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Tools for Label-free Peptide Quantification*

Open AccessPublished:December 17, 2012DOI:https://doi.org/10.1074/mcp.R112.025163
      The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.
      Over recent decades, mass spectrometry has become the analytical method of choice in most proteomics studies (e.g. Refs.
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      ). Comprehensive overviews of different quantification strategies can be found in Refs.
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      . Because of the shortcomings of labeling strategies, label-free methods are increasingly gaining the interest of proteomics researchers (
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      The proteomic profile of circulating pentraxin 3 (PTX3) complex in sepsis demonstrates the interaction with azurocidin 1 and other components of neutrophil extracellular traps.
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      • Novotny M.V.
      Glycomic and proteomic profiling of pancreatic cyst fluids identifies hyperfucosylated lactosamines on the N-linked glycans of overexpressed glycoproteins.
      ). In label-free quantification, no label is introduced to either of the samples. All samples are analyzed in separate LC/MS experiments, and the individual peptide properties of the individual measurements are then compared. Regardless of the quantification strategy, computational approaches for data analyses have become the critical final step of the proteomics workflow. Overviews of existing computational approaches in proteomics are provided in Refs.
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      . The computational label-free quantification workflow in visualized in Fig. 1. Comparing peptide quantities using mass spectrometry remains a difficult task, because mass spectrometers have different response values for different chemical entities, and thus a direct comparison of different peptides is not possible. The computational analysis of a label-free quantitative data set consists of several steps that are mainly split in raw data signal processing and quantification. Signal processing steps comprise data reduction procedures such as baseline removal, denoising, and centroiding.
      Figure thumbnail gr1
      Fig. 1The sample cohort that can be analyzed via label-free proteomics is not limited in size. Each sample is processed separately through the sample preparation and data acquisition pipeline. For data analysis, the data from the different LC/MS runs are combined.
      These steps can be accomplished in modular building blocks, or the entire analysis can be performed using monolithic analysis software. Recently, it has been shown that it is beneficial to combine modular blocks from different software tools to a consensus pipeline (
      • Hoekman B.
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      • Suits F.
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      • Horvatovich P.
      msCompare: a framework for quantitative analysis of label-free LCMS data for comparative biomarker studies.
      ). The same study also illustrates the diversity of methods that are modularized by different software tools. In another recent publication, monolithic software packages are compared (
      • Zhang R.
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      Evaluation of computational platforms for LS-MS based label-free quantitative proteomics: a global view.
      ). In that study, the authors identify a set of seven metrics: detection sensitivity, detection consistency, intensity consistency, intensity accuracy, detection accuracy, statistical capability, and quantification accuracy. Despite the missing independence of these metrics and the loose reporting of software parameter settings, such comparative studies are of great interest to the field of quantitative proteomics. A general conclusion from these studies is that the choice of software might, to a certain degree, affect the final results of the study.
      Absolute quantification of peptides and proteins using intensity-based label-free methods is possible and can be done with excellent accuracy, if standard addition is used. With the help of known concentrations, calibration lines can be drawn, and absolute protein quantities can be directly inferred from these calibration measurements (
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      ). Furthermore, it has been suggested that peptide peak intensities can be predicted and absolute quantities can be derived from these predictions (
      • Timm W.
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      • Bocker S.
      • Kohlbacher O.
      • Nattkemper T.W.
      Peak intensity prediction in MALDI-TOF mass spectrometry: a machine learning study to support quantitative proteomics.
      ). However, the limited accuracy of predictions or the need for peptides of known concentrations limits these approaches to selected proteins/peptides only and prevents their use on a proteome-wide scale.
      Spectral counting methods have also been used for the estimation of absolute concentrations on a global scale (
      • Schwanhäusser B.r.
      • Busse D.
      • Li N.
      • Dittmar G.
      • Schuchhardt J.
      • Wolf J.
      • Chen W.
      • Selbach M.
      Global quantification of mammalian gene expression control.
      ), albeit at drastically reduced accuracy relative to intensity-based methods. In one study, the authors used a mixture of 48 proteins with known concentrations and predicted the absolute copy number amounts of thousands of proteins based on that mixture. Despite the fact that large, proteome-wide data sets will dilute the effects of different peptide detectabilities on the individual protein level, such methods will always be limited in their accuracy of quantification.
      The generic nature of label-free quantification is not restricted to any model system and can also be employed with tissue or body fluids (
      • Krishnamurthy D.
      • Levin Y.
      • Harris L.W.
      • Umrania Y.
      • Bahn S.
      • Guest P.C.
      Analysis of the human pituitary proteome by data independent label-free liquid chromatography tandem mass spectrometry.
      ,
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      • Lee M.Y.
      • Yu J.-H.
      • Shin B.
      • Jung H.-J.
      • Park J.-M.
      • Han W.
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      • Lee S.-W.
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      A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers.
      ). However, the label-free approach is more sensitive to technical deviations between LC/MS runs as information is compared between different measurements. Therefore, the reproducibility of the analytical platform is crucial for successful label-free quantification. The recent success of label-free quantification could only be accomplished through significant improvements of algorithms (
      • Cox J.u.
      • Mann M.
      MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
      ,
      • Sturm M.
      • Bertsch A.
      • Gröpl C.
      • Hildebrandt A.
      • Hussong R.
      • Lange E.
      • Pfeifer N.
      • Schulz-Trieglaff O.
      • Zerck A.
      • Reinert K.
      • Kohlbacher O.
      OpenMS—an open-source software framework for mass spectrometry.
      ,
      • Pluskal T.a.
      • Castillo S.
      • Villar-Briones A.
      • Oresic M.
      MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data.
      ,
      • Colaert N.
      • Gevaert K.
      • Martens L.
      RIBAR and xRIBAR: methods for reproducible relative MS/MS based label-free protein quantification.
      ). An increasingly large collection of software tools for label-free proteomics have been published as open source applications or have entered the market as commercially available packages. This review aims at outlining the computational methods that are generally implemented by these software tools. Furthermore, we illustrate strengths and weaknesses of different tools. The review provides an information resource for the broad proteomics audience and does not illustrate all algorithmic details of the individual tools.

      SOFTWARE PACKAGES

      There is a growing collection of tools for label-free quantification implementing one or several of the techniques discussed in the preceding section. Out of the plethora of available software tools, we have selected several commercial and academic packages that are widely known and (to some extent) maintained. Table I gives an overview of computational tools, as well as information on their licenses, release dates, and input formats.
      Table IOverview of software packages for label-free quantification
      NamePlatform(s)
      Bold and underlined text indicates the availability of binary packages; W = Windows OS, L = Linux OS, M = Mac OS.
      Latest versionInput format(s)Graphical user interfaceCMDOpen source
      + License if applicable; AL = Apache License.
      Resolution
      Resolution: H = high, L = low (according to documentation).
      Quant.Statistical analysis
      Academic/free
      MaxQuant (
      • Cox B.
      • Kislinger T.
      • Wigle D.A.
      • Kannan A.
      • Brown K.
      • Okubo T.
      • Hogan B.
      • Jurisica I.
      • Frey B.
      • Rossant J.
      • Emili A.
      Integrated proteomic and transcriptomic profiling of mouse lung development and Nmyc target genes.
      )
      W1.2.2.5 (2011)Thermo .RAW+NoHMS1
      OpenMS/TOPP (
      • Sturm M.
      • Bertsch A.
      • Gröpl C.
      • Hildebrandt A.
      • Hussong R.
      • Lange E.
      • Pfeifer N.
      • Schulz-Trieglaff O.
      • Zerck A.
      • Reinert K.
      • Kohlbacher O.
      OpenMS—an open-source software framework for mass spectrometry.
      ,
      • Kohlbacher O.
      • Reinert K.
      • Gröpl C.
      • Lange E.
      • Pfeifer N.
      • Schulz-Trieglaff O.
      • Sturm M.
      TOPP—the OpenMS proteomics pipeline.
      )
      W, L, M1.9 (February 2012)mz (ML|XML|Data)+++ (LGPL)LHMS1
      pView 2 (
      • Khan Z.
      • Bloom J.S.
      • Garcia B.A.
      • Singh M.
      • Kruglyak L.
      Protein quantification across hundreds of experimental conditions.
      )
      W, L, M2.0 (July 2011)mzXML, pepXML++ (BSD)HMS1+
      mzMine 2 (
      • Pluskal T.a.
      • Castillo S.
      • Villar-Briones A.
      • Oresic M.
      MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data.
      )
      W, L, M2.6 (February 2012)mz (ML|XML|Data), ThermoRaw, NetCDF++ (GPL 2.0)LHMS1+
      SuperHirn (
      • Mueller L.N.
      • Rinner O.
      • Schmidt A.
      • Letarte S.
      • Bodenmiller B.
      • Brusniak M.-Y.
      • Vitek O.
      • Aebersold R.
      • Muller M.
      SuperHirn—a novel tool for high resolution LC-MS-based peptide/protein profiling.
      )
      L, M0.3 (January 2009)mzXML, pepXML++ (AL 2.0)HMS1
      msInspect (
      • Bellew M.
      • Coram M.
      • Fitzgibbon M.
      • Igra M.
      • Randolph T.
      • Wang P.
      • May D.
      • Eng J.
      • Fang R.
      • Lin C.
      • Chen J.
      • Goodlett D.
      • Whiteaker J.
      • Paulovich A.
      • McIntosh M.
      A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS.
      )
      W, L, M2.3 (January 2010)mzXML, mzML (in head)+++ (AL 2.0)LHMS1
      Viper (
      • Monroe M.E.
      • Tolic N.
      • Jaitly N.
      • Shaw J.L.
      • Adkins J.N.
      • Smith R.D.
      VIPER: an advanced software package to support high-throughput LC-MS peptide identification.
      )
      W3.48 (September 2011)PEK, CSV (Decon2LS), mz (XML|Data)++ (AL 2.0)HMS1
      RIBAR/xRIBAR (
      • Colaert N.
      • Gevaert K.
      • Martens L.
      RIBAR and xRIBAR: methods for reproducible relative MS/MS based label-free protein quantification.
      )
      W, L, M1.1 (May 2011)ms_lims, .dat (Mascot)++ (AL 2.0)SC
      Census (
      • Park S.K.
      • Venable J.D.
      • Xu T.
      • Yates J.R.
      A quantitative analysis software tool for mass spectrometry-based proteomics.
      )
      W, L, M1.72 (March 2010)mzXML, MS1, MS2, pepXML, DTASelect++NoLHSC, MS1
      Corra (
      • Brusniak M.-Y.
      • Bodenmiller B.
      • Campbell D.
      • Cooke K.
      • Eddes J.
      • Garbutt A.
      • Lau H.
      • Letarte S.
      • Mueller L.N.
      • Sharma V.
      • Vitek O.
      • Zhang N.
      • Aebersold R.
      • Watts J.D.
      Corra: computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.
      )
      L3.1 (November 2010)mzXML, pepXML+++ (AL 2.0)LHMS1+
      Commercial
      Mascot Distiller
      Matrix Science.
      W2.4.2 (October 2011)mz (ML|XML), major vendors++NoLHSC, MS1
      SIEVE
      Thermo Scientific.
      W?Thermo .RAW+NoLHMS1+
      Progenesis LC-MS
      Nonlinear Dynamics Ltd.
      W4.0 (September 2011)mz (ML|XML), major vendors+NoLHMS1?
      Scaffold
      Proteome Software, Inc.
      W, L, M3.3.3Major search engines++
      Via ScaffoldBatch.
      NoSC+
      Spectrolyzer
      MedicWave AB.
      W1.0mz (ML|XML|Data), major vendors+NoLHMS1+
      a Bold and underlined text indicates the availability of binary packages; W = Windows OS, L = Linux OS, M = Mac OS.
      b + License if applicable; AL = Apache License.
      c Resolution: H = high, L = low (according to documentation).
      d Matrix Science.
      e Thermo Scientific.
      f Nonlinear Dynamics Ltd.
      g Proteome Software, Inc.
      h Via ScaffoldBatch.
      i MedicWave AB.
      Some commercial packages such as SIEVE are restricted to the native vendor format and cannot read open community formats like mzML, mzData, or mzXML, which can be easily converted so as to work with one other (e.g. via OpenMS/TOPP (
      • Sturm M.
      • Bertsch A.
      • Gröpl C.
      • Hildebrandt A.
      • Hussong R.
      • Lange E.
      • Pfeifer N.
      • Schulz-Trieglaff O.
      • Zerck A.
      • Reinert K.
      • Kohlbacher O.
      OpenMS—an open-source software framework for mass spectrometry.
      ,
      • Kohlbacher O.
      • Reinert K.
      • Gröpl C.
      • Lange E.
      • Pfeifer N.
      • Schulz-Trieglaff O.
      • Sturm M.
      TOPP—the OpenMS proteomics pipeline.
      ) or ProteoWizard (
      • Kessner D.
      • Chambers M.
      • Burke R.
      • Agus D.
      • Mallick P.
      ProteoWizard: open source software for rapid proteomics tools development.
      )). Mascot Distiller (Matrix Science), Spectrolyzer (MedicWave AB), Progenesis (Nonlinear Dynamics Ltd.), and Scaffold (Proteome Software, Inc.) support a wide range of vendor formats in addition to open formats like mzML. Most feature-based methods work on raw data and apply internal centroiding algorithms or can use centroided data directly. One exception is SuperHirn (
      • Mueller L.N.
      • Rinner O.
      • Schmidt A.
      • Letarte S.
      • Bodenmiller B.
      • Brusniak M.-Y.
      • Vitek O.
      • Aebersold R.
      • Muller M.
      SuperHirn—a novel tool for high resolution LC-MS-based peptide/protein profiling.
      ), which requires raw LC/MS data. All packages can deal with high-resolution data, but only some can work with low-resolution data. MaxQuant (
      • Cox B.
      • Kislinger T.
      • Wigle D.A.
      • Kannan A.
      • Brown K.
      • Okubo T.
      • Hogan B.
      • Jurisica I.
      • Frey B.
      • Rossant J.
      • Emili A.
      Integrated proteomic and transcriptomic profiling of mouse lung development and Nmyc target genes.
      ) and SuperHirn, for example, are specialized for high resolution, whereas OpenMS/TOPP and Census (
      • Park S.K.
      • Venable J.D.
      • Xu T.
      • Yates J.R.
      A quantitative analysis software tool for mass spectrometry-based proteomics.
      ) can deal with both. Most tools support either SC or feature-based quantification, with Census and Mascot Distiller being the only exceptions in our lineup supporting both.
      SC is supported by RIBAR/xRIBAR (
      • Colaert N.
      • Gevaert K.
      • Martens L.
      RIBAR and xRIBAR: methods for reproducible relative MS/MS based label-free protein quantification.
      ) and Census, both of which are freely available. The intrinsic details of Census are unknown, but they involve normalization for protein length and variability. Mascot Distiller and Scaffold are commercial alternatives, with the latter additionally supporting Gene Ontology term annotation. Mascot Distiller supports exponentially modified protein abundance index values, and Scaffold normalizes counts by the total count within the sample, gives access to relative and absolute counts, and allows for filtering rules.
      Feature-based methods usually follow similar steps from raw data to protein expression tables (centroiding, feature finding, map alignment, and normalization, as well as protein inference) but differ in the implementation details, which are not always published, even for non-commercial tools. Progenesis and OpenMS/TOPP offer wavelet-based peak picking, suitable for low-resolution data, whereas MaxQuant fits a Gaussian curve and SuperHirn uses a simple local-maxima heuristic. Feature finding in MaxQuant is done using a graph-based approach iteratively using the best sub-graphs as predicted by an averagine model. OpenMS/TOPP uses either a wavelet approach based on an averagine model or a model-based approach on centroided data incorporating an RT shape fit and averagine models in the m/z dimension. For map alignment, SuperHirn uses a LOWESS fit, and OpenMS/TOPP uses a linear (affine) model or b-spline driven by either pose clustering or MS2 identification landmarks with respect to a reference map. Similarly, MsInspect (
      • Bellew M.
      • Coram M.
      • Fitzgibbon M.
      • Igra M.
      • Randolph T.
      • Wang P.
      • May D.
      • Eng J.
      • Fang R.
      • Lin C.
      • Chen J.
      • Goodlett D.
      • Whiteaker J.
      • Paulovich A.
      • McIntosh M.
      A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS.
      ) employs smoothing-spline regression. Progenesis uses a different approach of first using map alignment based on centroided data, guided by (user-defined) landmarks. Once a master map of all peak information from all maps is created, features are identified using an isotope-fitting procedure. Statistical post-processing or visualization at the protein level (where inference methods differ widely) is not supported by all tools and in this case must be diverted to dedicated statistical tools such as R. pView (
      • Khan Z.
      • Bloom J.S.
      • Garcia B.A.
      • Singh M.
      • Kruglyak L.
      Protein quantification across hundreds of experimental conditions.
      ) has a tight R integration, Corra (
      • Brusniak M.-Y.
      • Bodenmiller B.
      • Campbell D.
      • Cooke K.
      • Eddes J.
      • Garbutt A.
      • Lau H.
      • Letarte S.
      • Mueller L.N.
      • Sharma V.
      • Vitek O.
      • Zhang N.
      • Aebersold R.
      • Watts J.D.
      Corra: computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.
      ) features plots, and mzMine2 (
      • Pluskal T.a.
      • Castillo S.
      • Villar-Briones A.
      • Oresic M.
      MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data.
      ) allows for basic analysis procedures (e.g. PCA). Spectrolyzer has potent visualization capabilities and built-in classification and regression functionality.
      Almost all packages run on Windows, with the exception of Corra and SuperHirn. Not every package provides a binary installer, so manual compilation might be required. Commercial packages tend to be Windows only; all non-commercial packages support Linux (with the exception of VIPER (
      • Monroe M.E.
      • Tolic N.
      • Jaitly N.
      • Shaw J.L.
      • Adkins J.N.
      • Smith R.D.
      VIPER: an advanced software package to support high-throughput LC-MS peptide identification.
      )) (see Table I for details).

      CONCLUSION

      Quantitative proteomics is highly relevant for systems biology, biomarker discovery, and many other biomedical applications. Among all the methods for differential peptide quantification, label-free approaches provide the highest flexibility, and as a result of recent progress in software and hardware, their dynamic range and accuracy are continuously improving. Both SC and intensity-based measures have been shown to provide good quantification results. The intensity-based measures avoid stochastic effects in ion sampling and are therefore slightly more accurate, and they potentially provide higher reproducibility. SC is easy to implement and fast.
      There is a large collection of software solutions that are currently used for label-free peptide quantification, and each comes with different strengths and weaknesses. For users who intend to use standard workflows and do not need to develop algorithms and pipelines themselves, monolithic solutions such as Progenesis or MaxQuant are very suitable tools for fast data analysis. If more flexibility is needed or if an understanding of the underlying algorithms is required, open-source packages have their advantages. Large proteomics labs and core facilities will most likely appreciate the modularity and automation provided by pipeline tools.
      A current challenge arises from the increasing amount of samples in more and more complex proteomics studies, in particular in clinical proteomics. Although label-free techniques scale well in general, many software tools have issues with these large-scale studies. The mere amount of data involved (hundreds of LC/MS runs resulting in hundreds of gigabytes of data) certainly causes problems, but also algorithmically there are scalability issues when these maps need to be aligned and linked. Whereas small analyses can be run on laptop computers, studies requiring more than a dozen maps usually require more powerful hardware. Multi-core central processing units with a large amount of random access memory (64+ GB) and a generous amount of hard disk space are recommended for these larger studies.
      Although there is still room for improvement, software tools for label-free quantification have reached a level of sophistication that makes their use convenient and reliable for most purposes. In many cases, label-free quantification is thus a good alternative to labeling techniques in quantitative proteomics.

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