[Methods in Molecular Biology] Difference Gel Electrophoresis (DIGE) Volume 854 || Assessing Signal-to-Noise in Quantitative Proteomics: Multivariate Statistical Analysis in DIGE Experiments

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Rainer Cramer and Reiner Westermeier (eds.), Difference Gel Electrophoresis (DIGE): Methods and Protocols, Methods in Molecular Biology, vol. 854, DOI 10.1007/978-1-61779-573-2_4, © Springer Science+Business Media, LLC 2012

Chapter 4

Assessing Signal-to-Noise in Quantitative Proteomics: Multivariate Statistical Analysis in DIGE Experiments

David B. Friedman

Abstract

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appro-priate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically signifi cant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard uni-variate tests.

Key words: DIGE , Principal component analysis , Multivariate statistics , Variation , Technical replicates , Biological replicates

The complex methodologies used in many quantitative proteomics studies involving multiple experimental conditions often are com-prised of small sample sets and underpowered experiments. Ultimately what is being measured in these experiments is variation . Utilizing multiple, independently derived (biological) replicate samples is the only way to determine if an observed change is due to variation in the signal (the biology) or the noise (technical/analytical variation or normal biological variation that is not associated with the experi-ment). Technical replicates (repeat analyses on the same samples) are necessary to control for analytical variation, but when this technical

1. Introduction

32 D.B. Friedman

noise is low, the biological replicates can control for both technical and biological noise. Due to the low amount of technical noise demonstrated for DIGE ( 1 ) , this technology platform enables statis-tically powered experiments (see Note 1) using a relatively low number of biological replicates without the need for additional tech-nical replicates of each sample (see Note 2).

In the simplest type of quantitative proteomics experiment, there are two conditions being measured, typically some experi-mental condition and a control. Even here the need for indepen-dent biological repetition remains paramount; it is insuffi cient to simply multiplex a single experimental and control sample into the same analytical run (e.g., DIGE gel) and be able to determine if the observed change represents a relevant biological signal or tech-nical/biological noise. Even pooling independent samples does not alleviate this issue, as once samples are pooled the ability to distinguish signal from noise is lost (see Note 3).

Using the requisite number of biological replicates, the most commonly used statistical test for quantitative proteomic changes is the univariate Student’s t -test, whereby the distribution about two means is compared with the magnitude of difference between these means, and the resulting p -value refl ects the likelihood that the measurements are derived from the same distribution (the null hypothesis). When experimental conditions become greater than two, then the univariate analysis of variance (ANOVA) test is com-monly invoked (the t -test is a special case of the ANOVA test). Despite their commonplace usage in quantitative proteomics, these univariate tests only assess changes on a feature-by-feature basis; a single species relative to itself across all samples and conditions. Although multiple testing correction algorithms are available to compensate for univariate tests performed within large datasets (e.g., Bonferroni and Benjamini-Hochberg methods), univariate tests do not take into account the variation present in the global experimental system. Especially in the small-sample-size regime, the likelihood of measuring a change by chance increases regard-less of what the univariate p -value suggests. Thus, knowledge of the global variation is essential and in some cases can infl uence the likelihood that univariate changes are biologically signifi cant. The univariate p -value is only a guide, a means to an end but not an end unto itself.

In contrast, multivariate tests enable the visualization of the experimental variation on a global scale, analyzing all of the vari-ables simultaneously. Technical noise (poor sample prep, run-to-run variation) and biological noise (normal differences between samples, especially present in clinical samples) are almost always associated with analytical datasets in quantitative proteomics and may well override any variation that arises due to actual differences related to the biological questions being tested. Multivariate tests can highlight major sources of variation within a dataset, and when performed in an unsupervised fashion, can test if this variation is

334 Assessing Signal-to-Noise in Quantitative Proteomics…

consistent with the anticipated biological differences between samples as well as identify sample outliers, fouled samples, and even potentially poor experimental design.

Multivariate analyses such as principal component analysis (PCA) and hierarchical clustering are most commonly used in this realm of quantitative proteomics, and both are easily accessible using commonly employed software suites designed for DIGE anal-ysis, such as DeCyder (GE Healthcare) and Progenesis SameSpots (Nonlinear Dynamics) (see Note 4). These multivariate analyses work essentially by comparing the expression patterns of all (or a subset of) proteins across all samples, using the variation of expres-sion patterns to group or cluster individual samples. At the very least, unsupervised clustering of related samples adds additional confi dence that a “list of proteins” changing in a DIGE experiment are not arising stochastically. At the very most, these tools offer additional insights to the underlying structure of the variation within a complex multivariable experiment and enable the discovery of protein expression changes by patterns and within subgroups that are beyond the scope of simple pairwise tests.

Using PCA as an example, this chapter will cover the steps taken in typical DIGE analyses to perform multivariate statistical tests. It will use as examples experiments with varying levels of both biological signal and technical noise to illustrate measures taken to evaluate the experimental variation and to derive mean-ingful information for typical DIGE analyses.

DeCyder software (GE Healthcare, Uppsala, Sweden) and SameSpots software (Nonlinear Dynamics, Newcastle-upon-Tyne, UK)—the versions used for the examples were 6.5 and 4.0.3779.13732, respec-tively—and a suitable computer for running these software packages.

As stated in the Introduction, ultimately what is being measured in quantitative proteomics experiments is variation . In the best cases, the measured variation is produced by the biology being manip-ulated, and insight can be drawn from an understanding of the proteins and their modifi ed forms that give rise to this variation. This is the signal . However, there is also signifi cant noise that can contribute to this variation, and this can result both from the tech-nology employed to measure the variation as well as from the normal biological variation that exists between samples but is unrelated to the biology being manipulated.

2. Materials

3. Methods

3.1. Sources of Variation

34 D.B. Friedman

Sources of technical noise in DIGE include gel-to-gel variation as well as sample handling and labeling variation; however, the use of the Cy2-labeled internal standard methodology effectively nor-malizes this variation ( 1– 5 ) . Additional sources of technical noise can be related to sample preparation (e.g., subcellular fraction-ation), laboratory conditions (e.g., medium composition, ambient temperature, incubation conditions), and sample procurement (e.g., tissue dissection, protein extraction). Testing independent biological replicate samples from each condition is necessary to distinguish between these different sources of variation, and PCA is an effective way to visualize the experimental variation on a global scale and enable the assessment of the major sources of vari-ation with respect to the experimental conditions.

PCA is a commonly used statistical tool that is capable of reducing the complexity of multivariable space of an experimental dataset into the major sources of variation, the principal components. In the case of DIGE, the variation arises from the expression values of each resolved protein feature, and these features are registered across all samples and gels (i.e., the spot patterns are matched and thereby so are the expression values across the dataset).

One need not to understand all of the underlying mathematics of PCA, which involve covariance matrices and their resulting eigenvectors and eigenvalues, to be able to utilize the strong diag-nostic and discovery capabilities that this statistical test provides. But an understanding of what PCA is doing to the dataset is neces-sary to properly interpret the results. Considering a plot of all of the variables in a multidimensional space (a typical DIGE analysis could contain close to 1,000 defi ned features each of which is represented in multiple samples), PCA distills out the major sources of variation as defi ned by the eigenvectors and eigenvalues associ-ated with each square covariance matrix calculated. The longest axis through this cloud of multivariable values is defi ned by an eigenvector (and associated eigenvalue) as the fi rst principal com-ponent, with the second principal component by defi nition being orthogonal (perpendicular eigenvector) to the fi rst. Additional principal components (each accounting for smaller amounts of variation) can also be defi ned in complex datasets.

Several software tools are available for DIGE analysis, and two of the more commonly utilized tools found in published experiments, DeCyder (GE Healthcare) and Progenesis SameSpots (Nonlinear Dynamics), shall be used as examples here. Both enable the straight-forward assembly of complex DIGE experimental designs using the Cy2-labeled internal standard and independent replicates from multiple experimental conditions, and both enable the visualization of global variation using PCA and other multivariate statistics. Where they differ the most is in the approach to feature detection

3.2. Principal Component Analysis

3.3. Applying PCA to DIGE Datasets


and missing values in the dataset (see Note 5). Both approaches have their unique strengths and limitations, but ultimately they deliver essentially the same information.

Regardless of software approach, care should always be taken to yield reproducible, high-resolution two-dimensional gel elec-trophoretic separations, and this should include optimization steps if necessary. In addition, this chapter assumes that the CyDye label-ing, electrophoresis, and image acquisition procedures documented elsewhere in this volume have been followed using proper experi-mental design with a mixed-sample Cy2-labeled internal standard to coordinate the appropriate number of biological replicates that have been randomized with respect to dye labeling and gel position to compensate for unanticipated dye or gel biases.

1. Inspect and import all gel images. 2. Detect feature boundaries and generate ratios on each gel inde-

pendently in the differential in-gel analysis (DIA) software module, and then defi ne groups and set up experimental design and matching between gels in the biological variation analysis (BVA) software module (this can all be set up in batch mode if desired). Normalized ratios are generated in BVA at this stage.

3. Assess matching on all images in BVA and manually adjust matching in all images following prescribed methods associ-ated with the software. Ratios are recalculated as matching is adjusted within the dataset.

4. Ensure that the picking references are not included in the anal-ysis by breaking matches for the picking references on at least one gel in the matched set (no more than two should be neces-sary, depending on how many mismatches you allow for in the EDA analysis). This will avoid skewing of the PCA results due to preferential fl uorescence of the reference markers.

5. Open the extended data analysis (EDA) software module and begin a new project (the project must be closed in BVA for this but can be reopened once the data have been imported into the new EDA project).

6. Create in EDA a manual base set, with 100% of spot maps where protein is present (no missing values, see Note 6) and remove the unassigned spot maps.

7. Perform the following calculations: (a) Differential Expression Analysis, ANOVA. (b) Principal Component Analysis, select option to place spot

maps into the score plot and proteins into the loading plot. (c) For hierarchical clustering (if desired), under Pattern

Analysis select spot maps vs. proteins for both Proteins and Spot maps and exp groups.

3.3.1. Quick Guide: DeCyder

36 D.B. Friedman

8. Inspect PCA results. The score plots (samples) and loading plots (features) are displayed on separate graphs. Default settings place PC1 on x -axis and PC2 on y -axis. These can be changed up to PC5, and up to three PCs can be viewed at once (in a 3D view that can rotate). The values assigned to each PC are analogous to the percent of the overall variation that accounts for each component (see Note 7).

9. If necessary, construct a fi ltered set to perform additional data reduction (see Note 8). If performed, the analysis is no longer considered to be unsupervised but can be valuable in visualizing weak signals by removing background noise (see Subheadings 3.5 and 3.6 ).

1. Import and inspect all gel images. 2. Assign groups and experimental design. 3. Ensure that the picking references are not included in the anal-

ysis by appropriately using the “Setting Mask of Disinterest” functionality. This will avoid skewing of the PCA results due to preferential fl uorescence of the reference markers.

4. Assess alignment on all images and manually adjust matching in all images following prescribed methods associated with the software.

5. Generate normalized ratios on prealigned images (see Note 9). 6. Inspect the PCA results in Progenesis Stats. The score plots

(samples) and loading plots (features) are displayed on a single overlapping graph (biplot). PCA and hierarchical clustering (if desired) can be selected under the “Ask another question” tab. Default settings place PC1 on x -axis and PC2 on y -axis. The values assigned to each PC are analogous to the percent of the overall variation that accounts for each component (see Note 10).

7. If necessary, construct a fi ltered dataset by setting tags and clusters and reevaluate in PCA.

Since PCA organizes independent samples based on the varia-tion between them, post hoc identifi cation of the samples enables the investigator to establish the relative signal-to-noise ratio (S/N) present in a dataset. When S/N is high, one can expect proper organization of the individual samples based on the fi rst two or three principal components. When S/N is low, additional data reduction may be necessary to determine if biological variation is present in the experiment. And when technical noise is extremely high, the principal components can reveal organization of the sam-ples that is not based on the biology but rather on some technical aspect of the sample preparation. What follows are examples of each of these scenarios, using the PCA functionality of DeCyder for illustration of the experimental variation.

3.3.2. Quick Guide: SameSpots


Helicobacter pylori is the strongest known risk factor for gastric adenocarcinoma, yet only a fraction of infected persons ever develop cancer. In a recent study, Franco et al. used DIGE to quan-tify differences between two related strains of H. pylori , one of which caused only gastritis in rodents whereas the other also induced adenocarcinoma ( 6 ) . The virulent strain was directly derived from the nonvirulent strain after passage through an ani-mal, leading to the expectation that only a few key changes gave rise to the more aggressive nature of the virulent strain. Membrane and cytoplasmic fractions were analyzed from both genotypes, and each was produced as four independent (biological) replicates to control for unanticipated technical variation. The resulting 16 samples were coresolved on 8 coordinated DIGE gels along with a Cy2-labeled internal standard using standard methods as described elsewhere in this volume.

The technical noise of the DIGE platform has been demon-strated to be low ( 1 ) , and in this case the additional variation (noise) derived from sample handling and normal biological variation was also expected to be low because the replicates involved clonal bacterial colonies, although the fractionation into cytoplasmic and membrane preparations could produce unanticipated technical variation. PCA performed on 842 features matched across all 8 gels (no missing values) demonstrated that the majority of variation (PC1 = 80%) was consistent with differences between cytoplasmic and membrane fractions, as expected. However, the second princi-pal component (PC2), which described the second greatest source of variation between the samples, accounted for only an additional 5% variation, but this variation organized the samples derived from the virulent vs. nonvirulent strain (see Fig. 1 ). Thus, PCA clearly indicated low technical noise among the independently derived samples and clearly indicated that a low but signifi cant level of bio-logical variation was correlated with virulence.

That these two largest sources of variation correlated with expectations about the biology, rather than with technical issues such as dye-labeling bias, sample prep number (each set was pre-pared on separate days using different reagents), or other unan-ticipated, nonbiological factors, enabled these investigators to focus on the small number of virulence-related changes with high confi dence and low expectations for false discovery, and led to a number of signifi cant fi ndings including an amino acid substitu-tion (cysteine-to-arginine, causing a pI shift) in a fl agellar protein that affected motility. This result was readily detected via DIGE coupled with mass spectrometry-based protein identifi cation because the mutation altered the pI of the resolved, intact pro-tein forms but otherwise did not affect the relative expression level of the fl aA protein. As such, this alteration would most likely have gone undetected in a peptide-base, bottom-up LC-MS/MS strategy.

3.4. Example 1: High Signal, Low Noise

38 D.B. Friedman

In another H. pylori study, Loh et al. investigated differential protein expression between wild-type and mutant strains deleted for the ArsS component of the ArsRS signal transduction system in response to growth in different pH media ( 7 ) . The experimental design was similar to that described for Example 1, with N = 4 independent (biological) replicates from two strains grown at two pH conditions, resulting in 16 samples coresolved across 8 DIGE gels, each of which contained an aliquot of a Cy2-labeled mixed-sample internal standard.

Six hundred and thirty-nine features were matched across all eight DIGE gels, all of which was evaluated by PCA. Using no missing values in the data (100% matching), PC1 accounted for 56.7% of the variation among these features and organized the samples by genotype, with none of the other principal components organizing the samples based on pH treatment (see Fig. 2 ). Thus, the biological signal with respect to genotype is high, but any signal consistent with pH-specifi c growth was too low to be visualized over the genotypic signal or any other technical noise present in the overall variation.

3.5. Example 2: Low Signal, Low Noise

5.5

−5.520−20 PC1: 80.3% of variance

PC

2: 5

.2%

of v

aria

nce

Unfiltered 100% matched features (842)

cytoplasmic membrane

virulent

non-virulent

pH 4 pH 7~200 kDa

~10 kDa

a b

Fig. 1. Principal component analysis (PCA) showing a high signal-to-noise ratio. The cytoplasmic and membrane fractions from two related strains of Helicobacter pylori that differ in carcinogenic potential were analyzed by DIGE. Protein lysates were extracted and fractionated from the B128 (nonvirulent) and 7.13 (virulent) strains independently in quadruplicate, and 842 features were matched across all eight gels. ( a ) Representative DIGE gel from an 8-gel set used to coresolve the result-ing 16 individual samples (labeled with either Cy3 or Cy5 using a dye-swapping strategy) along with the Cy2-labeled mixed-sample internal standard. ( b ) PCA was performed on the unfi ltered dataset. The protein expression characteristics from 842 features from each individual sample are represented by each of the 16 data points in the PCA score plot. This analysis demonstrated that 80.3% of the variance (PC1) separated cytoplasmic from membrane samples as expected. An additional 5.2% of variance (PC2) separated the virulent strain 7.13 from the nonvirulent strain B128. Adapted from ref. ( 6 ).


Fig. 2. PCA showing a low signal-to-noise ratio. Two related strains of Helicobacter pylori that differ by deletion of ArsS were grown in pH 5 vs. pH 7 medium, and protein expression was analyzed by DIGE. The experiment was independently repeated in quadruplicate, resulting in 16 samples coresolved across 8 DIGE gels along with a Cy2-labeled internal standard. ( a ) Representative DIGE gel from the 8-gel set. ( b ) PCA performed on the unfi ltered 639 features matched across all eight gels indicated that the majority of variation among these features was consistent with strain-specifi c differences, but no other correlation with phenotype was revealed in the other principal components. ( c ) Refi ning the PCA to those features that exhibited a signifi cant change between any group relative to the others (ANOVA p < 0.05) revealed that the second principal component was now consistent with pH-specifi c changes which were the subject of the study. Adapted from ref. ( 7 ).

40 D.B. Friedman

In cases such as this where the biological signal of interest is not readily evident but the technical signal is low, additional data reduction may be helpful. In this case, low signals of interest may be revealed if the background noise (whether it be technical or biological in nature) can be removed from the PCA, and this can effectively be done by applying a statistical fi lter. Analysis of vari-ants (ANOVA) is an appropriate univariate statistical test to use for this purpose when multiple variables are under consideration because it identifi es features that are changing in one group relative to any of the others without specifying any pairwise comparisons (see Note 11).

In the example from Loh et al. ( 8 ) , applying an ANOVA fi lter ( p < 0.05) to the dataset defi ned a subset of 168 features that were of biological signifi cance for the experiment. Although this imposes the bias of the biological experimental design onto the PCA, it only selects those features that were changing signifi cantly in one of the four classifi cation groups relative to the other three, and for each feature this could be an independent classifi cation. By remo-ving those variations that contributed to the background noise in the original set of 639 features, PC1 (genotype) increased to 81% of the remaining variation, and now PC2 was found to organize the samples based on pH treatment, accounting for 6.4% of the variation or about ten features (see Fig. 2c ). Thus, the experimental conditions appeared to induce changes of interest, but they were too subtle in magnitude and/or number to be visualized in the context of greater sources of variation in the experiment.

In this third example (also from H. pylori , now cultured under different medium conditions), the signal is so low from any of the biological sources of variation that now dye-labeling bias appears to be ordered by the fi rst principal component in an unfi ltered analysis of 977 matched features across a six-gel DIGE experiment (see Fig. 3 ). Dye-labeling bias is known to exist and is typically con-trolled for using a dye-swap labeling scheme in the experimental design (see Note 12). That Cy3/Cy5 labeling bias appears here as the driving force behind PC1 (see Fig. 3b ) indicates not only very low biological signal but also very low noise because this anticipated bias typically contributes very little to the overall variation. Thus, the fi nding of organization by dye labeling in PC1 is expected in the absence of signifi cant signal or other sources of technical noise.

In this example, using a univariate ANOVA fi lter (imposing bias from the experimental design) prevents these changes from infl uencing the PCA when a dye swap is used in the experimental design (see Fig. 3c , Note 13). As was found in Example 2, applying the ANOVA fi lter now enables PCA to organize the samples based on the biological signal of different culture conditions. The weak biological signal represented in these 68 features is qualitatively evident by the loose nature of the sample clustering but nevertheless

3.6. Example 3: Very Low Signal, Low Noise


Fig. 3. PCA showing an extremely low signal-to-noise ratio. Helicobacter pylori cultured under three different experimental conditions: A, B, and C. The experiment was repeated four independent times, resulting in 12 samples coresolved across six DIGE gels along with a Cy2-labeled internal standard. ( a ) Representative DIGE gel from the 6-gel set. ( b ) PCA performed on the unfi ltered 977 features matched across all six gels failed to organize the samples based on the anticipated biological manipulation but indicated a low level of variance (PC1 = 29%) that organized the samples based on Cy3/Cy5 dye-labeling bias. Such dye bias is known to exist; that it appears here indicates not only that the biological signal is not detected but also that the technical noise is similarly low because only dye bias, and not something else, is infl uencing PC1. ( c ) Refi ning the PCA to 68 features that exhibited a signifi cant change between any group relative to the others (ANOVA p < 0.05) now begins to reveal some biologically relevant ordering of the samples based on the fi rst two principal components.

demonstrates that the weak signal is present once the background noise (dye bias) is removed by imposing a biologically infl uenced fi lter over the data.

This last example depicts a worst-case scenario where, without inspecting the global variation with PCA, the experimental results might be misinterpreted. In this case, a primary mammalian cell

3.7. Example 4: Low Signal, High Noise

42 D.B. Friedman

line was cultured in the presence or absence of a drug at fi ve time points, and this experiment was repeated four independent times resulting in a 20-sample, 10-gel DIGE experiment. By all conven-tional methods, the experiment appears to have worked very nicely as illustrated by high-resolution separations (see Fig. 4a ) and the presence of some statistically signifi cant univariate changes (see Fig. 4b ). But PCA on the unfi ltered dataset of 1,012 features matched across all ten gels clearly indicates that the principal com-ponents are organizing the samples based on cell passage number and not by treatment (see Fig. 4c ).

Thus, a simple PCA on the unfi ltered dataset can quickly deter-mine if something went drastically wrong with the experiment and in this case saved the investigator considerable time and effort to follow up potential proteins of interest when clearly the major sources of variation were unrelated to the biology being manipulated. These fi ndings raised the likelihood that the few changes “found” by univari-ate ANOVA arose stochastically and not from the drug treatment.

Fig. 4. PCA showing unanticipated high background noise. Primary mammalian cells were subjected to a drug treatment during four successive passages in tissue culture. Five time points were taken in each independent experiment, resulting in 20 samples that were coresolved across ten DIGE gels along with a Cy2-labeled internal standard. ( a ) Representative DIGE gel from the 10-gel set. ( b ) A protein feature ( white arrow in panel A , identifi ed by mass spectrometry as Hsp27) was identifi ed as signifi cantly changing (ANOVA p < 0.05). ( c ) PCA performed on the unfi ltered 1,012 features matched across all ten gels clearly organized the samples into three clusters defi ned by PC1 and PC2, with each cluster containing each of the 5 time points rather than the anticipated clustering by time point. Closer inspection of the samples revealed that the samples in each cluster came from the same cell passage number in the experiment. These results indicated that far more variation was derived from changes occurring in the primary cells during tissue culture, and this experiment was aban-doned due to the tremendous risk that any observed univariate change (such as was found for Hsp27) simply arose stochastically rather than due to biologically signifi cant reasons.


But without the utility of visualizing this very complex dataset with PCA, this high degree of potentially confounding background variation would likely have remained undetected.

1. Statistical power is the ability to visualize an X -fold magnitude change (effect size) at a Y -% confi dence interval (e.g., p < 0.05) and be “correct” (usually expressed as power of 0.8, or 80% of the time it is “correct”). Statistical power depends heavily on the analytical variation of the instrumentation as well as the number of independent (biological) replicates. For example, the experimental noise of DIGE is extremely low owing to the internal standard experimental design, enabling statistically powered experiments with very few biological replicates ( 1 ) .

2. Technical and biological replicates are important for ensuring the accuracy and biological signifi cance of quantitative measure-ments. Technical replicates are necessary to control for variation in the analytical measurement. However, biological replicates are vital to assess whether or not changes in protein abundance/modifi cation are descriptive of the biology rather than arising from unanticipated sources of experimental variation (e.g., inter-subject variation, sample preparation variation, analytical variation of the instrument). Technical replicates provide con-fi dence in the result from the tested samples but do not provide any confi dence to the biological relevance. Determining whether any observed changes come from the biology rather than technical variation can only be assessed with independent biological replicates. In cases where technical noise has been demonstrated to be suffi ciently low, then biological replicates can also serve as technical replicates, which is what is typically done with the DIGE platform.

3. Pooling independent (biological) replicates should be done with extreme caution with respect to the statistical power of the resulting data and is not advised. If it is known a priori that technical variation is low between samples, then pooling can be effective, but if it is high, then a pooling strategy can be disas-trous. Even with the low analytical noise of DIGE, the pooled N = 1 comparison on a single gel assumes that the averaging of populations is refl ective of biological signal. In some cases it may be valid to create subpools from a larger experiment, to either produce suffi cient material or to minimize costs (the “economics of proteomics”), but in these cases it is still essen-tial to maintain some degree of individualization of samples to retain statistical power ( 8 ) .

4. Notes

44 D.B. Friedman

4. These programs differ mostly in the algorithms used to detect protein features (boundaries) and gel-to-gel alignment/registration (e.g., vector-based, image warping). In general, they all provide powerful analytical tools coupled with univariate (Student’s t -test, ANOVA) and multivariate statistical analyses (e.g., PCA and hierarchical clustering) that can be extremely benefi cial in evaluating abundance changes of individual protein features as well as global expression patterns that can help discern changes that describe the biological phenotype from those that arise from unanticipated variation in the experiment.

5. SameSpots employs a strategy of image prealignment followed by the same spot boundaries applied to all features across a data-set. Whereas this leads to no missing values in the multivariable space, it can also condense information from multiple features into the same spot boundary in regions of feature crowding. The DeCyder approach defi nes spot boundaries on the different gels independently, and then employs a matching algorithm to register features across gels. While this yields a higher resolution for features in crowded regions, it typically involves more man-ual feature editing and can also lead to missing values in the dataset which must then be imputed during multivariable statis-tics (although there is the option to analyze only data that have been registered in every gel). Both platforms enable the editing of spot boundaries and feature registration across the gel set to abrogate these issues, and ultimately both are capable of deliver-ing high-caliber quantitative DIGE results using both univariate and multivariate statistical analyses.

6. It is common to allow for mismatches in the dataset with DeCyder, thereby including some features that may not have a proper registration in every gel. However, this leads to missing values that then must be imputed into the multivariate analysis, which then imposes assumptions about data that do not exist. Focusing on 100% matches in DeCyder alleviates this issue at the expense of loosing real information from an uncorrected mismatch. Another approach is to construct the base set allow-ing for one or even two mismatches and then apply a 100% match fi lter to the data in step 9 in Subheading 3.3.1 .

7. Up to fi ve principal components can be selected in DeCyder, and these can be displayed in any 2- or 3-way combinations. Three-way comparisons are displayed on a three-dimensional projection that can be rotated with the computer mouse (right clicking and dragging).

8. Perform an ANOVA or Student’s t -test in the unfi ltered data-set along with the PCA and other calculations. Then, select “fi lter dataset” using this criterion with a p -value set to some meaningful threshold, such as p < 0.05 or 0.01. Then, repeat the PCA calculation with this new fi ltered dataset selected.


9. By performing the prealignment, the SameSpots approach enforces no missing values by having all features analyzed with the same spot boundaries. But this is at the expense of loosing information from features that are not well resolved on all gels (feature crowding).

10. SameSpots provides for the visualization of as many principle components as can be detected as contributing to the variance. Any of these principal components can be visualized in pairwise 2-dimensional displays.

11. When only two conditions are present (e.g., mutant vs. wild type), applying an ANOVA or Student’s t -test to fi lter the data should necessarily designate PC1 as organizing the samples by condition, although in these cases this test may still be useful to assess the clustering within each classifi cation to determine if the biological signals are uniform or infl uenced by other dimensions of principal components.

12. Dye bias is controlled for by labeling half of the samples with Cy3 and the other half with Cy5 from a given experimental condition. The groups separated by PC1 in Fig. 3b each have two members from each experimental condition, thereby negating any changes due to dye bias coming through a statis-tical test between biological groups.

13. Using the two-dye system, dye bias does not contribute to the experimental variation if the internal standard is always labeled with one dye (Cy3) and the experimental samples are always labeled with the other (Cy5).

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