Tissue Proteomics by One-Dimensional Gel Electrophoresis

Article pubs.acs.org/jpr

Tissue Proteomics by One-Dimensional Gel Electrophoresis Combined with Label-Free Protein Quantification Andrej Vasilj,†,§ Marc Gentzel,†,§ Elke Ueberham,‡ Rolf Gebhardt,‡ and Andrej Shevchenko*,† †

Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany Institute of Biochemistry, Faculty of Medicine, University of Leipzig, Johannesallee 30, 04103 Leipzig, Germany

‡

S Supporting Information *

ABSTRACT: Label-free methods streamline quantitative proteomics of tissues by alleviating the need for metabolic labeling of proteins with stable isotopes. Here we detail and implement solutions to common problems in label-free data processing geared toward tissue proteomics by one-dimensional gel electrophoresis followed by liquid chromatography tandem mass spectrometry (geLC MS/MS). Our quantification pipeline showed high levels of performance in terms of duplicate reproducibility, linear dynamic range, and number of proteins identified and quantified. When applied to the liver of an adenomatous polyposis coli (APC) knockout mouse, we demonstrated an 8-fold increase in the number of statistically significant changing proteins compared to alternative approaches, including many more previously unidentified hydrophobic proteins. Better proteome coverage and quantification accuracy revealed molecular details of the perturbed energy metabolism.

KEYWORDS: tissue proteomics, label-free quantification, LC−MS/MS, one-dimensional gel electrophoresis, mouse liver, adenomatous polyposis knockout mice

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INTRODUCTION Accurate quantification of the proteome in cells and tissues is required for adequate interpretation of the functional consequences in gene regulatory studies, including gene knockouts or RNA interference experiments (reviewed in refs 1, 2). Poor correlation between mRNA levels and concentrations of corresponding protein products has become a common notion in the proteomics/transcriptomics fields.3 Furthermore, system biology approaches aiming at modeling metabolic and functional features of cells and organs rely considerably on precise knowledge of both absolute amounts and relative (fold) changes of proteins in different physiological conditions.4 Although cell culture is an established model for the study of processes occurring in cells, its relevance and value is limited when expanding to the organismal level. More specifically, cultured cells may experience changes in gene expression and post-translational modifications.5 Also, tissues preserve native three-dimensional cellular organization and intracellular communication as well as a specific distribution of nutrients, metabolites, and signaling molecules. Hence, direct analysis of tissue proteomes becomes essential. Mass spectrometry offers a variety of protein quantification techniques with the most broadly applicable ones based on the quantification of tryptic peptides (reviewed in ref 6). Peptide counterparts labeled with stable isotopes are used as internal standards in techniques such as SILAC,7 Super-SILAC,8 iTRAQ,9 ICAT,10 and AQUA,11 among others, in which signals of unlabeled and labeled peptides are directly compared within the same LC−MS/MS analysis. Isotopic labeling strategies © XXXX American Chemical Society

have been the traditional choice since they are less affected by common LC−MS variability factors such as retention time drift, ion suppression, and spray instability, among others. AQUA and QCAT12,13 also support absolute quantification of proteins because the molar amounts of synthetic peptide standards (AQUA) or the amount of recombinant protein (QCAT) are known. However, concerning the quantification of tissues from higher organisms, the isotopic labeling strategies are losing ground even for common model organisms. In respect to tissues, incomplete and tissue-dependent incorporation of labeled amino acids14 is common, and interconversion of amino acids should be carefully controlled.15 A workaround solution might be to establish a reference cell culture and use labeled proteins as spiked internal standards for quantifying the relative differences between tissue proteomes.16,8 In this case, the cell culture should adequately represent the tissue proteome and is best established for each individual tissue of interest. Alternatively, peptides produced from compared tissue proteomes can be chemically labeled. Chemical labeling techniques such as iTRAQ are limited in the number of samples that could be quantified in parallel, the completeness of labeling reactions should be strictly controlled, and interference from sample preparation buffers and contaminants is common.17 Generally, all labeling strategies will struggle with cost-efficiency and experimental flexibility. Thus, we reasoned that a carefully designed label-free workflow might Received: February 14, 2012

A

dx.doi.org/10.1021/pr300147z | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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X-100 was from Serva Electrophoresis GmbH (Heidelberg, Germany).

be the better way of carrying out quantitative proteomics in tissues. The extracted ion chromatogram (XIC)-based label-free quantification approach, enabled by recent algorithmic advances and by the improvements of mass spectrometric instrumentation, replaces costly and laborious biochemical steps with informatics procedures (reviewed in refs 18−20). Here, the intensity of every isotopic distribution characteristic of a peptide and integrated over its chromatographic elution time is deemed to be a quantitative “feature”. These features are aligned between different acquisitions using primarily their accurate masses and assuming the possibility of retention time shifts. The result is a master map of all features with intensities reported across a theoretically unlimited number of runs. Comparison of feature abundances between the chromatographic runs reveals relative changes between peptide amounts. Protein mixtures of any origin are directly analyzed in a technically identical way, which makes the approach particularly attractive for quantifying the proteomes of individual tissues or even entire organisms.21,22 Software such as SuperHirn,23 OpenMS,24 MaxQuant,25 Progenesis LC−MS (Nonlinear Dynamics, Newcastle, UK), and Elucidator (Rosetta Biosoftware, Cambridge, MA), among others, is available to perform the essential steps of data processing. Despite its immediate appeal, the robustness and accuracy of label-free quantification have previously been questioned.8 Additionally, while there is a plethora of software tools available for individual data processing steps, the design of the overall label-free workflow is not a trivial task. In particular, experiments are designed with varying numbers of conditions, technical replicates, biological replicates, prefractionation techniques, instrumentation platforms, and acquisition methods. Such variables may affect the way the data processing software can be used and they will certainly affect the statistical procedures. Here, we outline a label-free quantification pipeline based on existing, well-established, open-source software in combination with modern statistical approaches that has been tailored for quantifying tissue proteomes. We highlight and provide solutions for some of the missing links when combining various software tools, and where necessary, we make improvements to the available open-source tools, while adhering to the increasing need for quality controls in proteomics.26 With the application of our informatics pipeline to a model mixture of standard proteins, we demonstrated a robust and reliable quantitative analysis. Finally, we validated the full one-dimensional gel electrophoresis liquid chromatography tandem mass spectrometry (geLC MS/MS) procedure with an analysis of hepatocytes from a liver-specific adenomatous polyposis coli (APC) knockout mouse, which confirmed previously published results and revealed new proteomic changes due to increased quantitative accuracy and improved dynamic range.

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Peptide and Protein Standards

EGVNDNEEGFFSAR was purchased from Sigma-Aldrich (Munich, Germany); TTPAVLDSDGSYFLYSK, VLETKSLYVR, VLETK(ε-AC)SLYVR, and VLETK(ε-Met)SLYVR were from PSL GmbH (Heidelberg, Germany); standard proteins were from Sigma-Aldrich (Munich, Germany). See Supporting Information 1 Table 1S for further details. Preparation of Model Protein Mixtures

Stock solutions of standard proteins were made in water at the concentrations of 1 pmol/μL. Absolute protein concentrations were determined by amino acid analysis (Functional Genomics Center Zurich, Switzerland). Protein stocks were digested in solution with sequencing-grade modified trypsin (Promega, Madison, WI) according to standard protocol.27 Individual digests were mixed in different ratios. The absolute amounts of each digested protein loaded onto the LC column in subsequent experiments ranged from 80 amol to 1.4 pmol; see Supporting Information 1 Table 2S for the compositions of individual samples. APC-KO Mice

Triple transgenic animals were produced by interbreeding the APCflox/flox28 with both the LT2-mouse (TALAP2)29 and the LC1-mouse (ptetO-Cre).30 The resulting ptet-Cre-APCflox/ flox and TALAP2-APCflox/flox were interbred to achieve the final ptetO-Cre-TALAP2-APCflox/flox mice. Heterozygous APCwt/flox mice and wildtype mice with mixed genetic background NMRI/Bl6 were used as controls. Knockout mice were withdrawn from doxycycline for 11 days before removal of liver. Animal experiments were carried out in accordance with the European Council directive (86/609/EEC) and were approved by local authorities. Extraction and Gel Separation of Mouse Proteins

Hepatocytes were isolated using an in vitro perfusion technique.31 Briefly, liver was perfused with calcium -free buffered saline and subsequently with collagenase (1 mg/mL, 240 U/mg; Biochrom AG, Berlin, Germany). Periportal hepatocytes from wildtype mice (mixed background NMRI/ Bl6) were isolated by a modified digitonin/collagenase perfusion technique.32 Cell suspension was centrifuged thrice at 70g for 5 min. Hepatocytes were collected in portions of one million cells, snap frozen in liquid nitrogen, and stored at −80 °C. Frozen cell pellets were resuspended into a lysis buffer containing 150 nM NaCl, 1 mM EDTA, 50 mM Tris-HCl (pH 7.5), 1 tablet Roche protease inhibitors, 0.2% w/v CHAPS, 0.1% w/v OGP, 0.7% v/v triton X-100, 0.25 μg/mL DNase and RNase. Cells were disrupted by passing through a 0.4 mm syringe and left on ice for 1 h. Lysates were cleared by centrifugation for 15 min at 16110 rcf with an Eppendorf 5415D. The lysates were quantified using a standard BCA assay (Applichem, Darmstadt, Gemanry) resulting in 5.4 mg/mL (knockout 1), 3.9 mg/mL (control 1), 5.3 mg/mL (knockout 2), and 4.4 mg/mL (control 2). A 100 μg sample of total protein from each lysate was loaded into 1.3 cm wide gel lanes in a custom-poured SDS-PAGE 1 mm thick mini-gel. Upon electrophoresis, each sample lane was cut into 13 slices. In gel digestion was performed according to standard procedures;27 150 fmol of enolase (S.cerevisiae) and 75 fmol of β-galactosidase (E.coli strain K12) were added to each digest along with 15.5 fmol of five standard peptides. The yeast enolase shares

MATERIALS AND METHODS

Solvents and Common Chemicals

Solvents were purchased from Fisher Scientific (Waltham, MA); common chemicals and buffers were from Sigma-Aldrich (Munich, Germany); Complete Ultra protease inhibitors were rom Roche (Mannheim, Germany); 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), n-octyl β-Dglucopyranoside (OGP), DNase, RNase, and bicinchoninic acid were from Applichem (Darmstadt, Germany); and Triton B



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only one tryptic peptide with the mouse enolase, and E.coli β-galactosidase shares none.

and actual FDR are provided for each LC−MS/MS run in Supporting Information 1, Table 7S. Next, Protein Prophet38 was used to assess the protein-level FDR and a minimal probability of 0.95 was required for identified proteins.

LC−MS/MS Analyses

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Digests were redissolved in 18 μL of 5% formic acid, and 5 μL were injected into a Dionex Ultimate 3000 nano-HPLC system. The system was equipped with a 300 μm i.d. × 5 mm trap column and a 75 μm × 15 cm analytical column (Acclaim PepMap100 C18 5 μm/100A and 3 μm/100A, respectively, both from Dionex, Idstein, Germany). Solvents A and B were 2% acetonitrile and 60% acetonitrile in aqueous 0.1% formic acid, respectively. Samples were loaded on the trap column for 5 min with solvent A flow of 20 μL/min. The trap column was then switched online to the separation column, and flow rate was set to 200 nL/min. Protein digests were analyzed using 155 min elution program: 6% B for 3 min; linear gradient 6% to 58% of B in 132 min; maintained at 58% of B for 8 min; reequilibration of the columns with 6% B for another 12 min. Spectra were acquired on a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). The FT survey scans were acquired from m/z 380 to 1200 with the target mass resolution of 60,000 (full width at half maximum, FWHM) at m/z 400. Automated gain control (AGC) target ion count was set to 1 × 106 for FT MS scans with maximal fill time of 500 ms. MS/MS spectra were acquired at the linear ion trap under normalized collision energy of 35%; dynamic exclusion time of 180 s; precursor ion isolation width of m/z 3.0; spectra were recorded in centroid mode; target ion count was set to 5 × 104 with maximal fill time of 100 ms. The data-dependent acquisition (DDA) cycle consisted of acquiring FT MS survey spectrum followed by 6 MS/MS spectra with a fragmentation threshold of 4000 ion counts; singly charged and chargeunassigned precursor ions were excluded. Lock mass was set to the singly charged ion of dodecamethylcyclohexasiloxane ion ((Si(CH3)2O))6; m/z = 445.120025).33

RESULTS AND DISCUSSION

Pipeline for Label-Free Quantitative Proteomics

The cornerstone of the label-free approach is the feature extraction and alignment software, yet several processing and statistical steps are required for a comprehensive analysis of protein abundance changes. Although open source software is available to perform all necessary steps, it is difficult to combine them in technical terms of input/output and automation and in rational terms of finding the best combination of software to achieve accurate quantification. Here, we employed SuperHirn for the essential task of feature extraction/alignment, and around its requirements and capabilities we have composed the remaining components (Figure 1).

Data Processing

The Trans-Proteomic Pipeline (TPP) v 4.3.134 software was used for converting spectra from proprietary format of Thermo Fisher Scientific (.raw) into formats required by analysis software. All settings for the TPP were default (as provided by the interface) except the probability cutoff was removed so that all identifications remained in the output. Mascot v2.2.04 (Matrix Science, London, UK) was used for peptide identifications in the IPI mouse v3.76 database, to which sequences of human keratins and porcine trypsin were added. A separate database including only sequences of keratin contaminants, trypsin, and standard peptides and proteins was used for identifying the spiked standards. Database searches were performed under the following settings: precursor mass tolerance of 5 ppm; fragment mass tolerance of 0.6 Da; fixed modification: carbamidomethyl (C); variable modifications: acetyl (protein N-terminus), oxidation (M); 2 missed cleavages were allowed. A customized version of SuperHirn v0.0323 was used for feature extraction and alignment. DanteR35,36 was used for data normalization and inferential statistics. A comprehensive list of software parameters for Mascot, SuperHirn, and DanteR is provided in Supporting Information 1, Tables 4S−6S.

Figure 1. Pipeline for label-free quantification. Protein samples are prefractionated by 1D SDS-PAGE. Gel lanes are cut in 10−20 slices that are separately digested with trypsin, and digests are analyzed by nanoLC−MS/MS. Peptide features are recognized in MS1 spectra, and their abundances are time-integrated, aligned over all runs, and combined with peptide identifications in a master map. Quantitative profiles of peptides are normalized, and inferential statistics are performed.

False Discovery Rates

FDR were assessed using the decoy search option of Mascot software.37 We then used the Mascot parser and in-house scripts to adjust the Mascot significance threshold such that the FDR was maintained below 5%. Mascot significance thresholds

Fractionation of complex proteome samples increases proteome coverage.39 It has been repeatedly shown that one-dimensional sodium dodecyl sulfate polyacrylamide gel C



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electrophoresis (SDS-PAGE) improves proteome characterization (reviewed in ref 40) and brings unique capacity in solubilizing and separating membrane proteins.41 Additionally, SDS-PAGE provides an effective sample cleanup and serves as convenient interface between protein preparation and quantification. Following SDS-PAGE, the entire electrophoresis lane is typically cut into 10−30 slices, which are digested and analyzed independently. Because the same protein can be distributed across several slices, peptide features are aligned across all gel slices and their intensities are summed together, providing a merged view. Gel slices were independently digested and analyzed by LC− MS/MS. To compensate for peptide composition-dependent matrix effects we spiked multiple standard peptides and predigested standard proteins into each sample after in-gel digestion for data normalization at a later stage. Using the Trans-Proteomic Pipeline (TPP), raw data from LC−MS/MS runs were converted into open formats usable by both peptide identification software and SuperHirn. Peptide features were automatically generated in each run, and their abundances were integrated and combined with peptide identifications. Sequence-annotated features were aligned into a master map after which their abundances were summed across all runs. Identified features were filtered by peptide-level false discovery rates. At this stage miscleaved peptides were filtered out, while corresponding completely cleaved counterparts were accepted if their abundance was 10-fold higher than the abundance of the miscleaved peptide. The same routine was applied for peptides with variable modifications and their unmodified counterparts. Finally, tabular data containing the abundances of identified peptides were loaded into DanteR for further normalization and inferential statistics. With a complete label-free quantification procedure at hand, we first validated the LC−MS and software performance using mixtures of standard proteins. Next, we validated the entire geLC−MS/MS quantification pipeline by analyzing the proteomic changes in the liver of APC-knockout mice.

Figure 2. Scatter plot of technical duplicates from the sample 1 of model mixtures used for the pipeline validation. Raw integrated feature intensities were taken from the SuperHirn master map, log 2 transformed, and plotted. Black dots: all features; red dots: identified features.

Data points lying on the axes represent singular features found in only one of the technical duplicates; they comprise 35.9% of all detected features in the example Figure 2. However, only 1.1% of them were supported by MS/MS identifications, all of which were extremely low scoring by Mascot. Manual inspection of singular features indicated that most of them were originating from error-prone detection of bona f ide chromatographic peaks (see an example in Supporting Information 1, Figure 10S). There the software recognized four features within a single chromatographic peak: one bona f ide feature corresponding to the major peak and three artificial features recognized at its trailing end. While this indicates that these singular features could safely be excluded from further analyses, it would nevertheless be beneficial to reduce this effect. One possible solution is to increase the minimum thresholds for the signal-to-noise ratio and feature intensity, albeit it reduces the sensitivity. Because we work only with identified features, we chose to recognize and merge these duplicates considering matched peptide sequences within a selected retention time window after the creation of the master map. When applied to the model protein mixture data, summation of the abundances of identical peptides (including different peptide charge states) within 5 min time windows merged 118 of the total of 359 features identified in samples 1a and 1b, including 43 out of 57 singular features. The R2 value of sample 1 duplicates including singular features was 0.40. After merging and subsequent removal of remaining singular features, the resulting R2 was 0.96, indicating a high level of correlation between the duplicates (Supporting Information 1, Table 8S). Selection of peptides for protein quantification is discussed in the next section. Here we only note that by using the averaged intensity of the top three most abundant peptides to represent each standard protein in the model mixtures, we achieved a quantitative linear dynamic range for serum transferrin greater than 1600-fold (R2 0.999) between the lowest and highest loadings of 350 amol and 567 fmol, respectively. In the same way, calibration curves for other standard proteins were linear within the range of as low as 80 amol to as high as 1400 fmol (see Supporting Information 1 Table 2S for on-column loadings of each standard protein and Figures 11S−17S for calibration curves with corresponding R2 values).

Validation of the Data Acquisition Method and Processing Software

We first tested if LC−MS/MS data acquired from multiple independent analyses were correctly interpreted by our software pipeline. The model experiment used 10 samples made of eight digests of different standard proteins mixed in varying but precisely known ratios. Across these 10 samples, the amounts of two digested proteins were kept constant in each sample, while three digests each were added in increasing and three in decreasing amounts. The lowest and highest amount of loaded protein differed by more than 4 orders of magnitude (Supporting Information 1, Table 2S). All samples were analyzed in technical duplicates. To estimate the technical reproducibility, we calculated the coefficient of determination R2 for the abundances of aligned features within successively injected duplicates (Supporting Information 1, Table 8S). An exemplary scatter plot of feature intensities is shown in Figure 2 and the remaining plots in Supporting Information 1, Figures 1S−9S. Expectantly, feature correlation improved with intensity: intense features had a higher signal-to-noise ratio and consisted of more MS1 data points, which created a smoother chromatographic peak. They were also less influenced by local spray instability and matrix effects. Similarly, features annotated with MS/MS identifications (red points) were also considerably better correlated than features having no identifications (black points).

Calculation of Protein Abundances

A fundamental assumption of mass-spectrometry-based quantitative proteomics is that peptide abundances faithfully represent protein content. To test whether this is truly the case, we focused on one protein from the model mixtures and monitored D



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how the peptide abundances represented the protein’s known concentration trend. The abundances of the 56 peptides of serum transferrin across all 10 standard protein mixtures are shown in Figure 3. The peptide profiles in the figure are

Thus, in certain cases matching peptide identifications back to MS1 features may become ambiguous. We solved this problem by storing MS2 scan numbers in the SuperHirn output xml files. Also, SuperHirn allowed features to potentially match several times between runs, which created systematic quantitative errors when merging features together on the basis of peptide sequences. In our implementation each feature was matched at most one time during the alignment process. As an extension to this point and to avoid the matching of true features of one run to low intensity tail features of another run, we sorted all features by intensity, giving the most intense features matching priority during alignment. All together, these changes improved the software performance and quantification robustness. Lastly, we modified the originally Unix-based source code to also compile for Microsoft Windows. The QT project files of our in-house implementation which can be compiled for Unix, Macintosh, or Microsoft Windows, including a custom GUI, are provided as-is upon request.

Figure 3. Profiles of 56 quantified peptides from the standard protein serotransferrin across all 10 model protein mixtures. The protein content was 350 amol in sample 1 to 1.13 pmol in sample 10. Peptides were sorted by and normalized to the sum of their intensities across the 10 samples. The analyzed samples contained 10 other proteins, whose peptides are not shown here. Green lines represent the top 5 most abundant peptides. Purple lines show the next 5 most abundant peptides. Light gray lines show the remaining peptides. Thick dotted black line shows the theoretical trend.

Normalization of Feature Intensities

Label-free quantification is inherently prone to technical variability: LC retention time drifts, local spray instabilities, ion suppression, and other matrix effects all being common reasons of quantification discrepancies. We propose that these issues can be resolved by applying three normalization procedures to “raw” peptide abundances: normalization to the abundances of internal standards, log2 transformation, and mean subtraction. Each procedure corrects for different effects. We spiked internal peptide standards into each in-gel digest to correct for platform-dependent technological instabilities. Log2 transformation is a common statistical procedure which reshapes the peptide abundance distribution toward a Gaussian distribution, allowing the later use of parametric statistical tests and thus typically achieving a higher power of statistical inference.44 Finally, to account for potential differences in total protein loading, there are several options to choose from such as mean subtraction, linear regression, non-linear regression, and quantile normalization.45,46 However, excessive normalization of a data set can lead to overfitting of data and subsequent invalidation of the quantitative inferences.47 For our purposes, we opted for mean subtraction. This sufficed in compensating for any total ion current (TIC) intensity disparities between the samples while not significantly perturbing the data. The starting point for data normalization is the list of peptide abundances summed across each gel slice of each sample. The summing procedure along with the normalization to internal standards was automated by in-house software but may just as well be performed manually in spreadsheet software. The list was then loaded into DanteR, log 2 transformed, and mean subtracted.

color-coded according to the abundance of corresponding features. The top five most abundant peptides (green lines) are the closest in following the theoretical trend (black line). This visualizes the previous notion by Silva et al.42 that abundances of the three most intense peptides adequately represent the protein content. The extent of the peptide abundance variance increases with reduced signal-to-noise ratio of peptide peaks. Hence, considering the abundances of all peptides instead of the 3−5 most abundant peptides may introduce systematic errors. We concluded that it is unnecessary and error-prone to consider more than the top five most intense peptides when recalculating protein concentrations. For further quantification we considered a minimum of two and a maximum of five most intense peptides for the representation of each protein. Note that miscleaved peptides and peptides bearing variable modifications (e.g., oxidation of methionine residues) were disregarded. Their completely cleaved or unmodified counterparts were considered only if they were at least 10-fold more abundant compared to the miscleaved or modified peptide, as judged by their peak areas on corresponding XIC traces. Software for Feature Extraction and Alignment

Recognizing, integrating, and aligning peptide features in multiple LC−MS/MS runs is the key data processing step in label-free quantification. We modified the original code of SuperHirn software to adjust it to the type and scale of analyses we performed. The original version created by Mueller et al.23 made use of the document object model (DOM) style xml reader for parsing pep.xml files. This was problematic as these files could be very large and a DOM structure could further inflate their size 10-fold, making them unloadable on a standard desktop computer. By replacing this code with the QT xml stream reader,43 significantly larger files could be parsed. Another issue was that the xml structure that SuperHirn creates for storing the extracted features only retains their retention time, but not the scan numbers of corresponding MS2 spectra.

Statistical Inference of Quantitative Changes

In order to generate statistically valid conclusions from the quantitative comparison of analyzed proteomes, we applied an ANOVA model similar to what was described in Clough et al.48 and Karpievitch et al.49 and as implemented in DanteR. In this way we achieved a high statistical power by including the variances from factors such as biological replicates, technical replicates, and even the intensity variances of the peptides of each protein. This benefit can be explained intuitively whereby having multiple peptides of equal relative intensities between samples for a given protein will result in a more confident estimate of protein abundance than having multiple peptides of dissimilar relative intensities. Note that this information is lost E



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Table 1. Comparison of Label-Free (LF) Quantification to Previously Reported Changes in APC Knockout (APC-KO) Micea IPI IPI00626790 IPI00109275 IPI00129178

IPI00132593 IPI00117914 IPI00111908 IPI00131438

name

LF (KO/WT)

Ammonia Metabolism and Transport Glutamine synthetase 121.38*** Glutamate carrier 1 2.34** Ornithine aminotransferase 133.46*** Rhesus blood group-associated B glycoprotein not found Chemotaxis Leukocyte cell-derived chemotaxin 2 not found Receptor Arginine vasopressin receptor 1A not found Other Functions Ribonuclease 4 not found Ammonia and Urea Metabolism Glutaminase2 0.18** Arginase1 0.17*** Carbamoyl-phosphate synthase 1 0.52** Carbohydrate Metabolism Phosphoenolpyruvate carboxykinase 1 0.08***

cDNA microarrayb

qRT-PCRb

8 13.9 3.6 not spotted

5.3 8.8 11.9 16.7

3.7

4.1

2.7

1.2

3.8

2.7

0.21 0.14 0.4

0.08 0.17 0.44

0.62

0.22

a

LF KO/WT indicates the fold change observed in our data between the APC-KO and the control mice which remained on doxycycline treatment (WT). Statistical significance is denoted by stars (* = p < 0.05, ** = p < 0.01, *** = p < 0.001). cDNA and qRT-PCR data are also presented as fold changes. bMicroarray and PCR data is taken from Benhamouche et al.55

when applying simple t-tests because the peptide abundance variances are not considered. In addition to generating probability values, this approach also generated fold change estimates that were again based on the ANOVA linear model. Because this test should be applied to each of potentially thousands of proteins observed in an experiment, this leads to a large number of tests and if a typical significance threshold of 5% is used, a potentially large number of false positives. To resolve this, the confidence of each test was systematically adjusted using a multiple test correction strategy based on the quantitative FDR established by Benjamini and Hochberg.50 In practical terms, for this step we continued data processing in DanteR by applying the ANOVA function (settings are provided in Supporting Information 1, Table 6S) and the multiple test correction function to previously normalized data.

242 were up-regulated. Proteins were changing from 465-fold (Cyp46a1) to 12638-fold (Lhpp), indicating a high dynamic range. The distribution of statistically significant protein abundance changes is visualized by a volcano plot in Supporting Information 1, Figure 22S. We hypothesized that our label-free pipeline should produce results corroborating previously found changes in the liver proteome of APC-KO mice. From the WNT signaling pathway, protein kinase alpha (PKA), casein kinase 2 (CK2), protein phosphatase 2 (PP2A), calcyclin binding protein (SIP), cullin 1 (Cul1), protein phosphatase 3 (CaN), and β-catenin were quantified (KEGG: mmu04310 wnt signaling pathway used for pathway members52 as well as Hilger and Mann53). The abundance of β-catenin changed in the expected direction (up-regulated 1.54-fold in the KO mice) and with statistical significance (p = 0.02). The low magnitude of fold change that we observed may be because there are two populations of β-catenin, one that is membrane junction-bound and the other that resides in the cytoplasm.54 Only the cytoplasmic β-catenin is expected to change, but we quantified the two pools together, which might diminish the magnitude of change observed. APC itself was not identified in the experiment. There are several known proteins expressed downstream of the WNT signaling pathway. Their mRNA levels were previously quantified using microarrays and quantitative RT-PCR.55 These results, along with the quantitative data from our experiment, are shown in Table 1. We were unable to detect four proteins from this list, but the proteins we do report were all changing concordantly with the published changes and with statistical significance. Furthermore, our data showed significantly higher magnitudes of change for glutamine synthetase (GS) and ornithine aminotransferase (Oat) as compared with the respective mRNA levels. This is in accord with previous findings that show translational regulation as an important component of the expression levels of these proteins.56,57 For glutamine synthetase, in particular, it was shown that the magnitude of transcriptional and translational regulation varies considerably from organ to organ and is especially high in liver.58,59

Proteome Level Changes in the Liver of APC-KO Mice

Using the pipeline, we assessed full proteome changes in the liver of APC-KO mice. The adenomatous polyposis coli (APC) gene acts within the WNT signaling pathway as a member of the β-catenin destruction complex.51 With no functional APC complex targeting β-catenin for degradation by ubiquitination, it accumulates in the cytoplasm, continuously translocates into the nucleus, and triggers downstream gene regulation. The analysis was performed using biological duplicates of control (WT) versus APC-knockout (KO) mice. The mice were a triple transgenic line with Cre-Lox recombination of the APC gene. The control mice were continuously treated with doxycycline, while the knockout mice were removed from doxycycline treatment to induce the knockout and sacrificed after a further 11 days. The liver was extracted, and hepatocytes were isolated using an in vitro perfusion technique. By applying peptide-level FDR of under 5% and a Protein Prophet identification probability of >0.95 as protein-level FDR, we quantified a total of 1789 proteins. The complete list of proteins is provided in Supporting Information 2 and includes Protein Prophet p-values reported for each hit. Using peptide-level ANOVA-based inference we were able to assign statistical significance to the quantitative changes of 454 proteins. Out of these, 212 were down-regulated in the APC-KO mice and F



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Table 2. Comparison of Label-Free (LF) and DiGE (2D) Protein Quantificationa Uniprot

name

P29758 P15105 Q61176 Q8C196 P35492 Q3UEH8# Q8BGQ7 Q564P4# Q283N4 Q9DBP5 Q8JZK9

Ornithine aminotransferase Glutamine synthetase Arginase 1 Carbamoyl-phosphate synthase Histidineammonia-lyase Phenylalanine-4-hydroxylase Alanyl-tRNA synthetase Adenine phosphoribosyl-transferase OHCU decarboxylase UMP-CMP kinase Hydroxymethylglutaryl-CoA synthase 1 Farnesyl pyrophosphate synthase Plasma retinol-binding protein Cytochrome b5 Myo-inositol-1-phosphate synthase A1 2-Hydroxyphytanoyl-CoA lyase Dihydroxyacetone kinase D-Lactate dehydrogenase FBP1 ATP synthase subunit a ATP synthase subunit b Isocitrate dehydrogenase [NAD] subunit a Hepatic fructokinase Aldehyde dehydrogenase 2 Lactoylglutathione lyase Carboxylesterase 1 Carboxylesterase 2 Carboxylesterase 3 Carboxylesterase 31 Catechol-O-methyltransferase

Q920E5 Q00724 P56395 Q9JHU9 Q9QXE0# Q8VC30 Q7TNG8 Q9QXD6 Q03265 P56480 Q9D6R2 Q8CD98# P47738 Q9CPU0 Q8VCC2 Q80VX3# Q8VCT4 Q63880 Q8BIG7#

2D KO/ WT

LF KO/ WT

19.6 5.5 −2.4 −3.7 −4 2 2.1 3.4 −1.7 −1.7 3

133.5*** 121.4*** −6*** −1.9** −10.9** 1.6*** 2.9*** 4.5*** −3.8* −1.5* 2.3

1.8 4 1.2 1.5

1.8 6.7** ND 18.1*

−1.8 −1.5 2.5 −2 −2.5 −2 1.8

−1.3* −4.5*** 1.4* −2.7*** −1.1* −1 3.5***

−1.8 1.8 −1.5 1.8 1.8 −1.9 −1.7 1.6

−1.9 1.6*** −15.4 ND ND −1.2 −5.5*** 1.2

Uniprot P40936 Q78JT3 Q9CXN7 Q99LB7 Q9D5J8# P20108 P17563 P08113# Q01853 Q91W90# Q9D1Q6 Q3TF72# P27773 P27005 Q64374 P63260# P05784 P11679 Q8C5W3 Q3TH46# Q3THE6# P07724 Q00896# A2AKN8# A2AKN9# Q9DCS2

name Indolethylamine N-methyltransferase 3-Hydroxyanthranilate3,4dioxygenase Probable isomerase MAWBP-1 Sarcosine dehydrogenase Glutathione S-transferase mu 6 Thioredoxin-dependent peroxide reductase Selenium-binding protein 1 HSP90-b Transitional endoplasmic reticulum ATPase Thioredoxin domain-containing protein 5 Thioredoxin domain-containing protein 4 Protein disulfide-isomerase Protein disulfide-isomerase A3 S100 calcium-binding protein A8 Regucalcin Actin,cytoplasmic 1 Cytokeratin-18 Keratin, type II cytoskeletal 8 Tubulin-specific chaperone cofactor E-like protein Radixin Ferritin light chain 1 Serum albumin a-1-Anti trypsin1−6 Major urinary protein Mup 8,11 Major urinary protein Mup 2 Hypothetical S-adenosyl-Lmethionine-dependent methyltransferase

2D KO/ WT

LF KO/ WT

−3 −1.6

−12.9 −2.8***

−1.5 −1.5 2.5 1.6

ND −1.4 ND 3.7***

1.7 1.6 2.1

−2.7*** 1.3* −1.2

2.1

1.3**

1.5

−1.2

1.5 −1.5 2.1 1.7 2.1 2 2 1.6

1.4** −1.1 ND 1.4*** 1.9 1.6 1.3 2.8*

−1.8 −3.5 −2 −2.7 −6 2 −1.5

1 1.1 1 −9.4*** ND ND −84.4

a DiGE results are from Chafey et al.;60 all changes were statistically significant according to the authors. (#) denotes matching proteins found in our experiment under different Uniprot accession numbers. LF KO/WT indicates the fold change observed in our data between the APC-KO mice and the control mice (WT). LF statistical significance is denoted by stars (* = p < 0.05, ** = p < 0.01, *** = p < 0.001). ND indicates “not detected”.

Chafey et al.60 have previously quantified the proteome changes in the liver of APC-KO mice by 2D differential gel electrophoresis (DiGE). Table 2 shows the statistically significant changes reported in this DiGE study along with our labelfree quantitative data. These two orthogonal techniques are in good agreement. Of the proteins, 75% (42 out of the 56 reported by Chafey et al., with 27 having p < 0.05) were changing in the same direction between the two approaches. There were eight proteins that were not quantified in our study, some because they were not identified, some because their peptides overlapped with other, more likely proteins, and others because they did not meet criteria for quantification in our data. Only 6 out of 56 proteins were changing in the opposite direction, yet only one of these, selenium-binding protein 1 (Selenbp1), was statistically significant in our data. Fractionation by 1D SDS-PAGE facilitated analysis of hydrophobic proteins. In addition to peripheral and lipidanchored membrane proteins, we identified a total of 125 multi-pass and 106 single-pass integral membrane proteins (IMPs) within our list of quantified proteins. Out of all IMPs, 63 were changing in abundance with statistical significance, and none of them were reported in the previous DiGE study.60

Subcellular location annotation for the IMPs is provided in Supporting Information 2. Physiological Consequences of Proteome Alterations after APC-KO

We found further indications of the expected shift in energy production in the APC-KO mouse model. Table 3 shows that members of complexes I and III of the electron transport chain were down-regulated, while complex V showed mixed up- and down-regulation. At the same time most enzymes of the citric acid cycle (TCA cycle) were up-regulated (Table 3), confirming earlier results for the lobular distribution of isocitrate dehydrogenase (Idh).61 Apparently in the glutamine synthetase-positive hepatocytes the TCA cycle enzymes are involved in the production of intermediates for biosynthetic reactions, e.g., glutamate and glutamine synthesis.62,63 Down-regulation of the two enzymes critical for gluconeogenesis, fructose-1,6-biphosphatase 1 (Fbp1) and phosphoenolpyruvate carboxykinase 1 (Pck1), corroborates with the fact that APC-KO leads to a perivenous phenotype.63 That glycogen synthase and fatty acid synthase were also downregulated is unexpected but might be explained by metabolic zonation as well. G



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Table 3. Changes in Mouse Liver Enzymes Related to Energy Metabolisma Uniprot P03911 Q6GTD3 Q91WD5 Q91YT0 Q99LC3 Q9CQJ8 Q9D6J6 Q9DCT2

Q9CZ13 Q9DB77

Q8BVE3 Q03265 P62814 Q9DCX2 Q9D7I5 Q91VM9 Q9D819

description Oxidative Phosphorylation, Complex I ND4L;mt-Nd4;mt-Nd4l NADH-ubiquinone oxidoreductase chain 4 Ndufa9 NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9, mitochondrial precursor Ndufs2 NADH dehydrogenase [ubiquinone] iron−-sulfur protein 2, mitochondrial Ndufv1 NADH dehydrogenase [ubiquinone] flavoprotein 1, mitochondrial Ndufa10 NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 10, mitochondrial Ndufb9 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 9 Ndufv2 Isoform 1 of NADH dehydrogenase [ubiquinone] flavoprotein 2, mitochondrial Ndufs3 NADH dehydrogenase [ubiquinone] iron−sulfur protein 3, mitochondrial Oxidative Phosphorylation, Complex III Uqcrc1 Cytochrome b-c1 complex subunit 1, mitochondrial Uqcrc2 Cytochrome b-c1 complex subunit 2, mitochondrial Oxidative Phosphorylation, Complex V Atp6v1h V-type proton ATPase subunit H Atp5a1 ATP synthase subunit alpha, mitochondrial Atp6v1b2 V-type proton ATPase subunit B, brain isoform Atp5h ATP synthase subunit d, mitochondrial Oxidative Phosphorylation, Other Lhpp Isoform 1 of Phospholysine phosphohistidine inorganic pyrophosphate phosphatase Ppa2 Isoform 1 of Inorganic pyrophosphatase 2, mitochondrial Ppa1 Inorganic pyrophosphatase

Uniprot

LF KO/WT −3.6*

Q8VCB3

−2.6***

Q9QXD6 Q9Z2V4

−2.3* −2.2***

P19096

−1.9***

Q5SVI6 P06745 Q91Y97 P17751 P09411 Q9DBJ1 Q3UEI4

−2.9*** −1.7** −2.4*** −2.4*** −2.6***

Q9CZU6 Q91VA7 Q9D6R2

3.4** −1.1*

Q8BMF4

3.2*

Q9Z2I9

−1.6*

P54071

12638.5**

Q9Z2I8

−1.3*

Q60597

−2.2***

P08249

description Glycogenesis Gys2 Glycogen [starch] synthase, liver Gluconeogenesis Fbp1 Fructose-1,6-bisphosphatase 1 Pck1 Phosphoenolpyruvate carboxykinase, cytosolic [GTP] Fatty Acid Biosynthesis Fasn Fatty acid synthase Glycolysis Gck Isoform 1 of Glucokinase Gpi1 Glucose-6-phosphate isomerase Aldob Fructose-bisphosphate aldolase B Tpi1 triosephosphate isomerase Pgk1 Phosphoglycerate kinase 1 Pgam1 Phosphoglycerate mutase 1 Pklr pyruvate kinase isozymes R-L isoform 2 TCA cycle Cs Citrate synthase, mitochondrial Idh3b isocitrate dehydrogenase 3, beta subunit Idh3a Isoform 1 of Isocitrate dehydrogenase [NAD] subunit alpha, mitochondrial Dlat Dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial Sucla2 Succinyl-CoA ligase [ADP-forming] subunit beta, mitochondrial Idh2 Isocitrate dehydrogenase [NADP], mitochondrial Suclg2 Isoform 1 of Succinyl-CoA ligase [GDP-forming] subunit beta, mitochondrial Ogdh Isoform 1 of 2-oxoglutarate dehydrogenase, mitochondrial Mdh2Malate dehydrogenase, mitochondrial

LF KO/WT −5.5*** −2.7*** −12.1*** −3.4* −7.2** −2*** −7.6*** −1.4** −1.8** −2** −3.1**

2.6*** 3.8*** 3.5*** 2.3*** −1.5*** 3.8*** −1.5** 1.6** 1.5**

a LF KO/WT indicates the fold change observed in our data between the APC-KO mice (KO) and the control mice (WT). Statistical significance is denoted by stars (* = p < 0.05, ** = p < 0.01, *** = p < 0.001).

The data processing steps involved in the pipeline are connected around a customized version of SuperHirn (available upon request), which performs the feature integration and alignment. We addressed issues of technical performance by demonstrating high duplicate reproducibility (R2 > 0.95), sensitivity (quantification as low as 80 amol), and dynamic range (over 1600-fold). Our SDS-PAGE gel approach allows for a comprehensive and unbiased analysis that is compatible with any sample preparation recipe. In APC-KO mice liver, using 1D SDS-PAGE we quantified a total of 231 single- and multi-pass integral membrane proteins, which have not been reported in previous proteomics analyses. For 27% of the integral membrane proteins we observed a statistically significant change between control and APC-KO mice. Our robust normalization procedures combined with statistical approaches including peptide-level ANOVA allowed for the quantification of 1789 proteins out of which 454 were significantly changing in our comparative study. As a perspective for the future work, we identify the need for a complete and user-friendly solution, encompassing the best elements from already available successful programs that would allow for a transparent, start-to-finish label-free analyses in accordance with accepted quality standards. Such a tool, combined

Chafey et al. had proposed that this knockout system should highlight the “Warburg effect”, which leads to reduced oxidative phosphorylation and increased anaerobic energy metabolism. We confirmed both that lactate dehydrogenase (LDH) was upregulated, which is consistent with earlier histochemical data,64 and that Fbp1 was down-regulated. However, we also observed the down-regulation of a few enzymes directly involved in glycolysis, such as glucokinase (Gck), glucose-6-phosphate isomerase (Gpi1), fructose-bisphosphate aldolase B (AldoB), triosephosphate isomerase (Tpi), phosphoglycerate kinase 1 (Pgk1), phosphoglycerate mutase 1 (Pgam1), and pyruvate kinase isozymes R-L isoform 2 (Pklr). This would lead to the conclusion that the APC knockout also down-regulates glycolysis. Finally, we note that the epidermal growth factor receptor (EGFR) protein was down-regulated 9.2-fold (p = 0.0001) in KO mice. This finding is consistent with the predominant expression of EGFR in the periportal zone of rat liver65 and shows again that APC-KO leads to reprogramming of the hepatocytes to a pericentral expression pattern.

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CONCLUSIONS AND PERSPECTIVES We have provided a blueprint of the pipeline for efficient labelfree quantitative proteomic analysis of biological tissues. H



Article

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with experimental design guidelines, would greatly facilitate the routine study of biological tissues.

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ASSOCIATED CONTENT

S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Author Contributions §

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Authors thank Dr. Owen Sansom (Beatson Institute for Cancer Research, Glasgow) for kindly providing APCflox/fox mice. Work in the A.S. laboratory was supported by Virtual Liver consortium grant (project code/0315757) funded by Bundesministerium f. Bildung und Forschung (BMBF)

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