Anal. Chem. 2007, 79, 5651-5658
Reproducibility Assessment of Relative Quantitation Strategies for LC-MS Based Proteomics Yeoun Jin Kim, Ping Zhan, Brian Feild, Steven M. Ruben, and Tao He*
Celera, 45 West Gude Drive, Rockville, Maryland 20850
The reproducibility of a given method for relative quantitation governs the reliability of liquid chromatographymass spectrometry (LC-MS) based differential analysis in proteomic studies. Understanding the noise level introduced from biological, chemical, and instrumental sources not only helps to determine the experimental design but also aids in assessing the reliability of expression ratios used for quantitation. Here we present a reproducibility assessment method for relative quantitation based on the intensity ratio distribution of common features in LC-MS replicates. This method applies to both decoupled (label-free quantitation) and coupled (labeldependent quantitation) methods. Aligning the features of LC-MS maps directly for the decoupled method or by matching an LC-MS map and its virtual map for the coupled method results in a list of common features for replicate samples. We find that the ratio distribution of the common features successfully indicates the reproducibility of each experiment prior to MS/MS peptide sequencing in three different quantitation strategies: decoupled, coupled isotope-coded affinity tag, and coupled stable isotope labeling of amino acids in cell culture experiments. Liquid chromatography-mass spectrometry (LC-MS) based proteomics uses relative peptide ion intensities measured by mass spectrometry to calculate expression levels of proteins.1 Compared to two-dimensional gel electrophoresis (2D gel) approaches, LCMS based methods offer more robust protein identification and quantitation, allowing easy automation and large-scale analysis with greater efficiency.1,2 Additionally, improvements in mass spectrometric instrumentation and software support permitted the emergence of LC-MS analysis as a major strategy in many proteomic applications such as therapeutic target discoveries, biomarker discoveries, and small-molecule drug development.3-6 Since the quantitation of a protein’s expression level relies * To whom correspondence should be addressed: (phone) 240-453-3715; (fax) 240-453-3978; (e-mail)
[email protected]. (1) MacCoss, M. J.; McDonald, W. H.; Saraf, A.; Sadygov, R.; Clark, J. M.; Tasto, J. J.; Gould, K. L.; Wolters, D.; Washburn, M.; Weiss, A.; Clark, J. I.; Yates, J. R., 3rd Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 7900-7905. (2) Gygi, S. P.; Corthals, G. L.; Zhang, Y.; Rochon, Y.; Aebersold, R. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 9390-9395. (3) Domon, B.; Broder, S. J. Proteome Res. 2004, 3, 253-260. (4) He, T.; Kim, Y. J.; Heidbrink, J. L.; Moore, P. A.; Ruben, S. M. Expert Opin. Drug Discovery 2006, 1, 477-489. 10.1021/ac070200u CCC: $37.00 Published on Web 06/20/2007
© 2007 American Chemical Society
exclusively on the mass spectrometric intensity of each peptide ion, the reproducibility of the intensity measurement remains the essential factor for reliable LC-MS based proteomics. The development of a series of stable isotope labeling methods has facilitated the use of peptide ion intensities for relative protein quantitation. These strategies include: cysteine labeling (ICAT, isotope-coded affinity tags),7 proteolytic 18O labeling,8 metabolic labeling (SILAC, stable isotope labeling by amino acids in cell culture),9 and tandem mass tagging (isobaric tags for relative and accurate quantitation).10,11 Although all four methods require labeling and pooling for relative quantitation, the level of reproducibility in quantitation may differ depending upon the stage of the labeling involved. In these experimental schemes (coupled methods), the relative quantitation is achieved by measuring the intensity ratio of light- and heavy-peptide ions detected in a combined LC-MS map. More recently, a direct comparison of peptide intensities across multiple samples without labeling and pooling has been tested and successfully applied to large-scale proteomic analyses.12,13 In this experimental scheme (decoupled method), each sample generates an independent LC-MS map (feature map). These maps are aligned based on an ion’s mass to charge ratio (m/z), retention time (tR), and charge state (z) to generate a matrix containing [m/z, tR, z] values as peptide identifiers with their corresponding ion intensities. Relative quantitation results from the comparison of a feature’s ion intensity across different LCMS maps following intensity normalization.14 (5) Qian, W. J.; Jacobs, J. M.; Liu, T.; Camp, D. G., 2nd; Smith, R. D. Mol. Cell. Proteomics 2006, 5, 1727-1744. (6) Aebersold, R.; Mann, M. Nature 2003, 422, 198-207. (7) Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994-999. (8) Yao, X.; Freas, A.; Ramirez, J.; Demirev, P. A.; Fenselau, C. Anal. Chem. 2001, 73, 2836-2842. (9) Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Mol. Cell. Proteomics 2002, 1, 376-386. (10) Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Mol. Cell. Proteomics 2004, 3, 1154-1169. (11) Zhang, Y.; Wolf-Yadlin, A.; Ross, P. L.; Pappin, D. J.; Rush, J.; Lauffenburger, D. A.; White, F. M. Mol. Cell. Proteomics 2005, 4, 1240-1250. (12) Chelius, D.; Bondarenko, P. V. J. Proteome Res. 2002, 1, 317-323. (13) Wang, J.; Barke, R. A.; Charboneau, R.; Loh, H. H.; Roy, S. J. Biol. Chem. 2003, 278, 37622-37631. (14) Anderle, M.; Roy, S.; Lin, H.; Becker, C.; Joho, K. Bioinformatics 2004, 20, 3575-3582.
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Figure 1. Sample preparation work flow for the three different experiments. (A) Decoupled experiment. Two sets of protein mixtures from T47D cells (samples 1 and 2) were labeled with ICAT-light and processed to generate LC-MS maps. (B) Coupled ICAT experiment. Proteins from T47D cells were labeled with ICAT-light (sample 3), and proteins in a second replicate were labeled with ICAT-heavy (sample 4). They were pooled before digestion and processed together to generate a combined LC-MS map. (C) Coupled SILAC experiment. T47D cells grown in media containing normal amino acids (sample 5) and T47D cells grown in heavy amino aicds (sample 6) were pooled before reduction and processed together to generate a combined LC-MS map.
The choice of experimental schemes among the previously mentioned methods depends on the biological or analytical requirements, or both, such as sample type (tissue, cell line, plasma, etc.), experiment size (multiple samples with statistical analysis or pairwise studies), and instrument capability (LC peak capacity, MS resolving power, computing power, etc.). Knowing the reproducibility of the system provides more confident expression ratios, which are used as criteria for differential analysis. An understanding of the system reproducibility can be achieved through the application of quality control processes in conjunction with a reproducibility assessment, which will minimize the number of false positives and negatives in differential analyses. Although some large-scale evaluations of reproducibility have reported CV values for expression ratios of identified peptides across replicate experiments,15,16 a lack of guidelines for displaying and assessing 5652
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reliability of the differential analysis remains an important issue in proteomic research.17 Here, we present a reproducibility assessment method based on ratio distribution analysis that can be applied to any of the aforementioned experimental schemes prior to differential analysis. The application of this method is demonstrated by utilizing (15) Molloy, M. P.; Donohoe, S.; Brzezinski, E. E.; Kilby, G. W.; Stevenson, T. I.; Baker, J. D.; Goodlett, D. R.; Gage, D. A. Proteomics 2005, 5, 12041208. (16) Old, W. M.; Meyer-Arendt, K.; Aveline-Wolf, L.; Pierce, K. G.; Mendoza, A.; Sevinsky, J. R.; Resing, K. A.; Ahn, N. G. Mol. Cell. Proteomics 2005, 4, 1487-1502. (17) Wilkins, M. R.; Appel, R. D.; Van, Eyk, J. E.; Chung, M. C.; Gorg, A.; Hecker, M.; Huber, L. A.; Langen, H.; Link, A. J.; Paik, Y. K.; Patterson, S. D.; Pennington, S. R.; Rabilloud, T.; Simpson, R. J.; Weiss, W.; Dunn, M. J. Proteomics 2006, 6, 4-8.
three commonly practiced strategies in quantitative proteomic analysis: decoupled, coupled ICAT, and SILAC methods. EXPERIMENTAL SECTION All reagents and solvents were purchased from VWR (West Chester, PA) unless otherwise specified. Cell Lysate Preparation. T47D human breast cell carcinoma cells were purchased from the American Type Culture Collection (ATCC CRL-1427). SILAC labeling medium (RPMI 1640) lacking lysine and arginine from Invitrogen (Carlsbad, CA) was supplemented with either heavy amino acids ([13C6]-Arg and [13C6]-Lys) (Cambridge Isotope laboratories Inc, Andover, MA) or light amino acids, and 10% dialyzed fetal bovine serum was added to both light and heavy media. Cells were grown at 37 °C with 5% carbon dioxide in air and harvested when they reached 70-90% confluency. Next, cells were lysed and proteins were precipitated by adding cold acetone in a ratio of 1:4 (v/v) sample/acetone for 2 h at -30 °C. Peptide Preparation. Three separate peptide preparation schemes were implemented depending upon the labeling method (Figure 1A-C). In the decoupled method, protein pellets from two independent T47D cell cultures were reconstituted with 6 M guanadine hydrochloride, 100 mM Tris, pH 8.0 to prepare 1 mg/ mL for each sample (Figure 1A). Proteins were reduced in 2.5 mM dithiothreitol (DTT) (BioRad, Hercules, CA) for 1 h at 37 °C and alkylated with ICAT light reagents (C0) according to the procedures recommended by the manufacturer (Applied Biosystems, Framingham, MA). The reaction was quenched by adding excess DTT. In the coupled ICAT method, two protein pellets from T47D cell lysates were reconstituted in 6 M guanadine hydrochloride, 100 mM Tris, pH 8.0 to a concentration of 1 mg/mL (Figure 1B). Following reduction (described above), one replicate was labeled using C0 while the other was labeled using ICAT heavy reagent (C9) according to the manufacturer’s protocol. The reaction was quenched by adding excess DTT, and the two samples were pooled to give a 1:1 (C0/C9) mixture based on protein content. In the coupled SILAC method, protein pellets from the heavy amino acid labeled T47D cell lysate (SILAC heavy) and SILAC light sample were pooled to give a 1:1 (SILAC light/SILAC heavy) ratio based on protein content (Figure 1C). Following reduction, proteins were alkylated with C0 according to the procedures recommended by the manufacturer. The reaction was quenched by adding excess DTT. All three preparations followed the same method for tryptic digestion. To remove excess ICAT reagent, and to reduce the concentration of guanidine hydrochloride, samples were centrifuged in 10 000 MW Centricon centrifugal filter devices (Millipore, Bedford, MA), passing 3 mL of 0.6 M guanidine hydrochloride (100 mM Tris, pH 8.0) through the filter three times before collecting the retentate. Proteins were digested overnight at 37 °C using sequencing grade, modified trypsin (Promega, Madison, WI) with an enzyme to substrate ratio of 1:25. Tryptic digests were desalted using 3-cm3 Oasis HLB solid-phase extraction columns (Waters, Milford, MA) and dried in vacuo. Cysteine Capture and Sample Cleanup. Peptides were reconstituted in a solution of 10% acetonitrile in PBS. Peptides were loaded onto an avidin column (2 mL, Applied Biosystems, Foster City, CA) using a Vision workstation system (Applied
Biosystems) with PBS. The avidin column was washed with 50 mM ammonium bicarbonate (EMD, Gibbstown, NJ), 20% methanol, followed by a water wash. Non-cysteine-containing peptides were washed onto an R2/10 column (4.6 × 50 mm, Applied Biosystems) with 5% acetonitrile in 0.1% TFA and eluted to the fraction collector with a step gradient to 95% acetonitrile, 0.1% TFA. The cysteine-containing peptides were eluted using 50% acetonitrile, 0.1% TFA (Pierce, Rockford, IL), collected, and vacuum-dried. Captured ICAT-labeled peptides were cleaved as described in the protocol provided by Applied Biosystems. Excess and cleaved ICAT reagent was removed using the same avidin/R2 cleanup described above. The R2/10 fractions (cysteine-containing peptides) were dried in vacuo and stored at -80 °C before LC-MS analysis. LC-MS and Feature Detection. Peptides were reconstituted in buffer A (0.1% formic acid in water) and separated over a C18 monomeric column (150 mm, 150-µm i.d., Grace Vydac 238EV5, 5 µm) at a flow rate of 1.5 µL/min in an Agilent 1100 HPLC system (Agilent Technologies, Palo Alto, CA). Internal standards (synthetic peptides) were added for mass calibration and retention time alignment. These internal standards are non-cysteine-containing peptides derived from human serum albumin and keratin with retention times evenly distributed over the 5-h gradient. Samples were loaded on to the trap column (50 mm, 150-µm i.d., Grace Vydac 238EV5, 5 µm) and washed for 10 min using 3% buffer B (0.1% formic acid in 90% acetonitrile). Peptides were eluted from the column using a gradient, 3-30% buffer B in 215 min, 30-90% buffer B in 30 min. Eluted peptides were analyzed using an online QSTAR XL (MDS/Sciex, Toronto, ON, Canada) equipped with electrospray ionization source. Peptide maps were generated using a 3-s cycle time, and data were collected over the mass range of 400-1500 amu. LC-MS wiff files were transformed to the list of features18,19 using ReSpect (PPL, Isleham, UK).20,21 The features detected at the highest confidence level were used in this study. Outliers in the coupled experiments were subjected to LC-MS/ MS analysis for peptide sequencing. Data Alignment. For decoupled analysis, the features of each LC-MS map were aligned based on their m/z, tR, and z. The retention times for the features in each map were adjusted to those of a reference map using a linear function determined by applying a least-squares fit based on the retention times for the 12 internal standard ions in each LC-MS map (Supporting Information Table 1). Ions in all the maps were grouped together, limiting feature matching by using a 2-min tR window and a 30 ppm m/z window. Aligned features were imported into Spotfire (Spotfire Inc. Somerville, MA), and intensities were normalized over all samples. For coupled experiments, an additional virtual map was generated by applying the equation (18) Listgarten, J.; Emili, A. Mol. Cell. Proteomics 2005, 4, 419-434. (19) Radulovic, D.; Jelveh, S.; Ryu, S.; Hamilton, T. G.; Foss, E.; Mao, Y.; Emili, A. Mol. Cell. Proteomics 2004, 3, 984-997. (20) Ferrige, A. G.; Ray, S.; Alecio, R.; Ye, S.; Waddell, K. Proceedings of the 51st ASMS Conference on Mass Spectrom. and Allied Topics 2003. (21) Ferrige, T. G.; Alecio, R.; Ray, S.; Pannell, L. K. Proceedings of the 54th ASMS Conference on Mass Spectrom. and Allied Topics 2006. (22) Efron, B.; Tibshirani, R. J. An Introduction to the Bootstrap; Chapman & Hall/CRC, 1993.
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[m/z]virtual ) [m/z]original - ∆mass/z (∆mass ) 9.03 Da for heavy ICAT, 6.02 Da for heavy SILAC) The original map and its corresponding virtual map were aligned using the same procedure that was used in the decoupled method. In this case, a narrower tR window was used since the retention time for the aligned features should be identical (tR window ) 0.2 min, m/z window ) 30 ppm). Ratio Distribution Analysis and Hypothesis Test. After data alignment and intensity normalization, intensity ratios for common features from two LC-MS maps in the matrix were calculated. Boxplots of the ratio distributions were generated for the decoupled, coupled ICAT, and coupled SILAC data sets. Extreme ratios (outliers) were excluded sequentially from the two tails of the distribution until a normal distribution was achieved.23 To compare the reproducibility of the decoupled and ICAT data sets to the SILAC data set, the 95% confidence interval (CI) of the median (50th percentile) was used to gauge the variance of the ratio distributions. Also, the 75th and the 95th percentiles were considered in order to cover the entire distribution. To assess the significance of the test, the bootstrap method22 was applied by resampling 1000 features without replacement 1000 times. The results established distributions of the 95% confidence intervals of a desired percentile for each method. For statistical analysis purposes, a null hypothesis was created stating that median of 95% CIs of a desired percentile of SILAC is equal to that of ICAT or decoupled methods. A p-value is defined as the percentage of CIs of SILAC that are more extreme than median CI of ICAT or decoupled methods. A p-value less than 0.05 is considered to be significantly different when two methods are compared. All statistical analyses were performed using S-Plus windows version 7.0 (Insightful, Seattle, WA). RESULTS AND DISCUSSION The reproducibility assessment method in this study includes feature alignment of LC-MS maps, intensity matrix generation, and statistical analysis of intensity ratios in the matrix. Considering the intrinsic difference between the coupled and decoupled quantitation strategies, we designed separate yet consistent processes for both methods (Figure 2). Automatic feature detection software, ReSpect,20,21 transformed the LC-MS raw data into lists of features represented by [m/z, tR, z]. The retention times of features in each map were normalized, and the evaluation of retention time alignment is included in the Supporting Information Table 1 and Supporting Information Figure 1. Alignment of two feature maps occurs by comparing the [m/z, corrected tR, z] values and grouping the features that match (common features). This process generates an intensity matrix containing [m/z, tR, z] values and the corresponding intensities from different LC-MS feature maps. Whereas, in the decoupled experiment, the lists of features from various samples are aligned directly, the coupled experiments required the generation of virtual maps prior to alignment (details are discussed in the coupled discussion sections). A boxplot was generated using the final data set of ratios, and the reliable ratio for quantitation was determined based on the summary statistics. (23) Yang, I. V.; Chen, E.; Hasseman, J. P.; Liang, W.; Frank, B. C.; Wang, S.; Sharov, V.; Saeed, A. I.; White, J.; Li, J.; Lee, N. H.; Yeatman, T. J.; Quackenbush, J. Genome Biol. 2002, 3, research0062.1-0062.12.
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Figure 2. Flow chart of process for reproducibility assessment.
Breast cancer cell line T47D was used to test both the coupled and decoupled strategies (Figure 1). Two identical but independently grown cell cultures were used for the decoupled method. For the coupled strategies, ICAT and SILAC methods were chosen as a representative of the chemical labeling and an early-stage amino acid labeling, respectively. Although the SILAC and decoupled methods do not require ICAT labeling for quantitation, ICAT enrichment was used in all three methods to standardize the processes for comparison studies and to reduce the complexity of the samples. Reproducibility Assessment in Decoupled Experiment. The decoupled method in this study refers to the proteomic quantitation strategy that measures mass spectrometric intensities of peptide ions across multiple samples without labeling and pooling. System variability is especially a concern for the decoupled method because peptide ion intensities are compared across multiple LC-MS maps. For the decoupled method, an assessment of reproducibility can then be used for determining the minimum reliable ratio of peptide/protein levels for further quantitative analysis. Cysteine-containing peptides from the tryptic digest were enriched by affinity chromatography, and LC-MS maps of the peptide mixtures from each sample were generated (samples 1 and 2, Figure 1A). The feature lists of sample 1 and sample 2 were aligned to generate an intensity matrix. Outlying features were excluded to achieve a normal distribution of the ratios in each data set. The normal distribution allows a confidence interval comparison based on the standard deviation to be applied.23 Figure 3 shows the scatter plot of log2-transformed intensities (log2 Int) of the common features in the decoupled experiment matrix (sample 1 vs sample 2) before filtration. Log2 ratios (ratio ) ion
Figure 3. Scatter plot of log2 Int for decoupled samples. The boxplot diagram represents the distribution of the log2 Ratios of all common features found in the scatter plot after discarding outlying features. The box and the bars represent (1SD and (2SD ranges, respectively, the center line shows the mean value, and all the data points outside of these ranges are shown with circles. Summary statistics were included: 2SDUL (upper limit of 2SD from the mean), 2SDLL (lower limit of 2SD from the mean), mean, 1SDUL, and 1SDLL in linear scale.
Figure 4. Alignment of the coupled samples using a virtual map. The virtual map was generated by subtracting a mass difference of the stable isotope label, from the original masses of the features. The retention times remained the same. The original map and the virtual map are aligned to determine common features. The common features represent heavy and light pairs in the coupled experiments.
intensity in sample 1/ion intensity in sample 2) of all common features were calculated. A total of 5489 common features were subjected to distribution analysis. The summary statistics are shown with the boxplot diagram. For the ratio distribution of the decoupled replicates, 2SDUL (upper limit of 2 standard deviations (SD) from the mean), 2SDLL (lower limit of 2SD from the mean), and mean values in linear scale are shown in Figure 3. This reproducibility assessment indicates that 95% of the total common features have intensities within an ∼2-fold difference. Therefore, in further differential analyses, any difference in expression ratio that is above 2-fold may be considered as differentially expressed with 95% confidence. The statistical analysis of the data as visualized in the boxplot provides valuable guidance for reproducibility control and further differential analysis. The ratio distribution can be displayed using an MA plot (Supporting Information Figure 2). Reproducibility Assessment in Coupled Experiment: ICAT. In the coupled experiments, experimental errors and variation of ionization efficiency in mass spectrometry apply equally to the pairs of peptides. Therefore, the reproducibility of the process can be controlled from the point of pooling the samples, creating higher reproducibility compared to the decoupled experiment.7-10 Coupled methods are often preferred in cases that require higher reproducibility or that involve multiple steps of sample processing.
However, the reproducibility assessment process we described in the previous section is not directly applicable to coupled experiments because light and heavy peptides are present in the same sample. In order to align peptides with different labels, a virtual map was generated by subtracting the calculated m/z of the label from the features in the original map. The features in the original map that represent the light-labeled sample now align with the features in the virtual map simulating the heavy-labeled sample, to generate a feature matrix (Figure 4). A narrower retention time window compared to the decoupled experiment was used for the alignment since the retention times for the common features in the coupled ICAT experiment are identical. For the coupled ICAT experiment, proteins from two separate cultures of T47D cells were labeled with reagents that contain different stable isotopes, ICAT-light (sample 3) and ICAT-heavy (sample 4), to incorporate a 9.03-Da difference between the molecular mass of cysteine-containing peptides in these samples (Figure 1B). The two labeled samples were pooled before digestion and further processed to generate a combined LC-MS map. The feature list of this map, therefore, contains pairs of peptide ions originating from the two samples. A virtual map was generated by subtracting 9.03 Da/z, the m/z difference between ICAT-heavy and -light, from all features in the original map. Figure 5 shows the scatter plot of log2 (intensity) of the common features between the original and virtual maps aligned in the ICAT experiment. With a total of 1306 common features, it should be noted that the number of common features is reduced significantly compared to the decoupled experiment. Fewer chromatographic peaks, increased complexity of coupled samples leading to ionization competition, or decreased confidence in detected features may contribute to the lower number of features in the coupled experiment.24,25 Additionally, the alignment algorithm for coupled experiments created false negatives from its inability to include multiply labeled peptides, thus eliminating all multi-cysteine-containing peptides from the alignment. (24) Cole, R. B. J. Mass Spectrom. 2000, 35, 763-772. (25) Tang, K.; Page, J. S.; Smith, R. D. J. Am. Soc. Mass Spectrom. 2004, 15, 1416-1423.
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Figure 5. Scatter plot of log2 Int of coupled ICAT samples (original map vs virtual map with 9.03 Da) with corresponding boxplot diagram and summary statistics.
Figure 6. Scatter plot of log2 Int of coupled SILAC samples (original map vs virtual map with 6.02 Da) with corresponding boxplot diagram and summary statistics.
The reproducibility assessment result in the summary statistics for the ICAT experiment clearly illustrates a tighter distribution and greater reproducibility in comparison to the decoupled experiment, based on 2SDUL (1.41 ICAT, 1.98 decoupled) and 2SDLL (0.72 ICAT, 0.54 decoupled) with 95% confidence (Figure 5). The virtual map alignment method demonstrated here can be applied to any types of coupled experiments with equivalent modifications of ∆ m/z (e.g., ∆ m/z ) 4.009 for proteolytic 18O labeling). Reproducibility Assessment in Coupled Experiment: SILAC. In the SILAC strategy, since labeled samples can be combined at an early stage in sample preparation, relatively higher reproducibility compared to peptide labeling or the decoupled method has been reported.9 In the present SILAC study, one set of T47D cells was grown in medium containing normal amino acids to prepare the SILAC-light sample (sample 5, Figure 1C); the other set of cells was grown in medium containing heavy amino acids to prepare the SILAC-heavy sample (sample 6). Based on the measurement of mass spectrometric intensity, the incorporation rate of SILAC-heavy labeling in the heavy sample was over 95%. The same amount of protein from each sample was pooled and further processed together to generate a combined LC-MS map. Since all tryptic peptides contain one lysine (K) or arginine (R) at the C-terminus, (with the exception of K/R residues 5656 Analytical Chemistry, Vol. 79, No. 15, August 1, 2007
adjacent to proline), the feature list generated from the map contains a mixture of peptide pairs with a 6.02-Da difference. A virtual map was generated from the coupled LC-MS map by subtracting 6.02 Da/z. Figure 6 shows the scatter plot of log2 (intensity) of the common features between the original and virtual maps. The number of common features was 1335, similar to the number of common features found in the coupled ICAT experiment. The reproducibility assessment result based on the summary statistics in Figure 6 show slightly increased reproducibility compared to the coupled ICAT experiment. At the 95% confidence level, 2SDUL and 2SDLL values were 1.33 and 0.74, respectively, in linear scale, compared to 1.41 and 0.72 for ICAT, respectively. Interestingly, outliers with more extreme ratios were detected in the scatter plot of the SILAC experiment before the filtration, compared to the ICAT experiments (Figure 5). For further investigation, a set of outliers was subjected to LC-MS/MS sequencing. Of 15 identified peptides, 14 peptides were multi-K/ R-containing peptides (8 of them contain K/RP motif). For example, the sequence of the peptide, VGEEFEEQTVDGRPCK, contains two labeled amino acids, and the correct pairing of this peptide should have a difference of 12.04 Da, as shown in the mass spectrum in Figure 7. However, the partially labeled peptide peak is also detected between the peptide pair even though the incorporation rate of SILAC-heavy labeling in this experiment
Table 1. Summary of log2 Ratio Distributions from Different Methods tailinga
common features experiment
number
2×SDUL
1×SDUL
mean
1×SDLL
2×SDLL
>2SD
>1SD
decoupled ICAT SILAC
5489 1306 1503
1.98 1.41 1.33
1.43 1.19 1.15
1.03 1.00 0.99
0.75 0.85 0.86
0.54 0.72 0.74
0.049 0.048 0.049
0.33 0.33 0.33
a
Percentage of features outside of 2SD and 1SD indicating normal distribution is obtained.
Figure 7. False positive pairing in SILAC analysis. The MS1 spectrum of one of the outliers circled in the scatter plot is inserted. The MS1 spectrum of this pair confirms a false matching. The peptide sequence of this feature is shown below the spectrum as a series of differently labeled states. The open circle indicates a peptide with two light amino acids, the gray circle indicates the peptide partially labeled with heavy amino acids (combination of two possible sequences), and the dark circle indicates the completely labeled peptide. Table 2. Summary of log2 Ratio Distributions Using Common Features in Combined Matrix tailinga
common features experiment
number
2×SDUL
1×SDUL
mean
1×SDLL
2×SDLL
>2SD
>1SD
decoupled ICAT SILAC
946 762 804
1.69 1.41 1.38
1.30 1.19 1.17
1.00 1.00 1.00
0.77 0.84 0.85
0.59 0.71 0.73
0.050 0.055 0.051
0.32 0.30 0.32
a
Percentage of features outside of 2SD and 1SD indicating normal distribution is obtained.
(>95%) is an acceptable rate for quantitative SILAC experiments.26 Complete digestion may reduce the number of outliers, and the number of outliers in the scatter plot can provide useful information about proteolysis efficiency. It is important to note that these outliers have been systematically eliminated in the distribution analysis shown in Figure 6 in order to achieve a normal data distribution, allowing more accurate assessment of reproducibility. For differential analysis, using process replicates in addition to the paired sample can eliminate the false positives derived from RP/KP-containing peptides. Comparison of Quantitation Reproducibility across the Different Strategies. Among the various quantitation strategies in the proteomics field, the choice of the method depends on the biological, analytical, and financial constraints of the research. The level of reproducibility is one of the most important criteria to (26) Gruhler, A.; Schulze, W. X.; Matthiesen, R.; Mann, M.; Jensen, O. N. Mol. Cell. Proteomics 2005, 4, 1697-1709.
consider for the various biological systems under investigation. Here we show an analytical example that compares the level of reproducibility across three different strategies. The statistics of ratio distributions acquired independently in each experiment are summarized in Table 1. The histogram (Supporting Information Figure 3) and low percentage of features outside of 2SD and 1SD (tailing) in the table indicate that normal distributions are obtained. The 95% confidence interval of the mean, which is shown by 2SDUL and 2SDLL, increases in order of SILAC, coupled ICAT, and decoupled. As discussed in the coupled ICAT section, the numbers of features used in the analyses are not consistent between the coupled and decoupled experiments. The variability in data size should not affect the final result since the ratio distribution in each data set follows a normal distribution. Furthermore, the linearities of the quantile-quantile plots of map intensity distributions suggest that the features in the decoupled method are not skewed Analytical Chemistry, Vol. 79, No. 15, August 1, 2007
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Table 3. Summary of Quantile Confidence Interval Study and Significance Assessment median of 95% CI
significance (p-value)
no. of intervals of SILAC
quantile
SILAC
ICAT
decoupled
> median interval of ICAT
50th 75th 95th
0.0166 0.0275 0.0461
0.0237 0.0335 0.0569
0.0544 0.0637 0.134
0 5 2
toward low intensities (Supporting Information Figure 4). Therefore, it is unlikely that the observed larger variations of ratios in the decoupled method arise owing to variations of intensity distribution among the three methods. Nevertheless, it is desirable to compare the methods using similar data sizes. A two-pronged approach was undertaken to avoid any possible bias generated by different data sizes and different intensity distributions. First, the ratio distribution analysis was repeated using common features that overlap across the three experiments. All six maps (two decoupled replicates, pairs of ICAT and SILAC experiments) were aligned to generate one comprehensive matrix. The statistics of ratio distributions using common features that overlap across the three experiments after filtration are summarized in Table 2. The same trend of reproducibility was obtained in this analysis as using independent matrices. Second, a bootstrap analysis was performed to minimize the potential influence of using a variable data set size as well as to assess the significance in the difference of the ratio distributions (Table 3). The median of 95% confidence interval for the 50th percentile increased in the order of SILAC, coupled ICAT, and decoupled method, showing the same trend found in Table 1. The null hypotheses based on p-value calculations were rejected in both ICAT and decoupled cases, indicating that SILAC has a tighter ratio distribution than the ICAT or the decoupled methods. Comparison at the 75th and 95th percentile follows the same trend as at the 50th percentile. The comparison study employing the reproducibility assessment method concluded that the SILAC experiment is most advantageous for studies that require greater precision in differential analysis. CONCLUSION Successful differential analysis in LC-MS based proteomics hinges on the reproducibility of the platform. Developing a reliable reproducibility assessment method for LC-MS data is essential
5658 Analytical Chemistry, Vol. 79, No. 15, August 1, 2007
> median interval of decoupled
SILAC vs ICAT
SILAC vs decoupled
0 0 0
< 0.001 0.005 0.002
< 0.001 < 0.001 < 0.001
for process quality control. We believe the approach described in this study could serve as a QC method for assessing the level of reproducibility between process replicates. Generating log2 intensity ratios of common features in LC-MS replicates enables ratio distribution analysis. Statistical analysis and its graphic representation offer a tool to assess, compare, and visualize the reproducibility of complex biological samples. The creation of “virtual maps” for feature alignment of the coupled experiments (pairing of heavy and light isotopically labeled common features) provides the capability to align features from coupled samples for ratio distribution analysis. Therefore, this method is compatible with coupled and decoupled quantitation strategies and can be conducted before peptide identification. The assessment result can provide guidance in determining the minimal ratios for future differential analysis. ACKNOWLEDGMENT The authors gratefully acknowledge Dr. Elizabeth Joseloff and Chad Danis for preparing T47D cells, Tony Major and Jeff Bobish for modifying quantitation software for the coupled experiments, and Drs. Jenny Heidbrink and Patrick Kaminker for their critical review of the manuscript. SUPPORTING INFORMATION AVAILABLE (1) Table 1: properties of internal standard peptide ions and their retention times before and after retention time alignment. (2) Figure 1: correlation of aligned retention times for internal standard ions (sample 1 vs sample 2). (3) Figure 2: MA plots of three experiments. (4) Figure 3: evaluation of normal distribution of features after eliminating outliers. (5) Figure 4: quantilequantile plots showing features in the decoupled method are not skewed at low intensities. This material is available free of charge via the Internet at http://pubs.acs.org. AC070200U
