.61 1.38 2.78 0.65 33.96 -5.26 5.36 -18.75 -2.83 -8.00 -1.24 -3.33

RC%

Mg

0.51 0.37 0.37 0.23 0.40 0.15 0.52 0.35 0.23 0.27 0.37 1.00 0.45 0.28 0.92 0.22 0.37 0.10 0.37 0.27 0.64 0.01 0.73 0.22 0.17 0.15 0.18 0.21 0.17

p

p

0.90 2.56 4.08 -17.22 -28.89 0.00 0.00 0.00 5.75 -1.39 -2.50 -3.70 -33.82 0.95 -58.75 -3.23 4.61 0.69 2.78 0.00 11.32 6.58 1.79 -19.38 5.78 -6.00 -1.24 -3.33

3.85 3.64 1.67 -2.50 -1.80 0.00 3.06

RC%

Ti p 0.12 0.12 0.37 0.37 0.12 1.00 0.10

3.85 3.64 1.67 2.50 -0.90 0.00 0.00 -13.88 -24.44 2.04 0.00 -7.89 -1.15 -4.17 -2.50

RC%

Co

0.37 0.12 0.12 0.18 0.11 1.00 -4.08 0.23 1.00 -4.19 0.09 1.00 -5.26 0.16 0.42 0.00 1.00 0.37 -4.17 0.00 0.37 -2.50 0.37 0.65 -7.41 0.41 0.27 -33.82 0.27 -38.97 0.89 -3.81 0.61 0.00 0.27 -71.82 0.19 -70.45 0.37 -3.23 0.37 -3.23 0.10 6.38 0.07 6.38 0.68 -0.69 0.64 -3.45 0.37 0.69 0.77 2.08 1.00 -1.96 0.16 -3.27 0.37 3.77 0.71 -5.66 0.53 -5.26 0.69 1.32 0.37 4.46 0.13 7.14 0.15 -29.38 0.09 -7.50 0.74 -3.93 0.64 -1.10 0.86 -4.00 0.92 -8.20 0.38 -1.48 0.11 -3.48 0.17 -3.89 0.10 -4.74

5.77 0.10 3.64 0.12 3.33 0.12

RC%

Al

Table 4. Results of Low Concentration Perturbation Test

RC% p

RC%

Cu p

RC%

Mn p

RC%

Ni p

RC%

V p

RC%

Zn p

RC%

Se p

0.12 7.69 0.05 -3.85 0.23 -1.92 0.37 -1.92 0.37 -1.92 0.37 -1.92 0.37 -1.92 0.37 0.12 5.45 0.10 -1.85 0.37 -1.85 0.37 -1.85 0.37 -1.85 0.37 0.00 1.00 0.00 1.00 0.37 1.67 0.37 -3.45 0.23 -1.72 0.37 -1.72 0.37 -3.45 0.23 -1.72 0.37 0.00 1.00 0.68 -2.50 0.37 -2.78 0.64 2.78 0.64 -8.33 0.16 0.00 1.00 0.00 1.00 -5.56 0.37 0.37 0.90 0.37 -1.00 0.37 -1.00 0.37 -2.00 0.23 -2.00 0.23 -1.00 0.37 1.00 0.52 1.00 1.28 0.37 -1.43 0.37 -1.43 0.37 0.00 1.00 -1.43 0.37 -1.43 0.37 1.43 0.52 1.00 4.08 0.16 -1.11 0.37 0.00 1.00 0.00 1.00 0.00 1.00 -1.11 0.37 0.00 1.00 0.26 -14.83 0.24 -6.13 0.01 -7.98 0.00 -3.07 0.25 -9.82 0.27 -6.13 0.05 -2.45 0.33 0.16 -25.56 0.16 -2.70 0.48 -11.71 0.05 -5.41 0.20 -9.91 0.25 -8.11 0.08 -4.41 0.10 0.52 -2.04 0.64 -2.33 0.37 -6.98 0.10 -4.65 0.23 -6.98 0.10 -4.65 0.23 1.00 -1.82 0.16 -3.64 0.03 -4.85 0.00 -1.21 0.12 -3.64 0.03 -4.85 0.16 0.10 -6.56 0.12 -3.28 0.42 -6.56 0.21 0.00 1.00 0.00 1.00 -6.56 0.21 0.64 -1.15 0.81 11.27 0.21 15.49 0.14 19.72 0.19 8.45 0.14 2.82 0.42 0.00 -2.78 0.12 0.00 1.00 1.67 0.37 1.67 0.37 3.33 0.12 0.00 1.00 0.00 1.00 0.37 -2.50 0.37 0.00 1.00 0.00 1.00 3.03 0.37 6.06 0.12 0.00 1.00 0.00 1.00 -12.70 0.12 15.00 0.15 6.43 0.09 7.86 0.18 4.29 0.50 12.14 0.24 -1.43 0.67 0.21 -36.03 0.24 13.24 0.27 4.41 0.35 7.35 0.09 8.82 0.18 0.00 1.00 1.00 -8.57 0.25 2.22 0.80 1.11 0.90 -8.89 0.41 -6.67 0.53 -7.78 0.44 0.20 -71.25 0.20 852.38 0.02 19.05 0.16 57.14 0.19 62.86 0.23 143.33 0.09 0.37 -3.23 0.37 -0.35 0.74 -1.41 0.05 -2.11 0.01 -1.41 0.15 -1.76 0.19 0.01 6.38 0.01 -1.06 0.29 -3.19 0.02 -3.90 0.03 -2.48 0.09 -1.77 0.28 0.09 -2.07 0.25 -2.33 0.16 0.00 1.00 -1.55 0.12 -1.55 0.12 -0.78 0.37 0.37 1.39 0.56 2.36 0.35 3.15 0.15 1.57 0.49 0.79 0.74 1.57 0.49 0.07 -1.31 0.37 0.00 1.00 2.26 0.10 0.75 0.68 1.50 0.12 2.26 0.10 0.71 -9.43 0.19 28.21 0.07 10.26 0.12 20.51 0.29 10.26 0.51 7.69 0.16 12.82 0.51 0.92 -9.21 0.50 -8.70 0.48 -18.84 0.16 -11.59 0.53 -17.39 0.25 -1.45 0.90 -13.04 0.41 0.09 3.57 0.29 6.19 0.18 3.09 0.10 3.09 0.10 0.00 1.00 2.06 0.56 3.09 0.51 0.30 -29.38 0.33 28.16 0.66 -7.77 0.83 12.62 0.73 25.24 0.61 -21.36 0.69 13.59 0.77 0.41 -6.30 0.58 5.56 0.79 -6.94 0.77 -14.35 0.54 -11.57 0.47 18.98 0.21 1.22 0.52 0.71 8.20 0.71 2.09 0.20 14.46 0.21 8.43 0.18 4.42 0.18 -0.80 0.78 0.30 4.61 0.78 7.83 0.33 7.75 0.19 4.65 0.70 2.33 0.16 0.78 0.64 0.94 2.59 0.80 1.69 0.11 3.13 0.19 5.79 0.72 1.93 0.71 3.47 0.26

p

Cr

Figure 1. Heat map showing the results of the high (left) and low (right) concentration perturbation test, based on the result of Tables 3 and 4; the labels of this figure are the same as those in Table 3. Red regions meeting both p e 0.05 and RC%>10% represent the “problematic” isotopes whose determination tend to be perturbed by the variations of corresponding elements; Yellow regions are the isotopes with p e 0.05 and RC% < 10%, which can be regarded as “alert” ones; blue regions represent the “safe” isotopes since they are not likely to be interfered by other elements (p > 0.05); green regions are the overlapped isotopes where “stimuli” element and objective element are identical, and this region are named as “itself”.

counts per second (CPS) values were imported into the SIMCA-P 11.0 Software package (Umetrics, Umeå, Sweden) for multivariate statistical analysis. To alleviate the impact of the heteroscedasticity14 originating from the jagged levels of elements on the multivariate statistical model, the data was logarithmically transformed and autoscaled (centered and scaled to unit variation) prior to analysis. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLSDA) model was used to visualize the high dimensional data and determine the variation between the OA and HC group. The OPLS algorithm utilizes an orthogonal signal correction filter which separates the structured noise of the matrices from the variation in X related to Y,and thereby, allows the establishment of an optimal model with a single predictive component for the single Y-variable case. Such a property enables a more straightforward and accurate interpretation of loading of the OPLS model than PLS.15,16 The loading of this model was then combined with the value of the Variable Importance in the project (VIP) calculated from the model to identify the differential elements contributing to the variation. A typical 7-round cross-validation was performed to validate the model against overfitting. Both one-way analysis of variation (ANOVA) and the Nonparametric Kruskal-Wallis test (the classical univariate analysis) were also utilized to validate the results of differential elements from multivariate statistical analysis. RESULTS AND DISCUSSION Method Optimization and Validation. Internal standards were selected based on (i) maximizing the number of elements (14) van den Berg, R. A.; Hoefsloot, H. C.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. BMC Genomics 2006, 7, 142. (15) Grizzetti, B.; Bouraoui, F.; de Marsily, G.; Bidoglio, G. Water Sci. Technol. 2005, 51, 83–90. (16) Ni, Y.; Su, M.; Qiu, Y.; Chen, M.; Liu, Y.; Zhao, A.; Jia, W. FEBS Lett. 2007, 581, 707–711.

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represented according to their mass and ionization potential, (ii) the relative concentrations of the elements in serum, and (iii) the degree to which the elements are essential for human health. On the basis of these selection criteria, three elements, 45Sc, 115In, and 209Bi were selected, primarily because of both their low concentration in human serum and their non-relevance to human health. It should also be noted that bismuth is affected by the exogenous bismuth agents which are the common medications used to eradicate infection with Helicobacter pylori and to prevent travelers’ diarrhea17 in clinical therapy. As such, the replacement of Bi with other elements, such as Re, Tl, might be a better choice in any future study cases related to the interaction of bismuth. There are several practical advantages in the present method. A sample size requirement of only 0.1 g is a clear advantage in “omics” research, providing more flexibility in experimental endeavors than in the past. Additionally, since this sample size is comparable to the size used in other “omics” fields, this further facilitates the integration data from “multi-omics” studies in the future. Lastly, this methodology incorporates high sample throughput, typically at a rate of 15 samples per hour. This high throughput is in part facilitated by programming the microwave digestion to enable release of a majority of elements as completely as possible, without losses because of volatilization. Selection of suitable elemental masses for ICP-MS analysis detection was complicated by the fact that although elements between 40 and 82 amu are largely the essential and trace elements for human beings (concentrations ∼ 1ug/L18), these same elements can also be compromised by the interferences19 (17) Lambert, J. R. Rev. Infect. Dis. 1991, 13 Suppl 8, S691-695. (18) Muniz, C. S.; Fernandez-Martin, J. L.; Marchante-Gayon, J. M.; Garcia Alonso, J. I.; Cannata-Andia, J. B.; Sanz-Medel, A. Biol. Trace Elem. Res. 2001, 82, 259–272.

Table 5. Precision, Reproducibility, and Linearity precision (RSD%) of the instrument at three concentrations (v/v) (n ) 12)

precision (RSD%) of the method (n ) 12)

element

mass

0.100

0.500

1.000

intraday

interday

Li Be B Mg Mg Mg Al Ca Ca Ca Ti Ti V Cr Cr Mn Fe Fe Co Ni Ni Ni Cu Cu Zn Zn Zn Ga Ga Ge Ge As Se Se Se Br Rb Sr Y Zr Nb Mo Rua Rh Pd Ag Cda Sn Sb Tea I Cs Ba La Ce Pr Nd Sma Eu Gd Tba Dya Hoa Era Tma Yba Lua Hf a Taa W

7 9 11 24 25 26 27 42 43 44 47 49 51 52 53 55 56 57 59 60 61 62 63 65 66 67 68 69 71 72 73 75 77 78 82 79 85 88 89 90 93 95 101 103 105 107 111 118 121 125 127 133 137 139 140 141 146 147 153 157 159 163 165 166 169 172 175 178 181 182

22.00 51.10 19.50 8.57 9.41 10.48 9.85 10.70 11.26 9.76 17.88 16.43 16.49 13.26 12.80 15.90 6.97 7.85 13.40 13.60 13.37 15.12 10.65 11.27 13.23 6.48 5.88 21.12 22.97 6.69 45.20 26.86 10.72 5.07 9.04 3.11 3.18 13.24 31.20 29.16 30.79 27.18 126.97 34.61 50.40 31.86 105.18 13.99 14.69 62.84 25.11 16.01 16.60 20.91 11.67 36.68 52.96 108.71 52.99 52.49 46.98 101.34 61.19 80.19 68.26 62.13 89.49 66.81 69.63 21.67

16.78 35.94 7.79 4.87 5.13 8.17 4.42 4.45 10.18 9.01 7.85 13.17 9.68 5.33 9.78 6.94 4.18 5.36 7.15 7.22 8.13 10.06 5.18 6.22 4.40 1.54 1.63 9.52 7.96 4.87 26.81 9.10 5.63 1.68 7.20 5.23 2.63 3.23 19.25 14.28 26.15 15.97 74.69 28.40 15.82 44.41 91.68 8.03 7.22 46.20 5.03 5.60 8.16 12.72 6.01 29.37 25.65 58.90 32.74 36.23 41.03 57.46 47.03 53.30 78.36 51.68 83.41 40.40 49.88 13.15

13.20 67.94 5.89 5.79 5.54 5.71 5.90 5.59 6.37 6.31 5.39 8.90 5.69 4.78 5.05 4.56 4.01 4.09 6.44 3.76 4.48 9.43 2.75 2.46 2.10 2.43 1.42 6.18 4.89 3.72 24.96 4.63 3.63 2.44 5.75 3.28 1.90 2.35 14.53 8.32 16.96 10.82 67.94 18.35 16.50 9.52 77.18 8.53 5.90 54.49 2.83 4.55 4.98 10.19 5.95 21.35 20.93 48.53 22.16 33.22 35.95 68.08 39.20 51.11 82.52 47.54 83.15 37.44 46.67 11.22

17.25 64.66 11.62 3.08 3.16 2.84 17.59 3.16 2.13 2.42 5.22 10.48 13.18 7.40 12.94 9.33 7.45 8.94 9.51 17.31 5.96 15.11 2.27 1.50 7.20 7.30 6.61 16.05 6.94 3.78 25.96 11.71 6.18 3.89 11.56 8.73 1.17 4.12 23.13 16.73 19.62 14.94 124.09 25.71 14.19 15.26 62.35 9.38 8.99 54.17 7.66 3.66 6.74 15.42 9.06 25.85 26.34 57.30 33.00 44.60 41.31 45.42 55.67 55.91 91.80 57.86 89.32 69.87 60.53 14.60

17.82 58.63 14.94 5.75 8.94 6.26 19.37 5.27 6.74 4.16 9.19 11.01 12.54 9.60 8.36 9.77 7.91 8.85 12.86 9.41 25.38 49.26 6.57 8.45 5.63 8.29 6.40 15.04 8.98 10.02 29.20 13.34 12.16 9.58 14.22 9.46 6.69 4.91 28.24 20.16 20.16 15.66 69.61 28.26 20.02 27.12 64.82 12.16 10.83 49.97 10.16 7.26 15.14 21.65 11.14 28.98 24.97 76.70 37.31 44.19 40.25 64.88 63.25 50.90 89.17 74.64 78.90 76.63 62.52 23.54

correlationb coefficient 0.9761 0.6694 0.9985 0.9994 0.9989 0.9987 0.9863 0.9997 0.9998 0.9999 0.9996 0.9958 0.9972 0.9996 0.9998 0.9357 0.9978 0.9983 0.9938 0.9851 0.9896 0.8726 0.9997 0.9988 0.9718 0.9648 0.9575 0.9968 0.9866 0.9987 0.8749 0.9956 0.9991 0.9772 0.9987 0.9987 0.9969 0.9996 0.9901 0.8870 0.9281 0.9949 0.9402 0.9578 0.9897 0.9991 0.9984 0.9997 0.9995 0.9871 0.9756 0.9866 0.8047 0.8487 0.7825 0.9756

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Table 5. continued precision (RSD%) of the instrument at three concentrations (v/v) (n ) 12)

precision (RSD%) of the method (n ) 12)

element

mass

0.100

0.500

1.000

intraday

interday

Rea Osa Ira Pt Aua Hg Tl Pb Th U

185 189 193 195 197 202 205 208 232 238

84.03 88.91 96.92 21.92 95.51 35.75 40.78 20.95 35.30 44.31

96.17 113.16 94.90 18.02 56.55 18.26 20.87 10.58 12.92 31.16

86.54 78.45 85.58 11.31 50.65 15.31 16.91 6.48 13.31 25.70

65.21 98.37 98.92 19.57 59.18 25.99 24.70 16.81 28.84 16.52

65.92 100.29 121.59 17.66 48.96 27.34 28.19 10.68 34.98 21.45

correlationb coefficient

0.9948 0.9804 0.9739 0.9168 0.9236 0.9349

a The concentration of the element is lower than the limit of detection (LOD) which was calculated from the 3-fold standard deviation (SD) of the CPS value of corresponding element after 11 times continuous samplings of 2% HNO3. b Correlation coefficient was calculated in six consecutive concentration (v/v) gradients ranged from 0.100 to 1.000.

Figure 2. Score plot of the OPLS-DA model of ICP-MS data obtained from 21 female OA patients (red boxes) and 23 female HC (black dots). The x and y axis represent the serial number of the samples and the scores of individual samples in the predictive component, respectively. The serum samples of OA patients and HC have distinct separation in the predictive component of OPLS-DA score plot. It suggests that the serum of the OA patient must have significant abnormality in the trace element profile, some of which can be regarded as the potential element markers of the disease.

such as oxides, hydrides, argides, dimers, and doubly charged ions. Selection of the single-element standard solutions used in both perturbation and accuracy testing was also primarily based on the above reason. Both the purity of the standards and the elemental concentration in human serum must be considered when selecting both the “stimuli” and target elements in the perturbation test. Concentrations of the elements in a serum sample varied by 9 orders of magnitude, from high (1000 mg/L) to ultratrace (1ng/L) levels. For this reason, it was not advisable to assume that only the addition of less abundant elements could significantly interfere with the more abundant elements, and therefore, some elements (for example Ga) were not selected as the “stimuli” in perturbation test. Additionally, impurity concentrations in single-element standard solutions were only guaranteed at a level of below 1/10,000 of the principal element concentration of the standard. Therefore, addition of standard solution of (19) Hsiung, C. S.; Andrade, J. D.; Costa, R.; Ash, K. O. Clin. Chem. 1997, 43, 2303–2311.

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elements of higher abundance was more likely to interfere with an element of lower abundance. As a result of these factors and also taking into consideration the known concentration levels of human serum elements,13 elements of mass >82 amu were excluded from the target elements list in the perturbation test. The purpose of recovery testing was twofold. Testing was designed to determine both the impact of differing matrix and elemental compositions on the accuracy of individual measurement of elements (and their isotopes) and the extent and stability of interferences. This testing was performed using two pooled serum samples targeted for selection from multiple pooled serum samples. The samples were selected specifically based on significant differences in element composition as evidenced by cluster analysis of their ICP-MS data. As seen in Table 2, of the 14 targeted elements (and isotopes), at least one isotope of 10 of the 14 objective elements showed recoveries between 80% and 120%. Furthermore, these recoveries were stable between two different pooled serum samples at three concentration levels. Ca, Ti, Mn, and Zn demonstrated relatively low recoveries, especially at low concentration levels. Zinc is prone to interference because of its low concentration in blood and its highly abundant interference from 34S16O16O, 33S16O16OH, 32 16 18 S O O, and 32S34S.19 Nevertheless, consistent recoveries between the two pooled samples sets ensured reproducibility in relative quantification study. Optimization in mass detection of elements and estimation of the susceptibility of the method to interferences was achieved using perturbation tests. These tests we performed using high concentrations of elements which were specifically selected based on their ability to impact the quantitation of other elements, specifically through the formation of the interfering substances to “stimulate” the other potential objective elements in serum sample. The perturbation test can be regarded as a simple, intense, and quantitative simulation of serum samples analysis, the results of which directly reflect the reliability of the method. A Student’s t test was used to calculate the significance of changes in elemental concentration in the perturbation tests. Percent relative change (%RC) and p values from the Student’s t test were utilized to determine if masses of target elements were perturbed by the corresponding “stimuli” element. The criteria for perturbation was both a %RC value of >10% and a p value