Comparison of Different Sample Preparation Protocols Reveals Lysis

Article pubs.acs.org/jpr

Comparison of Different Sample Preparation Protocols Reveals Lysis Buffer-Specific Extraction Biases in Gram-Negative Bacteria and Human Cells Timo Glatter,*,† Erik Ahrné, and Alexander Schmidt* Proteomics Core Facility, Biozentrum, University of Basel, 4056 Basel, Switzerland S Supporting Information *

ABSTRACT: We evaluated different in-solution and FASPbased sample preparation strategies for absolute protein quantification. Label-free quantification (LFQ) was employed to compare different sample preparation strategies in the bacterium Pseudomonas aeruginosa and human embryonic kidney cells (HEK), and organismal-specific differences in general performance and enrichment of specific protein classes were noted. The original FASP protocol globally enriched for most proteins in the bacterial sample, whereas the sodium deoxycholate in-solution strategy was more efficient with HEK cells. Although detergents were found to be highly suited for global proteome analysis, higher intensities were obtained for high-abundant nucleic acid-associated protein complexes, like the ribosome and histone proteins, using guanidine hydrochloride. Importantly, we show for the first time that the observable total proteome mass of a sample strongly depends on the sample preparation protocol, with some protocols resulting in a significant underestimation of protein mass due to incomplete protein extraction of biased protein groups. Furthermore, we demonstrate that some of the observed abundance biases can be overcome by incorporating a nuclease treatment step or, alternatively, a correction factor for complementary sample preparation approaches. KEYWORDS: Protein extraction, protein digestion, absolute protein quantification, sodium deoxycholate, urea, guanidine hydrochloride, ribosome, histone, iBAQ, FASP

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quantitative data, like TopN,20−22 iBAQ,23 and emPAI,24 were developed to estimate protein quantities on a proteome-wide scale without the need for expensive spiked-in heavy standards. These label-free workflows have gained considerable momentum recently, and lots of progress has been made regarding them, mostly on improving LC−MS and computational analyses.25 However, one important parameter for any method that aims at absolute protein quantification from complex samples that has received little attention so far is complete protein extraction from cellular material. In general, absolute protein quantification strategies strongly rely on the efficiency of the utilized workflow because precise protein quantification requires complete protein extraction from cells, the absence of which would inevitably result in erroneous protein quantification. A widely used sample preparation protocol in absolute proteome quantification studies incorporates the chaotropic denaturant urea, which disrupts the integrity of the cell, facilitates protein solubilization, and can be easily removed from the sample before LC−

INTRODUCTION Over the years, a number of approaches have been developed that allow proteins to be reliably quantified using shotgun mass spectrometry (MS). Popular relative quantification approaches span different metabolic, chemical labeling, and label-free quantification (LFQ) strategies. Although the value of relative quantification has been shown in numerous studies (reviewed in refs 1−5), specific aspects of life science-related research require information on absolute protein quantities, such as modeling biological processes,6−9 determining protein complex stoichiometries,10,11 protein synthesis rates,12 and others. Therefore, several absolute quantification strategies have been recently established that allow either precise quantification of selected proteins using stable-isotope dilution (SID) or total protein quantity estimations on a system wide level to be made.12−16 For SID-based quantification, heavy-labeled proteins17,18 or peptides19 of known amounts are spiked in the samples as a reference to quantify their endogenous counterpart. Unfortunately, the considerable costs associated with the synthesis of these SID references limit its application and make this technique unsuitable for system-wide studies. Alternatively, protein abundance estimation approaches based on label-free © XXXX American Chemical Society

Received: December 19, 2014

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DOI: 10.1021/acs.jproteome.5b00654 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research MS analysis.14,26 Another set of studies builds on the filteraided sample preparation (FASP)13,27,28 workflow that itself builds on the spin-filter strategy pioneered by Manza et al.29 In the FASP strategy, cells are lysed in sodium dodecylsulfate (SDS), and the MS-incompatible SDS is then removed by buffer exchange to urea. The presence of high molarities of urea disrupts the SDS micelles and allows separation of SDS monomers from the protein samples by spin filters.29,30 Alternatively, acid bile detergents, such as RapiGest31 (RG) and sodium deoxycholate32−35 (SDC), have been receiving considerable attention in the field of shotgun-MS because they allow for efficient protein solubilization/digestion and simultaneously are easy to remove from a peptide samples prior to MS analysis. Although different workflows have been presented and applied for absolute protein quantification, less attention has been given to how these multistep workflows may introduce buffer-specific quantitative biases. The majority of the comparative studies that were carried out in the past focused on optimizing the digestion step, mostly representing identification-based parameters, and did not match the multistep setup of recent state-of-the-art sample preparation workflows.36−41 Recently, it has been shown that sample preparation is the largest contributor to data variation, underlining the importance of robust workflows.42 It has to be mentioned that, in general, it is difficult to make sample preparation comparisons across studies because factors such as individual handling, a limited range of experimental conditions, and differences in MS instrumentation and data analysis strategies may affect the outcome, which requires detailed investigation to determine if the parameters used between studies are in a comparable range. Only recently did Leon et al. perform a LFQ-based comparison of a number of in-solution digestion (ISD) and FASP-based sample preparation strategies on mitochondrial isolates.34 This study, indeed, covered a comprehensive set of experimental conditions; however, its conclusions may be rather organelle-specific, and the limited number of around 330 quantified proteins hardly allows inferences to be made for the majority of organismal proteomes. In this study, we set out to systemically evaluate solubilization and digestion properties of different buffer systems employed within ISD- and FASP-LFQ workflows on human embryonic kidney 293 cells (HEK) and a Gramnegative bacterium, the human pathogen Pseudomonas aeruginosa. We have chosen two cell types with markedly different cellular organization and architecture that will allow more general conclusions to be made regarding the applicability of the sample preparation procedures. We obtained significant differences in the solubilization power and digestion supportive effect of the different in-solution strategies. Ultimately, we detected buffer-specific enrichment of individual protein classes and complexes in both species that point toward likely candidates for wrong absolute protein numbers in proteomics studies, which has not been demonstrated so far. From our study, we conclude that cell type- and project-specific buffer systems are essential to avoid inefficient proteolysis or protein solubilization. In more detail, our data show that the accuracy of popular protein abundance estimation strategies like iBAQ is more dependent on proper protein solubilization than on digestion completeness. We, furthermore, demonstrate that most of the observed solubilization biases can be circumvented using protein lysate incubation with nucleases or by the

implementation of correction factors for complementary sample preparation approaches.

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MATERIALS AND METHODS

Cell Culture

HEK cells were cultured in DMEM (4.5 g/L glucose, 10% FCS, 2 mM L -glutamin, 50 mg/mL penicillin, 50 mg/mL streptomycin) to around 90% confluence, washed with cold PBS, and pelleted. P. aeruginosa (strain PA01) was cultivated overnight in Luria−Bertani (LB) medium, washed with cold PBS, and pelleted. All cell pellets were stored at −80 °C until they were further processed. Sample Preparation and Protein Digestion

For protein digestion, frozen cell pellets were resuspended in 8 M urea, 6 M Gua, 6 M urea/2 M Gua, 1% RG, 2% SDC, or 0.5% RG/0.5% SDC (all in 100 mM NH4HCO3) in the presence of 5 mM TCEP in triplicate experiments. Following sonication, all urea-containing samples were incubated for 1 h at 37 °C, whereas all other samples were incubated at 60 °C for 30 min. After the extraction and reducing steps, all samples were incubated with 10 mM iodoacetamide at 25 °C for 30 min. Following BCA measurement, the protein samples were quenched using 20 mM N-acetylcysteine, and 50 μg of total protein was used for protein digestion. 0.5 μg LysC was added to the protein samples after diluting the chaotropic salt concentration to 6 M and diluting SDC to 1% using 100 mM NH4HCO3. LysC was allowed to cleave for 4 h at 37 °C, followed by overnight digestion at 37 °C using 1 μg of trypsin. For the second digestion step, the LysC-digested samples were further diluted to a chaotropic salt concentration of 1.6 M. Before LC−MS analysis, SDC and RG were precipitated using 1% TFA, and all samples were desalted using C18 microspin columns (Harvard Apparatus) according to the manufacturer’s instructions. For digestion, see Glatter et al.22 For the FASP experiments, cells were reconstituted in 5% SDC and 4% SDS, respectively, sonicated, and incubated for 15 min at 95 °C in the presence of 5 mM TCEP. Following an additional sonication step, the samples were allowed to cool and further incubated with 10 mM iodoacetamide at 25 °C for 30 min. 50 μg of cleared protein isolate was then transferred into spin filters (Microcon YM-30, Millipore). SDS-containing samples were mixed with 8 M urea and centrifuged for 15 min at 14 000g. Two additional urea wash/centrifugation cycles were performed. Then, tandem digest was performed as described earlier. Upon protein digestion, the filter was rinsed twice with 0.1% TFA, and flowthroughs were collected in the same tube. For SDC-FASP, the protein isolate was washed once with 1% SDC prior to tandem digest. Postdigest SDC was precipitated using 1% TFA. For the nuclease experiment, frozen cell pellets were resuspended in 1% RG and SDC and sonicated, a nuclease mix consisting of micrococcal (S7 Nuclease) and Serratia marcescens nucleases (Pierce/Thermo) was added (1:100), and the samples were incubated for 30 min at 37 °C. Then, reduction and alkylation were performed as described. LC−MS Analysis and Label-Free Quantification

LC−MS/MS analysis of digested lysates was performed on a dual-pressure LTQ-Orbitrap mass spectrometer (Thermo Electron), which was connected to an electrospray ion soure (Proxeon Biosystems). Peptide separation was carried out using an easy nano-LC 1000 system (Proxeon Biosystems/Thermo B


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Journal of Proteome Research

Figure 1. Quantitative comparison of solubilization and digestion efficiencies using different sample preparation strategies. Pseudomonas aeruginosa and human embryonic kidney 293 (HEK) cells were lysed using the indicated buffer conditions. After measuring the extracted total protein amount by BCA, 50 μg of protein was used for protein digestion. Upon tandem LysC/trypsin digestion LC−MS analysis, label-free quantification and statistical evaluation of quantification results by SafeQuant were performed. All significantly regulated proteins were then subjected to analysis by the functional annotation tool DAVID. ISD, in-solution digestion.

Scientific) equipped with a RP-HPLC column (75 μm × 15 cm) packed in-house with C18 resin (Magic C18 AQ 1.9 μm; Dr. Maisch) using a linear gradient from 96% solvent A (0.15% formic acid, 2% acetonitrile) and 4% solvent B (98% acetonitrile, 0.15% formic acid) to 35% solvent B over 180 min at a flow rate of 200 nL/min. The data acquisition mode for the initial LFQ study was set to obtain one high-resolution MS scan in the FT part of the mass spectrometer at a resolution of 60 000 full width at half-maximum (at m/z 400) followed by MS/MS scans in the linear ion trap of the 20 most intense ions. To increase the efficiency of MS/MS attempts, the charged state screening modus was enabled to exclude unassigned and singly charged ions. Collision induced dissociation (CID) was triggered when the precursor exceeded 100 ion counts. The dynamic exclusion duration was set to 60 s. The ion accumulation time was set to 300 ms (MS) and 50 ms (MS/ MS). The automatic gain control (AGC) was set to 1 × 106 for MS survey scans and 1 × 105 for MS/MS scans. Following an instrumentation upgrade, the FASP-ISD comparisons and GuaLFQ experiments were carried at a precursor resolution of 240 000. MS/MS spectra were acquired using rapid scans for the acquisition of MS/MS spectra. ID-based Gua experiments of Pseudomonas samples were analyzed using similar settings except that a 120 min linear gradient was used for peptide separation. For follow-up experiments using nuclease treatment and Gua confirmation experiments on HEK cells, the MS data was acquired using a linear gradient of 120 min. MS/MS of the 10 most intense precursor ions was triggered using HCD on 1 × 105 ions and a collision energy of 35. The FT part was set to acquire MS/MS scans at a resolution of 15 000. For label-free quantification, MS raw files were imported into Progenesis software (Nonlinear Dynamics, version 4.0). Data in mgf format were exported directly from Progenesis, and MS/ MS spectra were searched using MASCOT against a decoy database of the predicted proteome from Homo sapiens and P. aeruginosa PA01 downloaded from the EBI homepage (www. ebi.ac.uk). The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues); two missed cleavages were allowed; carbamidomethylation (C) was set as a fixed modification; and oxidation (M) was set as a variable modification. The mass tolerance was

set to 10 ppm for precursor ions and 0.5 and 0.01 Da for fragment ions for CID and HCD, respectively. The peptide false discovery rate (FDR) was set to 1% on the peptide level and validated using the number of reverse protein sequence hits in the data sets. Results from the database search were imported into Progenesis, mapping peptide identifications to MS1 features. The peak heights of all MS1 features annotated with the same peptide sequence were summed, per LC−MS run, and protein abundance was calculated in Progenesis. Next, we evaluated the data obtained by Progenesis and calculated qvalues by our in-house developed SafeQuant R-script. To process the outputs of the new Progenesis software, version 2.0, an updated SafeQuant package was uploaded to github (http:// eahrne.github.io/SafeQuant/). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository43 with the data set identifier PXD001511. For all ID-based experiments, the MS raw data was converted into MASCOT-compatible mgf files using either MM conversion (MassMatrix) or MS convert.44 ID-based data was evaluated with regard to false discovery rate adjustment using Scaffold 4 (Proteome Software).

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RESULTS AND DISCUSSION In general, the performance of any shotgun-MS proteomics experiment critically depends on an effective and robust sample preparation procedure. This study aims to systematically analyze and compare ISD- and FASP-based sample preparation routines by LFQ, which has not yet been addressed on a global level. In addition, we specifically investigate, for the first time, the impact on relative and absolute quantification as well as the extent of proteome mass biases introduced by different workflows for HEK and P. aeruginosa cells. Different Sample Preparation Strategies Enriched Specific Protein Classes

In order to study the efficiency of different sample preparation procedures, we used the experimental strategy outlined in Figure 1. To start, we focused on a comparison of popular buffer systems and buffer combinations compatible with ISD solubilization and digestion: (i) 8 M urea, (ii) 6 M guanidine hydrochloride (Gua), (iii) 6 M urea/2 M Gua, (iv) 1% RG, (v) 2% SDC, and (vi) 0.5% RG/0.5% SDC. The Gua buffers were C


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Figure 2. Assessment of protein extraction and digestion efficiencies of the different buffer systems and buffer-specific protein enrichment biases. Upon lysis, BCA was performed to measure the total protein concentration obtained with the different buffers for Pseudomonas (A) and HEK cell lysates (B). (C, D) Volcano plot-based representation of label-free quantification data. Following LC−MS analysis and label-free quantification (LFQ), log2-intensity ratios of all detected proteins between RapiGest (RG) versus urea (Pseudomonas, in (C)) and sodium deoxycholate (SDC) versus urea (HEK cells, (D)) were plotted against q-values, indicating significance. The area comprising all proteins with q-values < 0.01 is colorcoded, and the number of significantly enriched proteins is shown in the colored area. The HEK-Gua sample was excluded from label-free D


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Journal of Proteome Research Figure 2. continued

quantification. (E, F) Significantly enriched proteins based on in-depth quantitative profiling. Following iterative SafeQuant analysis to obtain quantitative comparison of all possible pairs of conditions, the number of significantly enriched proteins (q-value < 0.01) was represented using bar charts. Blue charts refer to the number significantly enriched proteins under the conditions listed on the upper X-axis. Green charts depict the number of enriched proteins under control conditions. (G, H) Significantly enriched proteins were subjected to the functional annotation tool DAVID to screen for significantly enriched Swiss-Prot keyword terms using all quantified proteins as a background. Terms with p-values corrected for multiple testing (according to the Benjamini−Hochberg method) to 10 significantly enriched proteins were required. For individual enriched protein complexes, all proteins exceeding the significance threshold of 0.01 were considered for further analysis. Highly overlapping terms are only partially shown. The full list can be assessed in Table S3, Supporting Information.

from LFQ, but we included the urea/Gua condition to capture relevant Gua-mediated effects (see later in the text). In total, 2882 Pseudomonas and 3443 human proteins were quantified across all experimental conditions. On the basis of the 5688 predicted genes for P. aeruginosa (www.pseudomonas.com), we covered 51% of its potentially observable proteome by LFQ. Wilhelm et al. recently published the most exhaustive draft of the human proteome50 encompassing a significant number of human cell lines and stated that a core proteome of around 10 000 to 12 000 can be obtained across different cell lines, whereas the remnant of human proteins is rather cell linespecific. Taking these numbers into account, we obtained a 29− 35% core proteome coverage in HEK cells by LFQ. Both the Pseudomonas and HEK data sets, therefore, represent one of the most comprehensive MS1-LFQ-based solubilization/digestion comparisons to date from bacterial and mammalian cells. We observed significant protein changes with urea for all conditions (Figure S3 and Table S3, Supporting Information). For instance, in Pseudomonas, 139 proteins (or 4.7% of all quantified proteins; Figure 2C) were significantly enriched under the urea conditions, and 416 with RapiGest (14.4%), corresponding to a RG superiority of around 10% of the total quantified bacterial proteins. In HEK cells, SDC (n = 414, 12%) was around 6% more efficient on a global level than urea (n = 209, 6%, Figure 2D). On the basis of the LFQ, we noted that the detergents consistently enriched for more proteins than urea from whole-cell extracts of both Pseudomonas and HEK cells (Figure 2E,F). Overall, RG for Pseudomonas and the detergent mix RG/SDC for human cells showed the highest number of significantly enriched proteins compared to that with urea. To allow a side-by-side comparison to be made of all sample preparation procedures, we iteratively re-ran SafeQuant to calculate protein ratios and q-values against the whole data set (Figure 2E,F). This comparison scheme confirmed the superior enrichment efficiency of the detergents, but no significant differences between the different detergent conditions were found. We noted a slight improvement with SDC compared to RG in both species, and we confirmed the results obtained in other recent ID-based studies on subproteomes (e.g., membrane fractions) that demonstrated that SDC is a well-suited sample preparation agent for MS-based proteomics.34,51 In addition, we found that all ISD conditions generated reproducible data sets, where SDC performed most consistently across the two species (Figure S3, Supporting Information). In line with the results above, all Gua-containing buffers showed the lowest protein enrichment (Figure 2E,F). Interestingly, the comparison of Gua versus urea/Gua shows almost no significant difference for the Pseudomonas sample. Conversely, a considerable difference was obtained when comparing Gua buffers to all detergent buffers in both species, where Gua was between 2.5- and 3-fold less efficient at

included in this study because Gua is an efficient denaturant, although it is used only rarely in proteomics experiments because it interferes with trypsin proteolysis even at low concentrations.37,45,46 By using an urea/Gua mix, we aimed to test the complementarity of the chaotropic salts and reduced the amount of Gua present during proteolysis. In order to compare the efficiency of the different buffer systems using an ISD format, frozen pellets of HEK and P. aeruginosa cells were resuspended in the individual buffers, and all experiments were performed in independently processed triplicates (for more details, see Materials and Methods). We observed that, irrespective of the cell type, the MScompatible detergents extracted cellular proteins most efficiently (1.8- and 2.2-fold compared to urea) for both organisms (Figure 2A,B). The Gua and urea/Gua lysates also showed elevated protein levels compared to those with urea. Although we did not find a similar experimental set in the literature for comparison, our results are in agreement with the different solubilizing effects of Gua compared to that of urea and show additive effects for protein solubilization and possibly complementary effects of the two chaotropic salts when mixed.47 The observed protein extraction biases between the single-buffer systems employed in this study clearly indicate a significant impact of the sample preparation protocols on the absolute cellular protein pool and lead to the anticipation that they will have a strong influence on protein quantification. Upon protein digestion and LC−MS analysis (details in Materials and Methods), we consistently observed, for both cell types, the most comprehensive proteolytic cleavage in the detergent samples, which showed the most fully cleaved peptides and the least miscleaved peptides. In contrast, urea showed a slightly lesser degree of protein cleavage (Figure S1 and Table S1, Supporting Information). This is in agreement with identification-based or targeted quantitative studies that investigated buffer-mediated proteolytic effects.32,34,36,37,46 Using the Gua protocol, the fewest number of peptides was identified, and the highest degree of miscleavage was found, suggesting that protease activity was inhibited to some extent even with diluted Gua concentrations (1.5 M for LysC and 0.5 M for trypsin digestion in the urea/Gua mix) (Figures S1 and S2 and Tables S1 and S2, Supporting Information). In order to further elucidate the protein extraction qualities of the different experimental strategies and to detect protein candidates that may lead to quantification biases, we performed in-depth quantitative protein profiling of the different buffer conditions by MS1-LFQ. Since urea is frequently used in absolute quantitative studies,14,16,48,49 we used urea as the initial/entry control condition. Due to the significantly lower number of identified proteins compared to the other conditions (Figure S1 and Table S1, Supporting Information), we excluded the HEK-Gua samples (6 M LysC/1.6 M trypsin) E


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Figure 3. Quantitative biases introduced by the different buffer system. (A, B) iBAQ values for affected protein groups were calculated and are represented as bar charts. (C, D) Total proteome fraction was calculated by multiplying iBAQ values with the fraction of each proteins molecular weight.

against samples with chaotropic salt buffers (Figure 2G), indicating distinct localization-specific preferences of bacterial proteins for individual buffer systems. In HEK cells, we found similar results on membrane proteins, where SDC displayed the most proteins for different and overlapping membrane terms (Membrane, Transmembrane, etc.) compared to that with chaotropic salts. We found no differences among the detergent buffers in both species, except for the comparison of RG and RG/SDC, where DAVID reported a positive effect for RG for a number of terms in bacterial cells (Figure 2G). Although we did not detect similar differences in the human system, Leon et al. observed that the combination of detergents for FASP had a severely negative impact on peptide recovery,34 which is contradictorily discussed in the literature for FASP.55,56 Although the performance of all detergents was superior compared to that with chaotropic salts, in particular, on membrane proteins, we found that urea- and Gua-containing buffers showed a significant enrichment of a number of different proteins. Most apparent was the enrichment of ribosomal proteins in bacteria and human cells (Figure 2G,H and Table S3, Supporting Information) when sample preparation was carried out in the presence of Gua. Gua still has beneficial effects for enriching ribosomal proteins even when the urea/Gua mix was used, suggesting that Gua is not required at high molarity to disrupt the integrity of the ribosomal complex. Furthermore, we found that 6 M/1.6 M urea/Gua was efficient at capturing all Gua-mediated benefits in MS1-LFQ experimental formats and that further dilution of Gua for digestion did not increase the presence of any of the effected functional groups (Table S4, Supporting Information). On the basis of the results on Gua buffers in Pseudomonas and Gua dilution LFQ experiments in HEK cells, we conclude that, although the Gua sample was omitted from HEK cell LFQ analysis, urea/Gua still captures relevant Gua-specific hits.

enriching proteins. Next, because detergents have different solubilization and denaturing properties than chaotropic salts, we aimed to identify possible protein class-specific enrichment biases for the individual ISD sample preparation strategies. Such biases have rarely been considered in studies comparing different digestion strategies. Therefore, we subjected all enriched proteins to the functional annotation tool DAVID52 to screen for enriched Swiss-Prot keyword terms (Figure 2G,H and Table S3, Supporting Information). The most significant hits from the DAVID analysis revealed a consistent detergentspecific enrichment for a number of different membrane protein terms and protein groups that cover membrane proteins compared to the chaotropic salts in both cell types. This observation is in line with recent studies that showed a detergent-specific increase in the recovery of peptides mapping to membrane proteins derived from membrane fractions.32,53 We were particularly interested in finding terms related to different membrane substructures in bacteria to infer whether there are consistent or different effects between the buffer systems between the species. Compared to human cells, which have a single phospholipid bilayer for compartmentation, the bacterial cell envelope contains an outer membrane that is separated from the inner membrane by the peptidoglycan cell wall that spans through the periplasmic space. With this multilayer cell envelope architecture, the bacteria gain stability and, importantly, higher resistance against attacking chemicals like antibiotics.54 For Pseudomonas cells, “Membrane” and “Transmembrane” were the most significantly enriched terms for samples lysed with RG-containing buffer (Figure 2G), suggesting that RG is highly suited for the analysis of bacterial membrane proteins, in particular, inner membrane proteins, whereas SDC showed no advantage over urea. However, outer membrane proteins (OMPs) are detected in SDC only when comparison is made F


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Figure 4. Impact of the buffer systems on protein subclasses. (A, B) Different membrane protein classes are depicted as volcano plots to visualize significant enrichment and enrichment biases profiled for RG versus urea for Pseudomonas and SDC versus urea in HEK cells. (C, D) Bar chart representation of regulated membrane proteins. The number regulated proteins (blue and red bars) and summed log2 ratios of regulated proteins (black bars) are shown for defined membrane protein subclasses. O = outer membrane; i = inner membrane. (E, F) Fraction of membrane proteins of the total proteome mass. (G, H) Monitoring of the observed signal variations of the ribosome complex obtained under the different buffer conditions. Average/subunit iBAQ values for the Pseudomonas (G) and human (H) ribosome complexes are shown to report quantification accuracy on complex stoichiometries.

Next to ribosomal proteins, urea/Gua showed enrichment of nucleosome core proteins in HEK cells, which consists of the histone protein complex.57 The nucleosome core protein enrichment was specific for urea/Gua over SDC and urea but not RG. The specific enrichment of ribosomes and histones shows that the urea/Gua mix, unlike the detergent mix, exhibits complementary effects over the individual chaotropic buffers. This is further illustrated by the proteasomal proteins, which are enriched in urea compared to all detergents but not in urea/ Gua. To sum, the enrichment analysis revealed that the

Remarkably, despite their inhibitory effect on digestion, Gua buffers still enrich for ribosomal proteins in the HEK cell lysates (Figure 2H). Hervey and co-workers made a similar observation and showed that Gua-containing buffers captured more peptide IDs from a ribosomal fraction but performed less efficiently on MAP (microtubule-associated proteins) isolates.37 Therefore, our results and those from the Hervey et al. point toward individual protein classes requiring dedicated and specialized solubilization/digestion strategies for efficient sample preparation. G


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Pseudomonas. In particular, the bacterial cell outer membrane proteins (OMP) represent proteins with high importance for host invasion and development of resistance mechanisms, and they have been covered by only a few proteomics studies with diverse experimental set ups.59,60 Figure 4A,B shows volcano plots to visualize the membrane subproteome, quantifying detergents versus urea by LFQ. The characteristic asymmetry of the plots clearly marks the differential protein enrichment of the detergents over urea. Importantly, many membrane proteins are linked to fundamental biological processes and are often involved in disease development.61,62 Therefore, accurate absolute quantification of disease-related membrane-associated proteins relies on well-established buffer conditions and are likely incorrect on a global basis when lysis is carried out in urea. This is exemplified by the 17 Ras-related GTPases that were detected within the class of lipid-anchor proteins (Figure 4B). Rasrelated proteins are involved in vesicular trafficking that can contribute to disease phenotypes.63,64 Since RG for Pseudomonas cells and SDC for HEK cells were found to be very efficient for global ISD-LFQ, we used these conditions as a control condition for membrane protein comparison. We summarized the results of the LFQ analysis in Figure 4C,D by representing the number and summed log2 ratios of enriched membrane proteins versus RG for the bacteria and SDC for HEK cells. In both cell types, the detergents markedly outperformed the chaotropes on all membrane protein terms. All detergents performed equally well, whereas SDC slightly exceeded the ratios and number of significantly enriched proteins compared to those with the other detergents. Interestingly, SDC was the most efficient on bacterial OMPs. On the basis of the calculated TPM, we noted that the outer membrane protein OprF was, by far, the most abundant OMP (Table S3, Supporting Information), with abundances reaching those of the most abundant nonmembrane Pseudomonas proteins like elongation factor EF-Tu and the chaperon GroEL. Because of the high efficiency with which SDC extracts OMPs, the outer membrane mass is around 8-fold higher in SDC samples compared to those with urea and even 2-fold increased compared to those with RG (Figure 4E). As a result, the proportion of detectable OprF on cellular TPM considerably increased from 0.88% (urea) to 6.9% (SDC), which, in turn, has a strong impact on the overall determined cellular mass distribution. In HEK cells, all detergents performed equally well at extracting a comparable mass for membrane proteins (Figure 4F). Of note, compared to Pseudomonas membrane proteins, is that human membrane proteins are more in the medium-abundance range and therefore the overall impact on the TPM is less pronounced than in the bacterial system (Table S3, Supporting Information). To further investigate the quantification accuracy of the buffer systems, we next focused on the ribosome as one of the biggest and most abundant cellular protein complexes that is affected by different sample preparation strategies. Figure 4G,H shows the iBAQ values for the bacterial 30s/50s and human 40s/60s ribosomal subunits. We observed that only Guacontaining buffers resulted in the expected stoichiometry ratio of 1:1 between the smaller and larger ribosomal subunit, whereas other buffer systems, in particular, SDC, resulted in higher variation and lower intensities of the subunits.

individual sample preparation strategies, indeed, have quantitative biases for a diverse set of membrane proteins and highabundant and heavily studied protein complexes, like ribosomes, the proteasome, and histones of the nucleosome core. Given the fact that absolute quantification studies are carried out by building on different buffer systems, our study shows, for the first time, how multistep cellular proteomics workflows can lead to buffer-specific protein quantification biases, which have to be carefully considered. Impact of Buffer System Biases on the Quantitative Proteome and Subproteome

To complement our relative quantitative comparisons above, we next evaluated the extent of biases mediated by the different buffer systems on the absolute cellular proteome mass determined for our two model organisms. This gives a more detailed picture of extraction/digestion biases, since the concentration of a protein is considered for the analysis. Therefore, we calculated iBAQ values12,58 for all quantified proteins, grouped proteins accordingly, and found the largest differences between the sample preparation conditions for membrane proteins in bacteria and histones in human cells (Figure 3A,B and Table S3, Supporting Information). In HEK cells, the histone proteins exhibited the highest iBAQ values with urea/Gua. The sum of all membrane protein iBAQ values for SDC in Pseudomonas was around 5-fold higher compared to that in urea, which was itself 2.3-fold higher than SDC. When looking at total cellular protein masses, we found that protein classes affected by the different buffer systems made up to around 16% of the total proteome mass (TPM) in Pseudomonas and up to 35% in HEK cells (Figure 3C,D and Table S3, Supporting Information). According to iBAQ values, a significant impact on the observable proteome was obtained for the histones proteins. When sample preparation was carried out using the ISD-urea protocol, the quantified histones covered only 7% of the total proteome mass, whereas urea/ Gua lead to an increase of histones to around 16% of TPM. Following the results of the DAVID analysis, we traced the impact of the buffers on bacterial and human ribosomal proteins and found that, similar to the human histone complex, Gua-containing buffers allowed for the most comprehensive detection of Pseudomonas ribosomal proteins to be obtained, with an increase of almost 1.7-fold, from 4.8 to 8%, compared to SDC, which was found to be the least efficient for the quantification of bacterial ribosomes. Figure 3A,B illustrates that membrane proteins, which represent a more diverse set of proteins, are highly influenced by the buffer system. In Pseudomonas, membrane protein mass ranged from 2.8% of the total proteome using urea to around 11.5% using SDC. Interestingly, although RG was found to be the most efficient on a global level, SDC was the most effective for membrane proteins in both organisms, with a lower extent in HEK cells. Here, membrane proteins increased only from 12 to 15% TPM. Since membrane proteins are a highly diverse protein group, we assigned different subclasses of membrane proteins to more specifically investigate the observed buffer-specific influences. As the DAVID analysis reported positive effects of detergents on Pseudomonas and HEK cell membrane substructures, we profiled the relative quantities and TPM of the membrane subproteome for both cell types. We subdivided the membrane subproteome into transmembrane proteins, lipid anchor proteins, and peripheral membrane proteins for the human cell line and inner and outer membrane proteins for H


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Figure 5. Comparison of in-solution digestion strategies to FASP. (A, B) Total proteome comparison of different in-solution extraction/digestion (ISD) strategies to FASP workflows by LFQ. (C, D) Significantly enriched proteins were subjected to the functional annotation tool DAVID to screen for significantly enriched Swiss-Prot keyword terms using all quantified proteins as background. (E) Detailed analysis of affected protein groups in Pseudomonas by LFQ. (F) TPM of affected protein groups in Pseudomonas cells. (G) LFQ comparison of SDS/urea-FASP and urea/Gua. The iBAQ values for the urea/Gua biased histone and ribosome proteins are shown. Error bars in the figure refer to the standard deviation. **, P < 0.05.

tions.28,65−67 However, so far, a detailed comparison of it to other sample preparation strategies using LFQ approaches is little known in the literature. For instance, Leon et al.34 recently showed that FASP-SDC performed more efficiently than other ISD approaches on mitochondrial isolates; however, this study did not include a comparison with the original FASP protocol by LFQ. In addition, despite the fact that truly comprehensive experimental testing was performed, only a limited number of proteins were quantified (around 330), which hardly allows the findings to be generalized to large-scale proteome analyses. Therefore, our study comprises the first quantitative wholeproteome comparison of the original FASP protocol to SDC.

Taken together, the results demonstrate that the cellular mass of certain proteins and protein groups determined by LFQ LC−MS workflows was considerably affected by the different buffer systems and that consequently, lysis buffers have to be carefully selected and optimized to achieve accurate protein concentrations analyses. Comparison to FASP

In recent years, the FASP strategy has gained popularity across proteomics laboratories and has been applied to a variety of studies aiming to establish complementary digestion approaches and quantitative analysis of proteomes and subproteomes, including absolute protein quantification estimaI


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Figure 6. Correcting major quantification biases by nuclease treatment and correction factors. (A) The impact of nuclease treatment on SDC lysates versus urea/Gua was assessed by the protein intensity sum of all ribosomal proteins. (B) Same as in (A) but for histone proteins. (C) 40s/60s ribosomal subunit iBAQ values were calculated to report on quantification accuracy. (D) DAVID analysis of cell lysates with and without nuclease incubation. (E) Correction factors for histone core complex components. An independent triplicate LFQ experiment was performed between HEK cell lysates prepared with SDC-ISD and urea/Gua-ISD. Correction factors are calculated based on iBAQ values. Correction factors for the most affected protein groups can be used to correct incorrect protein abundances derived from SDC-LFQ experiments. (F) Histone iBAQ values were calculated to show the discrepancy in quantification accuracy of SDC in comparison to urea/Gua. For the calculation of the iBAQ values, feature intensities were used. Error bars in the figure refer to the standard deviation. **, P < 0.05; ***, P < 0.01.

In order to compare our ISD strategy to FASP in more detail, we performed independent triplicate experiments using the ISD-SDC protocol, FASP-SDC, and FASP-SDS/urea for bacterial and human cells as well as ISD-RG for Pseudomonas only. By using a higher temperature compared to that of our entry study (95 °C instead of 60 °C) for solubilization and reduction for all conditions, we aimed to match the experimental FASP settings. Following LFQ, we observed that all ISD conditions showed slightly less variation in the data

In bacterial cells, a recent study was performed by Tanca et al., who presented a comparison of SDS/urea-FASP and ISDRG on Escherichia coli lysates.68 They found a slight preference of FASP over RG when assessing identification/spectrum-based parameters among around 1000 bacterial proteins. The comparison of FASP to SDC on bacterial proteomes, however, is controversially discussed in the literature,69,70 which is likely due to individual and laboratory-specific differences in handling, instrumentation, and data analysis strategies. J


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suited for the extraction and solubilization of these proteins. Even though Gua showed the best extraction capabilities for ribosomes, it is not suited for global proteome analysis (Figure 3) in comparison to detergents, which provide a much more unbiased representation of the proteome. Therefore, we aimed to overcome the limitation linked to the lower abundances determined for ribosomes and histones using detergent-based buffers, in particular, the SDC protocol. Because all ribosome and histone complexes that were found to be depleted in SDC buffer (Figures 2 and 3) are nucleic acid-associated, we reasoned that treating protein samples with nucleases prior to digestion might further destabilize the complex and improve its solubilization. In order to test this, we performed follow-up experiments and incubated SDC-HEK cell protein isolates with a nuclease cocktail (see Materials and Methods) to digest all DNA/RNA structures before protein digestion to support the disruption of nucleic acid-bound protein complexes. When profiling the intensity sum of all detected ribosomal and histone proteins, we indeed found a significant increase in the histones and ribosomal proteins identified in SDC lysates after nuclease treatment (Figure 6A,B and Table S6, Supporting Information). By incorporating the nuclease treatment into our SDC sample preparation workflow, we were able to increase the intensities for histones and ribosomes by around 13−20% and obtained a final difference between the urea/Gua the SDC-nuclease protocol of only 1.3-fold (instead of 1.7-fold) for histone proteins; the difference for the ribosome was almost completely eliminated. In addition, we observed that the 40s/60s ribosomal subunit stoichiometry was improved using the SDC-nuclease protocol compared to that using SDC only (Figure 6C). The nuclease treatment, therefore, increased the ribosome and histone signal intensities relative to urea/Gua to the level that we obtained with SDS/urea-FASP (Figure 5G). Next, we submitted proteins significantly enriched versus urea/Gua to DAVID. We found that the major biases were suppressed by the nuclease incubation, as terms associated with the ribosome were either strongly decreased or below the significance threshold after nuclease treatment (Figure 6D). However, the nuclease treatment did not completely eliminate the SDC-introduced biases on ribosomes and histones, as urea/Gua still leads to higher intensities for those protein complexes. Since we found the measured SDC-induced biases to be highly consistent, another way to overcome this underrepresentation would be the incorporation of correction factors in quantitative MS studies. This can be achieved simply by performing a urea/Gua-to-SDC correction LFQ experiment to establish laboratory-specific correction factors, which then can be employed for the whole shotgun-MS samples to be compared (Figure 6E). On the basis of our initial and follow-up analysis in HEK cells, we conclude that urea/Gua has good complementary properties to those of SDC, as it is most efficient at extracting ribosomal proteins and histones, whereas ISD-SDC was most efficient on a global scale and particularly suited for the analysis of membrane proteins. In another triplicate experiment, we incorporated higher temperatures (95 °C instead of 60 °C) and more sonication steps (see Materials and Methods) to exert more solubilizing and disrupting conditions for SDC. Even though the urea/Gua protocol was executed at low temperatures in order to avoid protein modification by urea,71 SDC did not match the iBAQ intensities of the ribosomes and histones obtained by urea/ Gua, suggesting that SDC has inherent limitations that are hard to overcome by experimental conditions (Figure 6E).

set compared to that with FASP-SDS/urea (Figure S4, Supporting Information). We noted an organismal-specific difference in the performance of FASP compared to the ISD conditions. In Pseudomonas, SDS/urea-FASP showed the strongest protein enrichment on a global level, whereas in HEK cells, ISD-SDC was the most efficient, although the regulated hits were fewer in number and in the degree of regulation in HEK compared to bacteria (Figure 5A,B). Subsequent DAVID analysis of significantly enriched proteins showed that ISD-RG and SDS/urea-FASP enriched for several keywords associated with membrane proteins and nucleotidebinging proteins versus ISD-SDC in Pseudomonas, whereas in HEK cells, no highly significant hits were reported from DAVID between any of the conditions (Figure 5C,D). This observation supports the notion that results are hard to transfer from one cell type to another when cells are architecturally different. In order to further elucidate the organismal-specific difference of FASP and SDC-ISD, we plotted the number of regulated proteins along with their log-ratio sum for the affected membrane protein terms and the group of nucleotidebinding proteins, which comprise many signaling molecules (kinases and RNA/DNA-binding proteins, e.g., transcriptional regulators). FASP and RG showed a remarkable superiority for all affected protein groups, in particular, “Transmembrane proteins” (Figure 5E; see also Table S5, Supporting Information). Even for outer membrane proteins, slightly more significantly enriched proteins were detected for FASP compared to that with ISD-SDC, which we found to be the most efficient in our first data set. In addition, SDS/urea-FASP even slightly outperformed RG on inner and outer membrane proteins, which is consistent with observations by Tanca and co-workers on E. coli samples.68 Furthermore, most transmembrane protein mass was detected with SDS/urea-FASP, whereas the nucleotidebinding protein mass was equally distributed between the conditions, as significantly affected proteins are in the midabundance/mass range (Figure 5F). In HEK cells, in contrast, we obtained only marginal differences, as suggested by DAVID analysis. ISD-SDC showed a slight advantage compared to SDC-FASP and SDS/ureaFASP on the pool of detected membrane proteins (Figure S5, Supporting Information). No obvious difference was obtained between ISD-SDC and FASP-SDC, and this was consistent in both species, which is in contrast to the observations made by Leon et al.34 Except for a small advantage with histone proteins, we did not find any effect on any level that would promote SDS/urea-FASP over SDC in HEK cells (Figure S5, Supporting Information). Furthermore, FASP did not match the intensities that we obtained on histones and ribosomes in urea/Gua-LFQ experiments (Figure 5G). In summary, we found that SDS-FASP and ISD-RG are highly suited for the Gram-negative bacteria P. aeruginosa, where the fewest biases were observed. SDC-ISD and SDS/ urea-FASP, on the contrary, were found to perform quite equally on HEK cells, with SDC having a slight advantage on global protein quantification. Correction of Quantification Biases

Interestingly, RNA/DNA−protein complexes, like the ribosome and histone, appeared, despite a reduction in protease activity, with higher abundances using the ISD-Gua and urea/ Gua protocols. This finding indicates that Gua is particularly K


Article

Journal of Proteome Research Therefore, independent LFQ experiments can be performed to establish correction factors for the most affected protein groups. As it is frequently used as a basis for LFQ absolute quantification, correction may be performed on iBAQ values (Figure 6E). We generally calculated protein group-specific urea/Gua-to-SDC correction factors of 1.3-fold for ribosomes and 1.5-fold for histones, which were consistent across different experiments. Despite the moderate 1.5-fold difference within the histone complex, we obtained a remarkable difference in the relative iBAQ abundance between the histone core components. More specifically, the LFQ experiments showed that only core histone proteins H3 and H4 are difficult to analyze by SDC and are coherently under-represented (Figure 6F). The intensities of the other histone core components, H2A and H2B, are captured quite accurately by SDC.57,72 However, even after applying higher temperatures and more sonication steps, a minimum depletion of H3 and H4 of around 2.7-fold compared to that with urea/Gua remained in SDC. The correct quantification of all histone components is required, in particular, when histone proteins are correlated with DNA, as recently presented by the proteomic ruler approach,28 where the absolute cellular DNA content is measured and correlated with the histone proteins, which are then used as an internal protein standard for absolute protein quantification. Despite the fact that research laboratories may have individual workflow preferences, we point out that solubilization biases have to be carefully investigated and corrected, as it is likely that buffer-specific biases are hard to overcome when building on a single buffer system only and, furthermore, may be laboratory- and user-specific.

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AUTHOR INFORMATION

Corresponding Authors

*(T.G.) Tel.: +41 61 26 70 927. Fax: +41 61 26 72 009. E-mail: [email protected]. *(A.S.) Tel.: +41 61 26 72 059. E-mail: alex.schmidt@unibas. ch.

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CONCLUSIONS Taken together, we assessed for the first time the quantification biases of different ISD- and FASP-based strategies by LFQ on a global basis for two architecturally different organisms. We showed that on a global level detergent-based ISD protocols are better suited for quantitative studies than chaotropic strategies, as less quantitative biases are introduced on a proteome-wide level. Importantly, by comparing FASP to ISD strategies, we noted organismal-specific differences among the sample preparation strategies. FASP was particularly suited for the analysis of Gram-negative bacteria, whereas ISD-SDC and FASP were most efficient for HEK293 cells. In addition, GuaISD generated higher abundances for histones and ribosomes that were not matched by any of the detergent conditions. Importantly, these protein complexes are likely candidates to be under-represented in absolute quantification studies even when detergent-based strategies are used. We further demonstrated that these biases can be largely circumvented by incorporating nuclease digestion or correction factors into the workflow.

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to confirm guanidine hydrochloride observations (Figure S2); graphical SafeQuant output of label-free quantified and evaluated P. aeruginosa and H. sapiens MS data (Figure S3); graphical SafeQuant output of label-free quantified and evaluated P. aeruginosa and H. sapiens MS data derived from FASP-ISD comparison (Figure S4); bar charts representing different affected proteins groups in P. aeruginosa and HEK293 cells (Figure S5) (PDF). List of all peptide and proteins ID from P. aeruginosa and H. sapiens (HEK cells) samples (Table S1) (XLSX). Protein IDs showing confirmation of the effects of guanidinium hydrochloride (Gua) on P. aeruginosa and H. sapiens lysates (Table S2) (XLSX). SafeQuant output of label-free quantified (LFQ) and evaluated P. aeruginosa and H. sapiens MS data (Table S3) (XLSX). Monitoring possible ion suppression effects by Gua (Table S4) (XLSX). SafeQuant output of LFQ experiment comparing urea/ Gua to different Gua buffer alternatives (Table S5) (XLSX). SafeQuant output nuclease treatment experiment and LFQ experiment between SDC and urea/Gua for correction factor calculation (Table S6) (XLSX).

Present Address †

(T.G.) Max Planck Institute for terrestrial Microbiology, Karlvon-Frisch Str. 10, 35043 Marburg, Germany. Tel.: +49 6421 178 334. E-mail: [email protected]. Author Contributions

T.G. conducted all experiments and performed MS and data analysis. T.G. and A.S. designed the study and wrote the manuscript. E.A. contributed to data analysis. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We would like to thank Benoit Joseph Laventie for providing P. aeruginosa cell pellets and the PRIDE team for supporting data upload and availability.

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ASSOCIATED CONTENT

REFERENCES

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