Precise protein quantification based on peptide quantification using iTRAQ

Abstract

Background: Mass spectrometry based quantification of peptides can be performed using the iTRAQ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.

Results: A method comprising sound error estimation and statistical methods is presented that allows precise abundance analysis plus error calculation at the peptide as well as at the protein level. This yields the relevant information that is required for quantitative proteomics. Comparing the performance of our method named Quant with existing approaches the error estimation is reliable and offers information for precise bioinformatic models. Quant is shown to generate results that are consistent with those produced by ProQuant, thus validating both systems. Moreover, the results are consistent with that of Mascot 2.2. The MATLAB scripts of Quant are freely available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/, each under the GNU Lesser General Public License.

Conclusion: The software Quant demonstrates improvements in protein quantification using iTRAQ. Precise quantification data can be obtained at the protein level when using error propagation and adequate visualization. Quant integrates both and additionally provides the possibility to obtain more reliable results by calculation of wise quality measures. Peak area integration has been replaced by sum of intensities, yielding more reliable quantification results. Additionally, Quant allows the combination of quantitative information obtained by iTRAQ with peptide and protein identifications from popular tandem MS identification tools. Hence Quant is a useful tool for the proteomics community and may help improving analysis of proteomic experimental data. In addition, we have shown that a lognormal distribution fits the data of mass spectrometry based relative peptide quantification.

Figure 1

Chemical structure of the iTRAQ™ reagent. The label is composed of a peptide reactive group (red, NHS ester) and an isobaric tag of 145 Da, which consists of a balancer group (blue, carbonyl group) and a reporter group (green, N-methylpiperazine). The four available tags of identical overall mass vary in their stable isotope compositions such that the reporter group has a mass of 114–117 Da and the balancer of 28–31 Da. The fragmentation site between the balancer and the reporter group is responsible for the generation of the reporter ions in the region of 114–117 m/z.

Figure 2

The normal-probability-plot shows that a lognormal distribution fits the peptide ratio data. The transformed experimental data is plotted and lies on a line, so the data is nearly normally distributed. The x-axis denotes the inverse function of the normality and the y-axis represents the sorted log-transformed values.

Figure 3

The example peaks of two labels A and B are depicted. The area of the peaks is not proportional to the sum of intensities if peak distances and peak count are not equal. This has effects on the quantification results yielding notable differences. The summed intensities of the example above are 15.0000 and 20.000, respectively. The trapezoid integrals amount to 1.500 (A) and 2.000 (B). The corresponding ratios are 1.3333 (summed) and 1.3333 (area). Suppose an additional peak at 115.0600 m/z with an intensity of 7.6000. Then, the area of B would be the identical, whereas the summed intensities will change to 27.6000, yielding a ratio of 1.8400. This yields a difference in relative quantification of 38%. In the former case, the ratio would not reflect the ion count of the three peaks detected by the mass spectrometer, but the latter does as the intensity of each signal represents the amount of ions detected and counted by the mass spectrometer.

Figure 4

The amino acid sequence of the protein bovine serum albumin (P02769) is depicted as an example for sequence coverage. The uniquely identified peptide sequences are marked in red, whereas the blue marked regions are confirmed by non-unique peptides. The sequence coverage of the example shown above is 33.61%, the covered mass is 33.27%.

Figure 5

Quantification results of the protein BGAL_ECOLI (P00722). Samples were mixed in a ratio of 1:1. Figure a) shows the standard boxplot of the peptide ratios. The median is 1.0508. Figure b) depicts the protein ratio calculated by the LSE value of the single peptide ratios, 0.9106. The red line indicates the LSE value, i.e. the protein ratio calculated from the relative peptide abundances. The blue crosses mark the corresponding errors of each peptide ratio, the red ones the peptide ratios.

Figure 6

Quantification results of the protein TRFE_HUMAN (P02787). Samples were mixed in a ratio of 1:1. Figure a) shows the standard boxplot of the peptide ratios. The median is 0.9984. Outliers are marked in red. Figure b) depicts the protein ratio calculated by the LSE value of the single peptide ratios, 0.9673. The red line indicates the LSE value, i.e. the protein ratio calculated from the relative peptide abundances. The blue crosses mark the corresponding errors of each peptide ratio, the red ones the peptide ratios.

Figure 7

Quantification results of the protein ALBU_BOVIN (P02769). Samples were mixed in a ratio of 1:3. Figure a) shows the standard boxplot of the peptide ratios. The median is 0.9424. Figure b) depicts the protein ratio calculated by the LSE value of the single peptide ratios, 0.9742. The red line indicates the LSE value, i.e. the protein ratio calculated from the relative peptide abundances. The blue crosses mark the corresponding errors of each peptide ratio, the red ones the peptide ratios.

Figure 8

Quantification results of the protein BGAL_ECOLI (P00722). Samples were mixed in a ratio of 1:3. The sequence of the outlying peptide 7 is APLDNDIGVSEATR with a ratio of 1.5171 ± 0.0121. Figure a) shows the standard boxplot of the peptide ratios. The median is 0.9674. Outliers are marked in red. They not distort the calculation of the protein ratio. Figure b) depicts the protein ratio calculated by the LSE value of the single peptide ratios, 0.9936. The red line indicates the LSE value, i.e. the protein ratio calculated from the relative peptide abundances. The blue crosses mark the corresponding errors of each peptide ratio, the red ones the peptide ratios.

Figure 9

Quantification results of the protein TRFE_HUMAN (P02787). Samples were mixed in a ratio of 1:3. Figure a) shows the standard boxplot of the peptide ratios. The median is 1.0511. Figure b) depicts the protein ratio calculated by the LSE value of the single peptide ratios, 1.0526. The red line indicates the LSE value, i.e. the protein ratio calculated from the relative peptide abundances. The blue crosses mark the corresponding errors of each peptide ratio, the red ones the peptide ratios.