Comprehensive yeast proteome analysis using a capillary isoelectric focusing-based multidimensional separation platform coupled with ESI-MS/MS

As demonstrated in this study, a CIEF-based multidimensional separation platform not only is compatible with the detergent-based membrane protein preparation protocol, but also achieves both the largest yeast membrane proteome coverage and the most comprehensive analysis of the yeast proteome to date. By using a 1% false discovery rate for total peptide identifications, a total of 2513 distinct yeast proteins are identified from the SDS-solubilized fraction with an average of 5.4 peptides leading to each protein identification. Among proteins identified from the SDS-solubilized fraction, 407 proteins are predicted to contain at least two or more transmembrane domains using TMHMM (www.cbs.dtu.dk/services/TMHMM-2.0/), corresponding to 46% yeast membrane proteome coverage. Only four additional membrane proteins are identified in the soluble and urea-solubilized fractions, affirming the utility of SDS extraction for enriching the membrane proteome. By combining proteome results obtained from the soluble, urea-solubilized, and SDS-solubilized fractions, a single yeast proteome analysis yields the identification of 3632 distinct yeast proteins, corresponding to 55% theoretical yeast proteome coverage or 70% of proteins predicted to be expressed during log-phase growth in rich media.     

Comparison of 2-D LC and 3-D LC with post- and pre-tryptic-digestion SEC fractionation for proteome analysis of normal human liver tissue

The current "shotgun" proteomic analysis, strong cation exchange-RPLC-MS/MS system, is a widely used method for proteome research. Currently, it is not suitable for complicated protein sample analysis, like mammal tissues or cells. To increase the protein identification confidence and number, an additional separation dimension for sample fractionation is necessary to be coupled prior to current multi-dimensional protein identification technology (MudPIT). In this work, SEC was elaborately selected and applied for sample prefractionation in consideration of its non-bias against sample and variety of choice of mobile phases. The analysis of the global lysate of normal human liver tissue sample provided by the China Human Liver Proteome Project, were performed to compare the proteome coverage, sequence coverage (peptide per protein identification) and protein identification efficiency in MudPIT, 3-D LC-MS/MS identification strategy with preproteolytic and postproteolytic fractionation. It was demonstrated that 3-D LC-MS/MS utilizing protein level fractionation was the most effective method. A MASCOT search using the MS/MS results acquired by QSTAR(XL) identified 1622 proteins from 3-D LC-MS/MS identification approaches. A primary analysis on molecular weight, pI and grand average hydrophobicity value distribution of the identified proteins in different approaches was made to further evaluate the 3-D LC-MS/MS analysis strategy.     

Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome

Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.     

Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry

There is significant interest in characterization of the human plasma proteome due to its potential for providing biomarkers applicable to clinical diagnosis and treatment and for gaining a better understanding of human diseases. We describe here a strategy for comparative proteome analyses of human plasma, which is applicable to biomarker identifications for various disease states. Multidimensional liquid chromatography-mass spectrometry (LC-MS/MS) has been applied to make comparative proteome analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Peptide peak areas and the number of peptide identifications for each protein were used to evaluate the reproducibility of LC-MS/MS and to compare relative changes in protein concentration between the samples following LPS treatment. A total of 804 distinct plasma proteins (not including immunoglobulins) were confidently identified with 32 proteins observed to be significantly increased in concentration following LPS administration, including several known inflammatory response or acute-phase mediators such as C-reactive protein, serum amyloid A and A2, LPS-binding protein, LPS-responsive and beige-like anchor protein, hepatocyte growth factor activator, and von Willebrand factor, and thus, constituting potential biomarkers for inflammatory response.     

Optimization and use of peptide mass measurement accuracy in shotgun proteomics

Mass spectrometers that provide high mass accuracy such as FT-ICR instruments are increasingly used in proteomic studies. Although the importance of accurately determined molecular masses for the identification of biomolecules is generally accepted, its role in the analysis of shotgun proteomic data has not been thoroughly studied. To gain insight into this role, we used a hybrid linear quadrupole ion trap/FT-ICR (LTQ FT) mass spectrometer for LC-MS/MS analysis of a highly complex peptide mixture derived from a fraction of the yeast proteome. We applied three data-dependent MS/MS acquisition methods. The FT-ICR part of the hybrid mass spectrometer was either not exploited, used only for survey MS scans, or also used for acquiring selected ion monitoring scans to optimize mass accuracy. MS/MS data were assigned with the SEQUEST algorithm, and peptide identifications were validated by estimating the number of incorrect assignments using the composite target/decoy database search strategy. We developed a simple mass calibration strategy exploiting polydimethylcyclosiloxane background ions as calibrant ions. This strategy allowed us to substantially improve mass accuracy without reducing the number of MS/MS spectra acquired in an LC-MS/MS run. The benefits of high mass accuracy were greatest for assigning MS/MS spectra with low signal-to-noise ratios and for assigning phosphopeptides. Confident peptide identification rates from these data sets could be doubled by the use of mass accuracy information. It was also shown that improving mass accuracy at a cost to the MS/MS acquisition rate substantially lowered the sensitivity of LC-MS/MS analyses. The use of FT-ICR selected ion monitoring scans to maximize mass accuracy reduced the number of protein identifications by 40%.     

Cross-sample validation provides enhanced proteome coverage in rat vocal fold mucosa

The vocal fold mucosa is a biomechanically unique tissue comprised of a densely cellular epithelium, superficial to an extracellular matrix (ECM)-rich lamina propria. Such ECM-rich tissues are challenging to analyze using proteomic assays, primarily due to extensive crosslinking and glycosylation of the majority of high M(r) ECM proteins. In this study, we implemented an LC-MS/MS-based strategy to characterize the rat vocal fold mucosa proteome. Our sample preparation protocol successfully solubilized both proteins and certain high M(r) glycoconjugates and resulted in the identification of hundreds of mucosal proteins. A straightforward approach to the treatment of protein identifications attributed to single peptide hits allowed the retention of potentially important low abundance identifications (validated by a cross-sample match and de novo interpretation of relevant spectra) while still eliminating potentially spurious identifications (global single peptide hits with no cross-sample match). The resulting vocal fold mucosa proteome was characterized by a wide range of cellular and extracellular proteins spanning 12 functional categories.     

A proteomics performance standard to support measurement quality in proteomics

The emergence of MS-based proteomic platforms as a prominent technology utilized in biochemical and biomedical research has increased the need for high-quality MS measurements. To address this need, National Institute of Standards and Technology (NIST) reference material (RM) 8323 yeast protein extract is introduced as a proteomics quality control material for benchmarking the preanalytical and analytical performance of proteomics-based experimental workflows. RM 8323 yeast protein extract is based upon the well-characterized eukaryote Saccharomyces cerevisiae and can be utilized in the design and optimization of proteomics-based methodologies from sample preparation to data analysis. To demonstrate its utility as a proteomics quality control material, we coupled LC-MS/MS measurements of RM 8323 with the NIST MS Quality Control (MSQC) performance metrics to quantitatively assess the LC-MS/MS instrumentation parameters that influence measurement accuracy, repeatability, and reproducibility. Due to the complexity of the yeast proteome, we also demonstrate how NIST RM 8323, along with the NIST MSQC performance metrics, can be used in the evaluation and optimization of proteomics-based sample preparation methods.     

Improved proteome coverage by using high efficiency cysteinyl peptide enrichment: the human mammary epithelial cell proteome

Automated multidimensional capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been increasingly applied in various large scale proteome profiling efforts. However, comprehensive global proteome analysis remains technically challenging due to issues associated with sample complexity and dynamic range of protein abundances, which is particularly apparent in mammalian biological systems. We report here the application of a high efficiency cysteinyl peptide enrichment (CPE) approach to the global proteome analysis of human mammary epithelial cells (HMECs) which significantly improved both sequence coverage of protein identifications and the overall proteome coverage. The cysteinyl peptides were specifically enriched by using a thiol-specific covalent resin, fractionated by strong cation exchange chromatography, and subsequently analyzed by reversed-phase capillary LC-MS/MS. An HMEC tryptic digest without CPE was also fractionated and analyzed under the same conditions for comparison. The combined analyses of HMEC tryptic digests with and without CPE resulted in a total of 14 416 confidently identified peptides covering 4294 different proteins with an estimated 10% gene coverage of the human genome. By using the high efficiency CPE, an additional 1096 relatively low abundance proteins were identified, resulting in 34.3% increase in proteome coverage; 1390 proteins were observed with increased sequence coverage. Comparative protein distribution analyses revealed that the CPE method is not biased with regard to protein M(r) , pI, cellular location, or biological functions. These results demonstrate that the use of the CPE approach provides improved efficiency in comprehensive proteome-wide analyses of highly complex mammalian biological systems.     

Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap

Yeast remains an important model for systems biology and for evaluating proteomics strategies. In-depth shotgun proteomics studies have reached nearly comprehensive coverage, and rapid, targeted approaches have been developed for this organism. Recently, we demonstrated that single LC-MS/MS analysis using long columns and gradients coupled to a linear ion trap Orbitrap instrument had an unexpectedly large dynamic range of protein identification (Thakur, S. S., Geiger, T., Chatterjee, B., Bandilla, P., Frohlich, F., Cox, J., and Mann, M. (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, 10.1074/mcp.M110.003699). Here we couple an ultra high pressure liquid chromatography system to a novel bench top Orbitrap mass spectrometer (Q Exactive) with the goal of nearly complete, rapid, and robust analysis of the yeast proteome. Single runs of filter-aided sample preparation (FASP)-prepared and LysC-digested yeast cell lysates identified an average of 3923 proteins. Combined analysis of six single runs improved these values to more than 4000 identified proteins/run, close to the total number of proteins expressed under standard conditions, with median sequence coverage of 23%. Because of the absence of fractionation steps, only minuscule amounts of sample are required. Thus the yeast model proteome can now largely be covered within a few hours of measurement time and at high sensitivity. Median coverage of proteins in Kyoto Encyclopedia of Genes and Genomes pathways with at least 10 members was 88%, and pathways not covered were not expected to be active under the conditions used. To study perturbations of the yeast proteome, we developed an external, heavy lysine-labeled SILAC yeast standard representing different proteome states. This spike-in standard was employed to measure the heat shock response of the yeast proteome. Bioinformatic analysis of the heat shock response revealed that translation-related functions were down-regulated prominently, including nucleolar processes. Conversely, stress-related pathways were up-regulated. The proteomic technology described here is straightforward, rapid, and robust, potentially enabling widespread use in the yeast and other biological research communities.     

Effect of 2MEGA labeling on membrane proteome analysis using LC-ESI QTOF MS

One of the challenges associated with large-scale proteome analysis using tandem mass spectrometry (MS/MS) and automated database searching is to reduce the number of false positive identifications without sacrificing the number of true positives found. In this work, a systematic investigation of the effect of 2MEGA labeling (N-terminal dimethylation after lysine guanidination) on the proteome analysis of a membrane fraction of an Escherichia coli cell extract by 2-dimensional liquid chromatography MS/MS is presented. By a large-scale comparison of MS/MS spectra of native peptides with those from the 2MEGA-labeled peptides, the labeled peptides were found to undergo facile fragmentation with enhanced a1 or a1-related (a(1)-17 and a(1)-45) ions derived from all N-terminal amino acids in the MS/MS spectra; these ions are usually difficult to detect in the MS/MS spectra of nonderivatized peptides. The 2MEGA labeling alleviated the biased detection of arginine-terminated peptides that is often observed in MALDI and ESI MS experiments. 2MEGA labeling was found not only to increase the number of peptides and proteins identified but also to generate enhanced a1 or a1-related ions as a constraint to reduce the number of false positive identifications. In total, 640 proteins were identified from the E. coli membrane fraction, with each protein identified based on peptide mass and sequence match of one or more peptides using MASCOT database search algorithm from the MS/MS spectra generated by a quadrupole time-of-flight mass spectrometer. Among them, the subcellular locations of 336 proteins are presently known, including 258 membrane and membrane-associated proteins (76.8%). Among the classified proteins, there was a dramatic increase in the total number of integral membrane proteins identified in the 2MEGA-labeled sample (153 proteins) versus the unlabeled sample (77 proteins).     

Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations

Researchers have several options when designing proteomics experiments. Primary among these are choices of experimental method, instrumentation and spectral interpretation software. To evaluate these choices on a proteome scale, we compared triplicate measurements of the yeast proteome by liquid chromatography tandem mass spectrometry (LC-MS/MS) using linear ion trap (LTQ) and hybrid quadrupole time-of-flight (QqTOF; QSTAR) mass spectrometers. Acquired MS/MS spectra were interpreted with Mascot and SEQUEST algorithms with and without the requirement that all returned peptides be tryptic. Using a composite target decoy database strategy, we selected scoring criteria yielding 1% estimated false positive identifications at maximum sensitivity for all data sets, allowing reasonable comparisons between them. These comparisons indicate that Mascot and SEQUEST yield similar results for LTQ-acquired spectra but less so for QSTAR spectra. Furthermore, low reproducibility between replicate data acquisitions made on one or both instrument platforms can be exploited to increase sensitivity and confidence in large-scale protein identifications.     

Toward the complete yeast mitochondrial proteome: multidimensional separation techniques for mitochondrial proteomics

Proteomic analyses of different subcellular compartments, so-called organellar proteomics, facilitate the understanding of cellular functions on a molecular level. In this work, various orthogonal multidimensional separation techniques both on the protein and on the peptide level are compared with regard to the number of identified proteins as well as the classes of proteins accessible by the respective methodology. The most complete overview was achieved by a combination of such orthogonal techniques as shown by the analysis of the yeast mitochondrial proteome. A total of 851 different proteins (PROMITO dataset) were identified by use of multidimensional LC-MS/MS, 1D-SDS-PAGE combined with nano-LC-MS/MS and 2D-PAGE with subsequent MALDI-mass fingerprinting. Our PROMITO approach identified the 749 proteins, which were found in the largest previous study on the yeast mitochondrial proteome, and additionally 102 proteins including 42 open reading frames with unknown function, providing the basis for a more detailed elucidation of mitochondrial processes. Comparison of the different approaches emphasizes a bias of 2D-PAGE against proteins with very high isoelectric points as well as large and hydrophobic proteins, which can be accessed more appropriately by the other methods. While 2D-PAGE has advantages in the possible separation of protein isoforms and quantitative differential profiling, 1D-SDS-PAGE with nano-LC-MS/MS and multidimensional LC-MS/MS are better suited for efficient protein identification as they are less biased against distinct classes of proteins. Thus, comprehensive proteome analyses can only be realized by a combination of such orthogonal approaches, leading to the largest dataset available for the mitochondrial proteome of yeast.     

Evaluation of shotgun sequencing for proteomic analysis of human plasma using HPLC coupled with either ion trap or Fourier transform mass spectrometry

This paper reports on studies directed to the characterization of the proteome of human plasma by the shotgun sequencing approach, namely the use of HPLC coupled to mass spectrometry (MS). The report will present data from two laboratories that allows the comparison of peptide and protein identifications by either accurate mass measurement on a Fourier transform mass spectrometry or MS/MS fragmentation on an ion trap mass spectrometer. Because the dynamic range of the protein components of plasma is one of the largest for a biological sample, the analysis of such a challenging sample was aided by the use of these two MS approaches. The major classes of proteins observed were transport proteins, enzymes, and enzyme inhibitors, blood-clotting factors, membrane-associated proteins including soluble forms of receptors, hormones, immunoglobulins, and other glycoproteins. The protein identifications were also highly consistent with results obtained from 2D gel studies, although a larger number of additional proteins were observed with the shotgun sequencing approach. The quantitation of low to medium level proteins was explored in the ion trap with an add-back of a known amount of human growth hormone (hGH) at a clinically relevant level (5 ug/L). The isotope coded affinity tag (ICAT) approach was used to quantitate successfully different levels of hGH in replicate analysis via the disulfide linked tryptic peptide (T6-T16). These studies suggest that the shotgun sequencing approach can be used to characterize part of the plasma proteome and serve as a starting point for the use of multidimensional analytical approaches for the analysis of complex biological samples.     

Proteomic mapping of brain plasma membrane proteins

Proteomics is potentially a powerful technology for elucidating brain function and neurodegenerative diseases. So far, the brain proteome has generally been analyzed by two-dimensional gel electrophoresis, which usually leads to the complete absence of membrane proteins. We describe a proteomic approach for profiling of plasma membrane proteins from mouse brain. The procedure consists of a novel method for extraction and fractionation of membranes, on-membrane digestion, diagonal separation of peptides, and high-sensitivity analysis by advanced MS. Breaking with the classical plasma membrane fractionation approach, membranes are isolated without cell compartment isolation, by stepwise depletion of nonmembrane molecules from entire tissue homogenate by high-salt, carbonate, and urea washes followed by treatment of the membranes with sublytic concentrations of digitonin. Plasma membrane is further enriched by of density gradient fractionation and protein digested on-membrane by endoproteinase Lys-C. Released peptides are separated, fractions digested by trypsin, and analyzed by LC-MS/MS. In single experiments, the developed technology enabled identification of 862 proteins from 150 mg of mouse brain cortex. Further development and miniaturization allowed analysis of 15 mg of hippocampus, revealing 1,685 proteins. More that 60% of the identified proteins are membrane proteins, including several classes of ion channels and neurotransmitter receptors. Our work now allows in-depth study of brain membrane proteomes, such as of mouse models of neurological disease.     

Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment

We report a global proteomic approach for analyzing brain tissue and for the first time a comprehensive characterization of the whole mouse brain proteome. Preparation of the whole brain sample incorporated a highly efficient cysteinyl-peptide enrichment (CPE) technique to complement a global enzymatic digestion method. Both the global and the cysteinyl-enriched peptide samples were analyzed by SCX fractionation coupled with reversed phase LC-MS/MS analysis. A total of 48,328 different peptides were confidently identified (>98% confidence level), covering 7792 nonredundant proteins ( approximately 34% of the predicted mouse proteome). A total of 1564 and 1859 proteins were identified exclusively from the cysteinyl-peptide and the global peptide samples, respectively, corresponding to 25% and 31% improvements in proteome coverage compared to analysis of only the global peptide or cysteinyl-peptide samples. The identified proteins provide a broad representation of the mouse proteome with little bias evident due to protein pI, molecular weight, and/or cellular localization. Approximately 26% of the identified proteins with gene ontology (GO) annotations were membrane proteins, with 1447 proteins predicted to have transmembrane domains, and many of the membrane proteins were found to be involved in transport and cell signaling. The MS/MS spectrum count information for the identified proteins was used to provide a measure of relative protein abundances. The mouse brain peptide/protein database generated from this study represents the most comprehensive proteome coverage for the mammalian brain to date, and the basis for future quantitative brain proteomic studies using mouse models. The proteomic approach presented here may have broad applications for rapid proteomic analyses of various mouse models of human brain diseases.     

Comprehensive proteomic analysis of membrane proteins in Toxoplasma gondii

Toxoplasma gondii (T. gondii) is an obligate intracellular protozoan parasite that is an important human and animal pathogen. Experimental information on T. gondii membrane proteins is limited, and the majority of gene predictions with predicted transmembrane motifs are of unknown function. A systematic analysis of the membrane proteome of T. gondii is important not only for understanding this parasite's invasion mechanism(s), but also for the discovery of potential drug targets and new preventative and therapeutic strategies. Here we report a comprehensive analysis of the membrane proteome of T. gondii, employing three proteomics strategies: one-dimensional gel liquid chromatography-tandem MS analysis (one-dimensional gel electrophoresis LC-MS/MS), biotin labeling in conjunction with one-dimensional gel LC-MS/MS analysis, and a novel strategy that combines three-layer "sandwich" gel electrophoresis with multidimensional protein identification technology. A total of 2241 T. gondii proteins with at least one predicted transmembrane segment were identified and grouped into 841 sequentially nonredundant protein clusters, which account for 21.8% of the predicted transmembrane protein clusters in the T. gondii genome. A large portion (42%) of the identified T. gondii membrane proteins are hypothetical proteins. Furthermore, many of the membrane proteins validated by mass spectrometry are unique to T. gondii or to the Apicomplexa, providing a set of gene predictions ripe for experimental investigation, and potentially suitable targets for the development of therapeutic strategies.     

Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma

The plasma proteome has a wide dynamic range of protein concentrations and is dominated by a few highly abundant proteins. Discovery of novel cancer biomarkers using proteomics is particularly challenging because specific biomarkers are expected to be low abundance proteins with normal blood concentrations of low nanograms per milliliter or less. Conventional, one- and two-dimensional proteomic methods including 2D PAGE, 2D DIGE, LC-MS/MS, and LC/LC-MS/MS do not have the capacity to consistently detect many proteins in this range. In contrast, new higher dimensional (Hi-D) separation strategies, utilizing more than two dimensions of fractionation, can profile the low abundance proteome.     

Comparative Analysis of Label-Free and 8-Plex iTRAQ Approach for Quantitative Tissue Proteomic Analysis

High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free approach (intensity-based) and 8-plex iTRAQ was conducted as applied to the analysis of tumor tissue samples from non-muscle invasive and muscle-invasive bladder cancer. For the latter, two acquisition strategies were tested including analysis of unfractionated and fractioned iTRAQ-labeled peptides. To reduce variability, aliquots of the same protein extract were used as starting material, whereas to obtain representative results per method further sample processing and MS analysis were conducted according to routinely applied protocols. Considering only multiple-peptide identifications, LC-MS/MS analysis resulted in the identification of 910, 1092 and 332 proteins by label-free, fractionated and unfractionated iTRAQ, respectively. The label-free strategy provided higher protein sequence coverage compared to both iTRAQ experiments. Even though pre-fraction of the iTRAQ labeled peptides allowed for a higher number of identifications, this was not accompanied by a respective increase in the number of differentially expressed changes detected. Validity of the proteomics output related to protein identification and differential expression was determined by comparison to existing data in the field (Protein Atlas and published data on the disease). All methods predicted changes which to a large extent agreed with published data, with label-free providing a higher number of significant changes than iTRAQ. Conclusively, both label-free and iTRAQ (when combined to peptide fractionation) provide high proteome coverage and apparently valid predictions in terms of differential expression, nevertheless label-free provides higher sequence coverage and ultimately detects a higher number of differentially expressed proteins. The risk for receiving false associations still exists, particularly when analyzing highly heterogeneous biological samples, raising the need for the analysis of higher sample numbers and/or application of adjustment for multiple testing.