Quantitative Proteome Analysis in Cardiovascular Physiology and Pathology. I. Data Processing Thomas Grussenmeyer,*,† Silvia Meili-Butz,‡ Thomas Dieterle,‡ Emmanuel Traunecker,† Thierry P. Carrel,†,§ and Ivan Lefkovits† Department of Biomedicine and Department of Cardiac Surgery, University Hospital Basel, Switzerland, Department of Biomedicine and Cardiobiology Laboratories, University Hospital Basel, Switzerland, and Bern University Hospital, Clinic for Cardiovascular Surgery, Switzerland Received July 14, 2008
Methodological evaluation of the proteomic analysis of cardiovascular-tissue material has been performed with a special emphasis on establishing examinations that allow reliable quantitative analysis of silver-stained readouts. Reliability, reproducibility, robustness and linearity were addressed and clarified. In addition, several types of normalization procedures were evaluated and new approaches are proposed. It has been found that the silver-stained readout offers a convenient approach for quantitation if a linear range for gel loading is defined. In addition, a broad range of a 10-fold input (loading 20-200 µg per gel) fulfills the linearity criteria, although at the lowest input (20 µg) a portion of protein species will remain undetected. The method is reliable and reproducible within a range of 65-200 µg input. The normalization procedure using the sum of all spot intensities from a silver-stained 2D pattern has been shown to be less reliable than other approaches, namely, normalization through median or through involvement of interquartile range. A special refinement of the normalization through virtual segmentation of pattern, and calculation of normalization factor for each stratum provides highly satisfactory results. The presented results not only provide evidence for the usefulness of silver-stained gels for quantitative evaluation, but they are directly applicable to the research endeavor of monitoring alterations in cardiovascular pathophysiology. Keywords: Proteome • two-dimensional gel electrophoresis • silver-staining • quantitation • normalization • cardiac muscle • heart • Dahl rat • Wistar rat
Introduction In our quest for defining biomarkers of cardiovascular pathologies, we have focused on establishing dynamic alterations in the expression of gene products during time kinetic processes, which in the presented studies were processes of inducing pathological alterations in animal models. From our earlier work,1-5 and findings of other authors 6-8 we realized that beside all-or-none alterations (i.e., absence of the marker in the healthy individual and presence at the disease state, or vice versa) there are processes of up- or down-regulation, in which the changes are of quantitative nature. Proteomic studies are uniquely suited in following multiple changes. Our ISODALT system is capable of handling simultaneously 20 samples both at the isoeletric focusing stage and at the electrophoretic size separation, and allows a straight* To whom correspondence should be addressed. Thomas Grussenmeyer, University Hospital Basel, Department of Biomedicine, Hebelstrasse 20, CH 4031 Basel, Switzerland. Phone: +41 61 265 3225. E-mail: t.grussenmeyer@ unibas.ch. † Department of Biomedicine and Department of Cardiac Surgery, University Hospital Basel. ‡ Department of Biomedicine and Cardiobiology Laboratories, University Hospital Basel. § Bern University Hospital. 10.1021/pr8005292 CCC: $40.75
2008 American Chemical Society
forward visualization of up- and down-regulatory processes. There are, however, some obstacles in retrieving “reliable” data sets. The problems can be defined as follows: • the proteomic technique has to allow parallel sample processing • the proteomic image analysis has to provide satisfactory spot modeling • the proteomic readout has to yield reliable quantitation • the procedure should be usable both for abundant and rare proteins • the output should distinguish from true quantitative changes from artifacts (improper quantitation of spots on streaks, wrong assignment of spots due to mismatches in tide spot clusters, etc.) In our experience, if the proteomic readout is based on metabolic labeling, for example, 35S-methionine incorporation,1,9 both the spot modeling and the quantitation yield adequate results. The scientific community for various reasons considers staining procedures less reliable. This is clear from the first principles of the readout: Low energy beta emitters (35S, 14C, 3H) yield autoradiographic images that within a certain range of film exposure offer Journal of Proteome Research 2008, 7, 5211–5220 5211 Published on Web 10/25/2008
research articles reliable quantitation, since the grayness level of the film is proportional to the absolute number of beta-emitting atoms. Binding of Coomassie blue dye to polypeptide molecules is to some extent proportional to the amount of material, and allows quantitation with the range of stainability. Deposition of silver atoms on polypeptide molecules as a result of a chemical reaction of reduction of Ag+ ion is not fully understood, and various aspects (like negative staining, or color alterations) prompt the experimenter to some caution.10,11 While elaborating our projects related to the comparison of “constitutive proteome” versus “turnover proteome” of human trabeculae,12 we believe that, upon certain conditions, silver staining can be used for quantitative comparisons. In this paper, we focus on a major endeavor related to analysis of hypertension-induced heart failure in Dahl rats by high salt diet.13-15 Since this project is based on “whole animals”, metabolic labeling was not the method of choice. Proteomic analysis was performed in heart samples (as well as samples of other relevant organs) and solubilized samples were submitted to 2D gel analysis. In this paper, we provide an analysis of the system and provide evaluation of the procedure that was supposed to be used in the “central part” of the project.
Materials and Methods Animals. We used young Wistar rats (RCC, Itingen, Switzerland) and Dahl salt resistant rats (SR/JrHsd) (Harlan, Inc., AD Horst, The Netherlands) maintained in our animal facility. Dahl rats were either fed a low salt diet (0.3% NaCl) or high salt diet (4% NaCl) for 5 weeks according to Harlan’s formula. All experiments conformed to the rules of the Swiss Federal Act on Animal Protection (1998) and were approved by the Veterinary Department of Basel (Switzerland). Protein Extraction. To prepare protein samples for 2D gelelectrophoresis, N2-frozen heart apices were pulverized using a dry ice-cooled mortars and pestles and subsequently solubilized in 7 M urea, 2 M thiourea, 4% CHAPS, 20 mM DTT and 2% ampholines, pH 9-11 (Invitrogen). Per 10 mg of frozen tissue, a volume of 0.1 mL of solubilization buffer was employed. Solubilization was done for 20 min at room temperature, supported by 3 short periods of vigorous vortexing. Extracts were cleared by centrifugation at 14 000g for 10 min. Protein concentrations were determined with the Bio-Rad DC protein assay. Two-Dimensional Gel Electrophoresis and Spot Visualization. For two-dimensional gel electrophoresis, the ISODALT 16-18 system was used (ampholines pH 3-10 (Invitrogen) in the first dimension; and 11% -19% linear acrylamide gradient in the second dimension). In this system, up to 20 gels can be processed simultaneously. Protein spots were visualized by silver staining. This was performed by some modifications of the Vorum protocol detailed in ref 19. Protein fixation was done overnight in 40% ethanol and 10% acetic acid. The development step was performed for 10 min in 6.75% sodium carbonate decahydrate and 0.005% formaldehyde. Wet silver-stained gels were scanned by Pharmacia Image Scanner with 300 dpi, 16 bit. Image Analysis and Spot Quantitation. PDQuest image analysis software, version 7.2 was used for spot detection, matching and quantitation. Briefly, spot registration starts with automatic detection by PDQuest image analysis procedures. 5212
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Grussenmeyer et al. To avoid too many artifacts recognized as spot, we choose “robust” parameters for spot detection, which will not detect the weakest spots on gels. Those were added manually per mouse click. Manual intervention is also needed to delete residual artifacts wrongly registered as spots. About 30-50 landmarks were usually introduced to achieve proper matching of spots between gels. Quantitation results from PDQuest calculations based on Gaussian modeling of spots. No calibration for gray density values was used. Cumulative Distribution Plotting. Spot volumes were exported into simple text files and further processed with Microsoft Excel spread sheet software. Blank values of those spot lists were eliminated. Quantile (percentile) values were calculated for each spot volume using a formula by which the “rank”-result of each spot volume is divided by the number of spots in the corresponding data column. To analyze subpopulations of spots, data sets were reduced to those spots present at least once in each of the to 2 groups of gels to be compared. Correlation Analysis/Scatter Plot/Coefficient of Variation/ Correlation Expression: The scatter plot (on log/log scale) is a convenient way to identify quantitative relationship of compared gene products. The plot takes into consideration data from two patterns (x-axis for one pattern, y-axis for another one). Since each dot on the scatter plot is “clickable” (in the PDQuest program) and allows to inspect the actual spot (on the displayed pattern), it is rigorously used as an indispensable interactive method of proofreading to check for possible “spot mismatches”. Graphs presented in this communication were generated with excel. Calculations for Pearson’s correlation coefficients, numbers of common spot per comparison and means of coefficients of variation were done by PDQuest. Statistics of Gel Segments. Spot data were exported to tables containing all relevant parameters, that is, spot volumes and spot positions on the gel. The relevant decision regarding spots belonging to certain segments were based on the position in the master pattern. We developed perl-scripts to perform separate calculations of spot volume medians, averages, spot numbers and spot lists for specified gel segments and segment sizes. Resulting data lists were further processed with excel. Normalization factors were introduced to into PDQuest’s external normalization table. In PDQquest, analysis sets were created to restrict statistical analysis to specified spot lists representing defined gel segments. Analyses of Significance of Expression Differences. To identify significant expression differences (between groups of replica gels) Student’s t test-function of PDQuest with a confidence interval of 0.95 was used.
Results Extracts from the heart-tissue of untreated Wistar rats were prepared and submitted to electrophoretic separation using ISODALT system. On the resulting stained gels, as detailed in Materials and Methods, we performed proteomic image analysis. For the electrophoretic runs, we loaded samples covering a range of 20-650 µg (>30-fold loading range) in half-log steps. In Figure 1A, we present the experimental output in a synoptic view of 12 patterns, four loading sets of three replica gels. Color labels of the frames (green, turquoise, dark blue and red) are used in a congruent manner, such that the set of gel patterns is marked with the same color as are the plots in the portions of Figure 1B,C. Upon visual inspection of the gels, we concluded that all 12 images were usable for further analysis, although “sample overloading” might yield patterns where
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Figure 1. Cumulative distributions of spot volumes at an extract loading range of 20-650 µg. (A) Images of 12 silver-stained gels at graded sample loadings: 20 µg (green frames), 65 µg (blue frames), 200 µg (dark blue frames), 650 µg (red frames). Stacked images of three sample replicas for each group are shown. (B) Cumulative distributions of all four graded inputs, using three replicas for each input. (i) Data sets of all 12 proteomic patterns. (ii) Data sets upon merging spot-volume-values from replica sets. Vertical dotted lines indicate the intercept of the cumulative plots with the 0.5 quantiles. Short horizontal bars depict the distances at 0.5 quantiles (a, b, c). [x-axis, Spot volumes in PDQuest units; y-axis, Quantiles in terms of ranked position of spots divided by the number of spots in the data set]. (C) Cumulative plots. Three series of spot-subset comparisons. (iii) Input 20 µg vs to 65 µg. (iv) Input 65 µg vs 200 µg. (v) Input 200 µg vs 650 µg. Horizontal bars at 0.5 quantile indicate the ratio (log distance) of the compared populations. Vertical dotted lines and gray horizontal bars relate to the results of the (ii) plot. The icons at the upper left part of the three series mark the pair that is being compared. Journal of Proteome Research • Vol. 7, No. 12, 2008 5213
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Table 1. Data on Individual Patterns
protein loading (µg) total densitya of gel (× 109 units) total staining of spots (× 109 units) detected spots average of 3 gels a
gel 1
gel 2
gel 3
gel 4
gel 5
gel 6
gel 7
gel 8
gel 9
gel 10
gel 11
gel 12
20 2.55 1.03 560
20 2.12 0.82 457 482
20 2.14 0.85 429
65 5.45 2.28 790
65 5.02 2.14 817 816
65 4.77 2.51 840
200 13.08 8.24 1406
200 11.70 6.81 1140 1204
200 10.82 6.53 1067
650 16.53 8.28 1317
650 15.78 7.76 1346 1325
650 16.57 7.57 1312
Sum of density units of all spots. Data on all groups and replica gels are given.
Table 2. Data on Merged Patterns
protein loading/gel (µg) no. of spots in the merged populationa spot volume average (×106 units) average (log) median (log) standard deviation (log)b MCVc c
gel 1, 2, 3
gel 4, 5, 6
gel 7, 8, 9
gel 10, 11, 12
20 1446 1.92 5.72 5.62 0.65 38.3%
65 2447 2.72 5.95 5.90 0.64 40.7%
200 3613 6.03 6.32 6.27 0.66 41.3%
650 3975 5.83 6.45 6.49 0.57 42.9%
a Term “merged population” refers to the sum of spots in the three replica patterns. MCV: mean of coefficients of variation (relates to the variance of the individual spots).
Table 3. Ratio of Spots and Log-Distance A 20-65 (µg)
B 65-200 (µg)
Complete Populations: merged spot population sizea 1446/2447 2447/3613 log-distance (of the medians) 0.26 0.40 ratio (anti-log) 1.83 2.49
C 200-650 (µg)
3613/3975 0.14 1.38
Subpopulations of Common Spots: merged spot subpopulationsa 1374/1541 2395/2769 3190/3276 - relative to total 95%/63% 97.9%/76.6% 88.3%/82.4% log-distance (of the medians) ratio (antilog)
0.56 3.62
0.48 3.02
0.34 2.20
a Since two populations are compared, the first number (e.g., 1446) refers to the lower sample loading (e.g., 20 µg), the adjacent number (e.g., 2447) refers to the higher sample loading (e.g., 65 µg).
portions of images will contain nondiscernable streaks. The broad range of loading allowed the assessment of reproducibility and linearity of the system; the number of detected spots varied from 429 (for the lowest input) to 1346 (for the highest input). Spot counts of individual gel patterns, their total spot volumes (sum of intensities) and other image analyses parameters are listed in Table 1. Earlier systematic investigations of protein quantitation by two-dimensional gel electrophoresis have shown that the distribution of spot volumes closely fits a log-normal distribution,20 which in turn is the prerequisite for parametric statistical evaluations. We have found that not all our data fit to normal distribution (upon subjecting them to Kolmogorov-Smirnov test). This supports our notion (as will be shown below) that the normalization procedure based upon the use of the median is more adequate than the use of the mean. In addition, the performance of 2D gel electrophoretic experiments can be assessed and compared in the context of their resulting spot volume distributions. Upon organizing the data in ranked sequence of spot volumes, plots of percentiles against intensities (spot volumes) yield cumulative distributions as depicted in (i) of Figure 1B. The plots of replica samples were very nearly superimposable, while there were definite shifts in curve position related to graded loading input. Individual plots ((i) of Figure 1B) allow a straightforward inspection of the results obtained from each of the gels; by 5214
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b
Stdev: standard deviation (relates to the entire spot population).
merging spot lists, new populations were created that characterize the replica gel patterns in their entirety ((ii) of Figure 1B). Shifts of positions of the distribution curves are recognizable, and the “distances” at the 50th percentile can be calculated, which in turn can be used as a measure for the ratio of the “true” gel loadings. In our particular set of plots, we might have expected shifts with a value of 0.5 log, since the chosen ratio of input loadings was half a decade. The actually found distances (shifts at 50th percentile) deviated, however, from this value. The number of detected spots (as mentioned above) varied considerably with the graded loading input. Upon suboptimal loading, a certain portion of spots (those readily detected at higher input) will not be registered. Therefore, each of the presented plots in (i) and (ii) is based on a different number of data points. We assume that the observed deviations (from the expected 0.5 log shifts) occur due to the fact that populations of different size and composition are compared. The “surplus“ spots detected only at higher loadings should prevalently belong to a subset that is below the detection level at lower loading inputs. Therefore, the comparison of theses populations is not analogous, and might be the reason for the observed deviations. Two approaches offer themselves to equalize the number of data points, and to achieve comparable situation. One is to remove from the “larger spot list“ the lower tail of the ranked spots (low-abundance spots), and to compare the equalized spot population. The second approach was through redefining the populations that one intended to compare. By the PDQuest-defined spot matching, we have obtained spot sets that are of the same size, since each spot in one population has a partner in the other one. In Figure 1C, three sets of comparisons in terms of cumulative plots are depicted. In (iii), the input of 20 µg is compared to the input of 65 µg. The two cumulative plots show a high degree of parallelism, and the distance of the curves at the inflection point is upon this procedure indeed near 0.5 as expected by theory. A similar situation was found with the next set of comparison in Figure 4 (65 µg vs 200 µg). Here the shift value is 0.46 (expected 0.5). In (v), the comparison of the 200 µg versus 650 µg reveals that the shape of the ”red“ dot plot deviates from the expected parallelism to the “blue plot”. The
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Figure 2. Scatter plots. Log/log comparisons. (A) Replica-comparisons. Four scatter plots, each related to a pairwise comparison of the same input (20/20, 65/65, 200/200, 650/650 µg). Each of the plots represents only one of three possible pairwise combinations (selected by virtue of the correlation coefficient to be the mean one of the three combinations). At the lower right corner of each scatter plot, the number of spot pairs and the Pearson’s correlation coefficients are given. The means of the coefficients of variation (MCV) for all 3 replica gels were 38.3%, 40.7%, 41.3%, 42.9%. (B) Cross-comparisons. Three levels of comparison; loading difference 0.5, 1, and 1.5 log. Three plots for 0.5 log (20/65, 65/200, 200/650), two plots for 1 log (20/200, 65/650) and a single plot for the extreme loading ratios of 1.5 log (20/650). Helper diagonals are described in the text. Each dot represents a spot and its intensity in each of the compared patterns as defined by the x- and y-axis (in log units). At the lower right corner of each scatter plot, the number of spot pairs and the Pearson’s correlation coefficients are given.
reasons for this deviation will be considered in the Discussion; here we limit to mention that the highest loading input in this series (650 µg) seems not to be usable for quantitative analysis, whereas all other inputs provide a strong assurance that our approach of quantitation is usable. In Tables 2 and 3, some statistical values are given. Although these results provide all relevant information on spot populations, they do not offer any information on quantitative aspects of individual “spot-pair-comparisons”. To scrutinize the reproducibility of quantitation, we have chosen the tool of scatter plot presentations. As depicted in Figure 2, both “replica-comparisons” (i.e., gel patterns based on the same loading input) and “cross-comparisons” (comparing different inputs) were performed. Each dot represents a spot and its intensity in each of the compared patterns as defined by the log scale of x- and y-axis.
Replica-comparisons, that is, gels with the same sample loading were compared. In Figure 2A, four scatter plots are shown, each related to a pairwise comparison of the same input (20/20, 65/65, 200/200, 650/650 µg). The shaded icons above the plots are analogous to the gel representation of the Figure 1A. From the plots, it can be seen that the cloud of dots are distributed along the “idealized” diagonal. The number of displayed dots for the lowest input (367 dots) is considerably less than at the intermediate loadings (620 and 1098 dots), while the highest input reveals a widespread cloud (1182 dots). Note that the diagonal is an idealized line of slope 1, and it is not meant to be a measure of correlation. The numbers 0.885, 0.844, 0.843 and 0.780 are the actual Pearson’s correlation coefficients. For each loading, only one pair of comparisons is shown, the remaining ones are available in our database. Journal of Proteome Research • Vol. 7, No. 12, 2008 5215
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Figure 3. Comparison of proteomic patterns of heart extracts from rats fed with low- and high- salt diet. Four patterns from low salt group and three patterns from high salt group. Range of detected spots is indicated.
Cross-comparisons, that is, gels with different loading wer also compared. In Figure 2B, there are three levels of comparison shown (loading difference 0.5, 1, and 1.5 log). From the construction of the experiments (and also from the shaded icons), it follows that there are three scatter plots for 0.5 log (20/65, 65/200, 200/650), two plots for 1 log (20/200, 65/650) and a single plot of the extreme loading ratios of 1.5 log (20/ 650). Note that the “idealized diagonal” has a slope 1 as was the case in Figure 2A, but the intercept with the y-axis is shifted “upward” by 0.5 log as the comparison is widening. It can be seen that the position of the cloud of dots shifts also, as expected, upward. The spread of the cloud of dots is widening with increasing ratio of loading which will be dealt with in the Discussion section. No objective measures of all aspects of the reproducibility of a method exist; nevertheless, the scatter plots provide adequate information on the range of usability of the assay. We conclude that (a) the reproducibility of the replica gels is indeed very good for all chosen gel loadings, except of the highest one; (b) the cross-comparisons of the 20/65 µg and 65/ 200 µg and to some extent also 20/200 µg yield fairly good results (correlation coefficients 0.809, 0.807 and 0.694) indicating that the methodology is robust enough to accommodate varying loading inputs, while (c) all cross-comparisons in which the highest input of 650 µg was used have to be rejected; it can be said that with the highest input there is practically no “relationship” between loading and spot volume. In none of the above steps, a normalization procedure was included. This was intentionally the case, since the normalization would have eventually interfered with reproducibility, robustness and linearity. There exist several normalization procedures applied to proteomics using various algorithms, most of them based on ratios of the sum of spot volumes, or 5216
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Grussenmeyer et al. on their medians. The PDQuest image analysis system that we use offers such a utility, and in many instances, we have used this approach. It is our experience that when a set of very similar samples is compared, and when the staining procedure is highly uniform, any procedure of normalization will do. Contrary to this, when the normalization is supposed to correct for suboptimal staining of gels on one hand, or for variations in sample loading on the other hand, the normalization procedure based on the sum of all spot volumes in a pattern (as exemplified by PDQuest) yields inadequate correction factors. As soon as profound differences have to be balanced, some procedures might “work better” than others. To evaluate the effects of various normalization methods on experimental results of differently expressed proteins, proteomic patterns of two groups of Dahl rats fed with high- and low-salt diets respectively were examined. Such animals represent different stages of stress induction and should provide a substantial number of expression differences in heart proteomes. These differences were supposed to be studied. Figure 3 displays images (in a stacked fashion) of proteomic patterns obtained from heart samples of rats fed with the mentioned diets. Upon completing the image analysis, a normalization factor based on the sum of all spot volumes was calculated in each of the compared patterns (results of such calculations are provided by the PDQuest utility). We have also calculated the median-based normalization factor. We have devised an approach, which we call the “balance test” in order to determine whether the obtained normalization factor is acceptable in terms of suppressing the noise and minimizing the effects of experimental variations. The basic property of the noise (and of experimental variations) is that there is a balance of the apparent “increases” and “decreases” in abundances (i.e., the number of spots in both categories are similar). From all calculated normalization factors, that one is the best that yields the best-balanced ratios of “ups and downs”. The noise-related “ups and downs” have their own distribution, and it is our experience that an arbitrarily chosen “2-fold increase or decrease” covers conveniently the noiserelated category (the possibility that true biological alterations might be “masked” by noise is taken care of at the stage of applying tests for statistical significance). In Table 4 the normalization factors and the “balance test” data are given. It can be seen that without normalization there is an enormous disbalance of spots (26-fold) that are twice or more increased. “Sum-of-all-spot-volume” normalization yields 2.88-fold, and median normalization 1.17-fold ratio (of “ups and downs”). Therefore, the median-based normalization factor is to be preferred (this choice is furthermore preferred due to the fact that not all our data fit to normal distribution). In Figure 4A, we have plotted the results of the “balance test” in overlaying the master pattern with red and blue labels related to the increased or decreased abundance. The results were in this case not overly rewarding, since topographical bias of increased and decreased abundances in major portions of gels occurred. Since no biological meaning can be attributed to such topographical distributions, there must have been some staining bias (e.g., low molecular mass spots being stained disproportionately stronger). To follow up this assumption, we performed normalization analysis of the pattern upon virtual segmentation into 8 strata that contain altogether 879 spots (37, 57, 109, 163, 217, 154, 130, 12) as depicted in Figure 5. The segments were defined as
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Proteome Analysis in Cardiovascular Physiology and Pathology Table 4. Comparison of Results Based on Three Different Kinds of Normalization
normalization based on ratio of
number of spots that are 2× (or more) up 2× (or more) down number of spots up/down - ratioa, considering all spots number of spots significantlyb different in the compared populations number of spots significantly up number of spots significantly down up/down - ratio, considering only spots defined as significantly different in the two populations
no normalization performed
the sums of all spot volumes
576 22 598 26.18 304
242 84 326 2.88 165
155 91 246 1.7 132
101 106 207 0.95 101
304 0 all up
158 7 22.6
111 21 5.29
77 24 3.21
a Number of matched spots that are up- or down-regulated at least by a factor of 2. irrespective of the magnitude of up- or down-regulation.
b
the median values
the medians derived from segments
Number of matched spots, frequency of which are altered
Figure 4. Expression differences in heart proteomes (A) Disbalance of topographical representation. (B) Balanced representation upon segmented normalization. Red labels: spots with expression levels increased twice or more. Blue labels: spots with expression levels diminished twice or more.
strips of 1/8 of the gel area, and spots in the master pattern with corresponding coordinates were chosen; spots of other gels were defined as members of the segment based on matching rather than their positional coordinate. In Figure 5A, individual segments (on the master) are shown, while in Figure 5B, plots of median spot volumes in each segment are plotted. Each of the curves correspond to one of the gels shown in Figure 3. From this presentation, it is clear that there are considerable differences from segment to segment, although some parallelisms of the wavy curves are detectable. In Figure 5C, the ratio of the curves (median of each segment divided by the median of the master segment) is depicted. In Figure 4B, the same pattern (as in Figure 4A) has been replotted, again with red and blue labels indicating up- and down-abundances. The overall “topographical mix” of the two distributions is very good. These normalization results are fully satisfactory. We draw this conclusion just by visual inspection, and we refrain from formal proof thereof. The ratio of up- to down-abundances is now 0.95, which is a further improvement over the 1.17 value of the “median normalization” as will be mentioned below and in the Discussion. The actual analysis of up- and down-regulated entities is based on the significance of the findings, rather than just upon the ratios of abundances. Thus, for some spots, a ratio g2 can
be a significant alteration (based on Student’s t test); in others, it is an experimental fluctuation. Since we have defined the above quests as a “balance test”, we can disregard now the test, and proceed to asking which of the alterations are significant (in this context both up- and down-regulations are considered). In Figure 6, the master pattern with color-labeling of the significantly altered spots is shown. Some spots are marked with all three-color labels, others with two colors or a single color. Upon “sum-of-all-spot-volume” normalization, there are 165 significantly altered spots, upon median normalization 132 spots, and upon segmented normalization 101 spots. These qualities and their overlaps are shown in the Venn diagram of Figure 6B. Note that 67 spots (intersection of all three Venn populations) are considered as significantly up- or down-regulated according to all three types of normalization. The results obtained from this study are directly applicable on the evaluation of data that we shall report in the follow-up studies on proteomic alterations in development of heart failure in Dahl rats.
Discussion Work reported here is part of a larger study, in which we analyze quantitative changes in protein expression in various Journal of Proteome Research • Vol. 7, No. 12, 2008 5217
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Figure 5. Normalization of segmented patterns. (A) Pattern divided into eight segments (indicated by y coordinates ranging from 20-160 length units. Color coding distinguishes spots in individual segments. (B) Plots of median spot volumes in each segments. The number of spots in the pattern chosen for comparison is indicated at the top (37, 57, 109, etc). Results of seven patterns (each animal group marked by different shading) are shown. (C) Ratios of medians. The basal level (of selfcomparison) obtains values of 1 (gray line).
animal models of pathophysiology of heart failure. We are convinced that the search for biomarkers and for pharmacological targets that is based on all- or nonexpression differences yields an incomplete picture, while only a broad scrutiny of “sets of changes” will provide a true description of disease, and will eventually lead to novel therapeutic applications. Methodological evaluation of the proteomic analysis of cardiac tissue has been performed with a special emphasis on establishing procedures that allow reliable quantitative analysis of silver-stained readouts. Questions of reliability, reproducibility, robustness and linearity were addressed and clarified. In addition, several types of normalization procedures were tested and evaluated and new approaches proposed. It has been found that the silver-stained readout offers a convenient approach for quantitation provided that a linear range for gel loading is defined. A broad range of a 10-fold input (loading 20-200 µg per gel) fulfills the linearity criteria, although at the lowest input (20 µg) a portion of protein species will remain undetected. The method is reliable and reproducible within a range of 65-200 µg input. The scatter plot is based on the comparison of spot volume pairs, in which the overall view of all plotted dots (each dot ) 5218
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Figure 6. Alteration of expression levels upon various types of normalization. (A) Alterations in expression upon establishing 95% significance level. Color-coding relates to PDQuest normalization (green), median normalization (blue) and segment normalization (red). Some spots are marked with all three color labels, others with two- or a single- color. (B) Venn diagram of three sets. Interiors of overlapping circles indicate spots that belong to more than one set. The total number of spots in each of the sets is indicated outside of the Venn diagram (165, 132, 101).
1 spot pair) permits a judgment of the entire spot population in terms of the position and compactness of the cloud of dots. This, in turn, allows to evaluate (to judge) the combined effect of experimental variation and of system error. It is of interest to note that the means of the coefficients of variation (MCVs) in our experiments with values ranging from 35 to 43% were similar to data reported by other research teams.21-23 The spread of the cloud of dots in the scatter plots is a useful measure for the alteration of usability with modification of the loading inputs. This is especially clear from the scatter plot comparing 200 and 650 µg in terms of decrease of correlation coefficients in the 200/650 µg comparison (Figure 2). In some special situations, especially when the emphasis is on retrieving spots for mass spectrometric (MS) analysis (and when no quantitation is attempted), overloading of gels could be useful in making spots that would be weak at low input strong enough to be picked for further MS analysis. In quantitative analysis, overloading should be, wherever possible, avoided. In the presented experimental system, the loading of an extract from heart tissue of 650 µg is definitely excessive.
Proteome Analysis in Cardiovascular Physiology and Pathology Cumulative distributions derived from proteomic images of overloaded gels revealed a substantial underestimation of the modeled spot volumes. We interpret this either as a staining saturation of high-abundance proteins, or as diminished spot volumes due to lack of sharp segregation of neighboring spots. In spite of the above warning, proteomic patterns from overload-inputs might be useful in quantitation of those species of proteins that are below threshold of detection at 20 µg (or 65 µg) loading. We consider such helping measures as semiquantitative approaches, since in situations in which only a small portion of spots is considered (because of overloadstreaks) it is difficult to establish a correct normalization factor. In several reports,22,24 doubts were expressed on the suitability of silver staining for quantitative evaluation of proteomic patterns. Most of the arguments relate to poor reproducibility and narrow linear range of the usability. Other reports show the usability of silver staining for quantitation.23,25 In results reported here, we have reliably detected spot volumes in the range of 4 orders of magnitude (104-108 units) and established an utilizable range of protein loading of 1 order of magnitude. As already mentioned, the reproducibility in terms of coefficients of variation was comparable to those reported by others for silver stained proteins.21,23 Smaller coefficients of variation were reported for metabolically labeled proteins (20-28%),26 Coomassie blue (32%)27 and Sybro Ruby (0.03%-0.33%)22 stained proteins. Those values, however, were based on smaller spot populations. If we inspect spots exhibiting the highest coefficients of variation, we realize that these are mainly those ones that are part of streaks or overlapping spots. For such spots, the quantitation is questionable. This is also the limitation of the present version of the PDQuest software. In this communication, we present a series of gels that show strong regional staining differences. To solve problems of this kind, we introduced a normalization procedure based on segmental parameters. It is possible that future improvements in staining protocols will obliterate the need for this portion of analysis. For the time being, we routinely check whether segmentation is needed or not. Our routine checks upon image analysis the shape (and the inflection point) of cumulative distribution plots. We carefully evaluated the median not only from calculating the 50th percentile, but also by inspecting the “neighborhood” of median in the plot. If discontinuities are observed (e.g., due to gels containing faulty regions), we should not rely on the median, but we should work with the mean spot volume of interquartile range entities. Such fine-tuning details are omitted from this presentation, but it should be noted that normalization factors derived from the interquartile range might be a useful choice at some special instances. Important aspects of normalization have been worked out in recent investigations. Almeida et al.28 introduced cumulative distribution plottings and median normalization to compensate for intergel experimental variability. Chang et al.20 found quantile normalization superior to median normalization. This approach, however, uses multiple adjustments of segments of spot distributions and is not compatible with the PDQuest option of “external normalization”. Several normalization procedures were performed, and we concluded that the normalization factors obtained by the mentioned procedures have to be checked for their adequacy. The procedure leading to the “balance test” is based on all spots detected by the PDQuest software in any given gel. We consider matched spots always from two patterns, and we accept the
research articles
normalization factor as being correct, if the population of spots that reveal an up-ratio of 2 (or more) is in good balance with spots that have a down-ratio of 2 (or more). The ratio of >2 (up-, down-) is not meant to be an indication of up- and downregulation, since it includes everything what the comparison of two gels can include (noise, experimental variation, differences in spot volume modeling, and of course also the up- and down-regulated protein expressions). If the two resulting populations (up’s vs down’s) are well-balanced, the normalization procedure is provisionally accepted. We have observed that the plot of the mentioned populations in some instances does not lead to acceptance, which is the case at occasions when the two spot populations are not topographically properly spread over the entire proteomic pattern. We do not have a formal measure for the “good or bad spread”, but if too many spots of one kind are localized in some portions of the pattern, a special refinement of the normalization through virtual segmentation of patterns is required. This is shown in Figures 4B and 5. The up- and down-ratios of the “balance test” do not refer to the significance of up- and down-regulated expressions. The actual statistical significance is based on the comparison of numerical values of spot volumes in two groups that are under comparison. The differences might be significant (e.g., with 95% confidence) irrespective of the value of the ratio. It is interesting to note that different normalization approaches that we have used lead to different numbers of significantly up- or downregulated entities, but it is encouraging to note that 67 spots are found to belong to the set of significantly up- or downregulated proteins by all three normalization procedures. Such sets are the basis for further scrutiny, as it will be presented in further publications (manuscript in preparation).
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