Comparative LC‐MS: A landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well‐known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC‐MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC‐MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label‐free LC‐MS. In order to compare at comprehensive scale peak intensity data in multiple LC‐MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundancies provides an impressive level of detail. This approach also requires an experimental set‐up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.

[1]  Gene H. Golub,et al.  Missing value estimation for DNA microarray gene expression data: local least squares imputation , 2005, Bioinform..

[2]  N. Karp,et al.  Design and Analysis Issues in Quantitative Proteomics Studies , 2007, Proteomics.

[3]  Richard D. Smith,et al.  Quantitative proteome analysis of breast cancer cell lines using 18O‐labeling and an accurate mass and time tag strategy , 2006, Proteomics.

[4]  Kathryn S Lilley,et al.  Impact of replicate types on proteomic expression analysis. , 2005, Journal of proteome research.

[5]  Hua Tang,et al.  Normalization Regarding Non-Random Missing Values in High-Throughput Mass Spectrometry Data , 2005, Pacific Symposium on Biocomputing.

[6]  Setsuo Hirohashi,et al.  Label-free Quantitative Proteomics Using Large Peptide Data Sets Generated by Nanoflow Liquid Chromatography and Mass Spectrometry* , 2006, Molecular & Cellular Proteomics.

[7]  Simon C. F. Sheng,et al.  Multidimensional Liquid Chromatography Separation of Intact Proteins by Chromatographic Focusing and Reversed Phase of the Human Serum Proteome , 2006, Molecular & Cellular Proteomics.

[8]  Wei-Hao Wang,et al.  Studies , 1926 .

[9]  Nikola Tolić,et al.  Targeted comparative proteomics by liquid chromatography-tandem Fourier ion cyclotron resonance mass spectrometry. , 2005, Analytical chemistry.

[10]  M. Orešič,et al.  Data processing for mass spectrometry-based metabolomics. , 2007, Journal of chromatography. A.

[11]  K. Markides,et al.  Chromatographic alignment by warping and dynamic programming as a pre-processing tool for PARAFAC modelling of liquid chromatography-mass spectrometry data. , 2002, Journal of chromatography. A.

[12]  Pei Wang,et al.  Bioinformatics Original Paper a Suite of Algorithms for the Comprehensive Analysis of Complex Protein Mixtures Using High-resolution Lc-ms , 2022 .

[13]  Ljiljana Paša-Tolić,et al.  An accurate mass tag strategy for quantitative and high‐throughput proteome measurements , 2002, Proteomics.

[14]  David M. Rocke,et al.  Variance-stabilizing transformations for two-color microarrays , 2004, Bioinform..

[15]  L. Huber,et al.  Zooming in: Fractionation strategies in proteomics , 2004, Proteomics.

[16]  Peicheng Du,et al.  Data reduction of isotope-resolved LC-MS spectra , 2007, Bioinform..

[17]  Jacob D. Jaffe,et al.  PEPPeR, a Platform for Experimental Proteomic Pattern Recognition*S , 2006, Molecular & Cellular Proteomics.

[18]  M. Gorenstein,et al.  Simultaneous Qualitative and Quantitative Analysis of theEscherichia coli Proteome , 2006, Molecular & Cellular Proteomics.

[19]  Li Hsu,et al.  Partially Supervised Learning Using an EM‐Boosting Algorithm , 2004, Biometrics.

[20]  David B. Searls,et al.  Data integration: challenges for drug discovery , 2005, Nature Reviews Drug Discovery.

[21]  J. Yates,et al.  Identification of proteins in complexes by solid-phase microextraction/multistep elution/capillary electrophoresis/tandem mass spectrometry. , 1999, Analytical chemistry.

[22]  Roeland C H J van Ham,et al.  Post alignment clustering procedure for comparative quantitative proteomics LC‐MS Data , 2008, Proteomics.

[23]  J. Listgarten,et al.  Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry , 2005, Molecular & Cellular Proteomics.

[24]  Ilya Levner,et al.  Feature selection and nearest centroid classification for protein mass spectrometry , 2005, BMC Bioinformatics.

[25]  Knut Reinert,et al.  High-Accuracy Peak Picking of Proteomics Data Using Wavelet Techniques , 2005, Pacific Symposium on Biocomputing.

[26]  F. Regnier,et al.  Recent advancements in differential proteomics based on stable isotope coding. , 2005, Briefings in functional genomics & proteomics.

[27]  H. Meyer,et al.  Approaches for the quantification of protein concentration ratios , 2003, Proteomics.

[28]  Richard D. Smith,et al.  High-efficiency nanoscale liquid chromatography coupled on-line with mass spectrometry using nanoelectrospray ionization for proteomics. , 2002, Analytical chemistry.

[29]  A. America,et al.  Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC‐MS , 2006, Proteomics.

[30]  Joachim Selbig,et al.  Metabolite fingerprinting: detecting biological features by independent component analysis , 2004, Bioinform..

[31]  Andrew J Link,et al.  Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry , 2006, Expert review of proteomics.

[32]  Jacob D. Jaffe,et al.  MapQuant: Open‐source software for large‐scale protein quantification , 2006, Proteomics.

[33]  W. Liang,et al.  TM4 microarray software suite. , 2006, Methods in enzymology.

[34]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[35]  Stephen J. Callister,et al.  Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. , 2006, Journal of proteome research.

[36]  Richard D. Smith,et al.  Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. , 2006, Analytical chemistry.

[37]  F. Regnier,et al.  An automated method for the analysis of stable isotope labeling data in proteomics , 2005, Journal of the American Society for Mass Spectrometry.

[38]  Catherine Fenselau,et al.  Evaluation of metabolic labeling for comparative proteomics in breast cancer cells. , 2004, Journal of proteome research.

[39]  Jeroen Krijgsveld,et al.  Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics , 2003, Nature Biotechnology.

[40]  Robert Tibshirani,et al.  Statistical methods for identifying differentially expressed genes in DNA microarrays. , 2003, Methods in molecular biology.

[41]  R. Bischoff,et al.  Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics: method development, evaluation, and application to proteinuria. , 2007, Journal of proteome research.

[42]  Brian L Hood,et al.  Biomarkers: Mining the Biofluid Proteome* , 2005, Molecular & Cellular Proteomics.

[43]  M. Gorenstein,et al.  Absolute Quantification of Proteins by LCMSE , 2006, Molecular & Cellular Proteomics.

[44]  Jean-Charles Sanchez,et al.  MSight: An image analysis software for liquid chromatography‐mass spectrometry , 2005, Proteomics.

[45]  T. Shaler,et al.  Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. , 2003, Analytical chemistry.

[46]  Jimmy Eng,et al.  A platform for accurate mass and time analyses of mass spectrometry data. , 2007, Journal of proteome research.

[47]  Peter J. Park,et al.  A multivariate approach for integrating genome-wide expression data and biological knowledge , 2006, Bioinform..

[48]  Jeffrey R. Whiteaker,et al.  Head-to-head comparison of serum fractionation techniques. , 2007, Journal of proteome research.

[49]  Ronald J Moore,et al.  Ultra-high-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. , 2004, Analytical chemistry.

[50]  M. Karas,et al.  Effect of different solution flow rates on analyte ion signals in nano-ESI MS, or: when does ESI turn into nano-ESI? , 2003, Journal of the American Society for Mass Spectrometry.

[51]  Martin Kussmann,et al.  OMICS-driven biomarker discovery in nutrition and health. , 2006, Journal of biotechnology.

[52]  Ronald J. Moore,et al.  Differential Label-free Quantitative Proteomic Analysis of Shewanella oneidensis Cultured under Aerobic and Suboxic Conditions by Accurate Mass and Time Tag Approach*S , 2006, Molecular & Cellular Proteomics.

[53]  Zhongqi Zhang,et al.  A universal algorithm for fast and automated charge state deconvolution of electrospray mass-to-charge ratio spectra , 1998, Journal of the American Society for Mass Spectrometry.

[54]  Richard E Higgs,et al.  Comprehensive label-free method for the relative quantification of proteins from biological samples. , 2005, Journal of proteome research.

[55]  C. Shriver,et al.  Label-free Semiquantitative Peptide Feature Profiling of Human Breast Cancer and Breast Disease Sera via Two-dimensional Liquid Chromatography-Mass Spectrometry*S , 2006, Molecular & Cellular Proteomics.

[56]  Lukas N. Mueller,et al.  SuperHirn – a novel tool for high resolution LC‐MS‐based peptide/protein profiling , 2007, Proteomics.

[57]  Emanuel F. Petricoin,et al.  Serum Proteomic Analysis Identifies a Highly Sensitive and Specific Discriminatory Pattern in Stage 1 Breast Cancer , 2007, Annals of Surgical Oncology.

[58]  Joachim M. Buhmann,et al.  Semi-supervised LC/MS alignment for differential proteomics , 2006, ISMB.

[59]  E. Marcotte,et al.  Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. , 2006, Analytical chemistry.

[60]  A. Pothen,et al.  Protocols for disease classification from mass spectrometry data , 2003, Proteomics.

[61]  Robert J Beynon,et al.  Metabolic Labeling of Proteins for Proteomics* , 2005, Molecular & Cellular Proteomics.

[62]  David M. Rocke,et al.  Design and analysis of experiments with high throughput biological assay data. , 2004, Seminars in cell & developmental biology.

[63]  Nikola Tolić,et al.  Ultrasensitive proteomics using high-efficiency on-line micro-SPE-nanoLC-nanoESI MS and MS/MS. , 2004, Analytical chemistry.

[64]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian Cancer , 2002 .

[65]  Ronald J Moore,et al.  Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[66]  Christopher H. Becker,et al.  Differential expression profiling of serum proteins and metabolites for biomarker discovery , 2004 .

[67]  Richard D. Smith,et al.  Proteomic analyses using an accurate mass and time tag strategy. , 2004, BioTechniques.

[68]  Xiang Zhang,et al.  Data pre-processing in liquid chromatography-mass spectrometry-based proteomics , 2005, Bioinform..

[69]  Michal Linial,et al.  Novel Unsupervised Feature Filtering of Biological Data , 2006, ISMB.

[70]  E. Petricoin,et al.  Serum proteomic patterns for detection of prostate cancer. , 2002, Journal of the National Cancer Institute.

[71]  R. Aebersold,et al.  Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. , 2003, Analytical chemistry.

[72]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[73]  John E Hale,et al.  The role of mass spectrometry in biomarker discovery and measurement. , 2006, Current drug metabolism.

[74]  Phillip C Wright,et al.  Novel approach for peptide quantitation and sequencing based on 15N and 13C metabolic labeling. , 2005, Journal of proteome research.

[75]  Xuegong Zhang,et al.  Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data , 2006, BMC Bioinformatics.

[76]  Radford M. Neal,et al.  Difference detection in LC-MS data for protein biomarker discovery , 2007, Bioinform..

[77]  Richard D. Smith,et al.  Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry. , 2004, Journal of proteome research.

[78]  Joachim Selbig,et al.  A gentle guide to the analysis of metabolomic data. , 2007, Methods in molecular biology.

[79]  M. Mann,et al.  Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* , 2002, Molecular & Cellular Proteomics.

[80]  Peicheng Du,et al.  Automatic deconvolution of isotope-resolved mass spectra using variable selection and quantized peptide mass distribution. , 2006, Analytical chemistry.

[81]  N. Karp,et al.  Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis*S , 2007, Molecular & Cellular Proteomics.

[82]  Chris F. Taylor,et al.  A common open representation of mass spectrometry data and its application to proteomics research , 2004, Nature Biotechnology.

[83]  Christophe Junot,et al.  Liquid chromatography-mass spectrometry and 15N metabolic labeling for quantitative metabolic profiling. , 2005, Analytical chemistry.

[84]  M. Wiener,et al.  Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures. , 2004, Analytical chemistry.

[85]  W. Liang,et al.  9) TM4 Microarray Software Suite , 2006 .

[86]  O. Fiehn,et al.  Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co‐regulation in biochemical networks , 2004, Proteomics.

[87]  Johannes P. C. Vissers,et al.  Analysis and Quantification of Diagnostic Serum Markers and Protein Signatures for Gaucher Disease*S , 2007, Molecular & Cellular Proteomics.

[88]  Benno Schwikowski,et al.  Assessing Bias in Experiment Design for Large Scale Mass Spectrometry-based Quantitative Proteomics*S , 2007, Molecular & Cellular Proteomics.

[89]  Richard D. Smith,et al.  Advances in proteomics data analysis and display using an accurate mass and time tag approach. , 2006, Mass spectrometry reviews.

[90]  Guanghui Wang,et al.  Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes. , 2006, Journal of proteome research.

[91]  R. Beavis,et al.  An Improved Model for Prediction of Retention Times of Tryptic Peptides in Ion Pair Reversed-phase HPLC , 2004, Molecular & Cellular Proteomics.

[92]  J. Yates,et al.  An automated multidimensional protein identification technology for shotgun proteomics. , 2001, Analytical chemistry.

[93]  R. Lahesmaa,et al.  A comparative evaluation of software for the analysis of liquid chromatography‐tandem mass spectrometry data from isotope coded affinity tag experiments , 2005, Proteomics.

[94]  Matej Oresic,et al.  Processing methods for differential analysis of LC/MS profile data , 2005, BMC Bioinformatics.

[95]  John Quackenbush Microarray data normalization and transformation , 2002, Nature Genetics.

[96]  John R Yates,et al.  Large Scale Protein Profiling by Combination of Protein Fractionation and Multidimensional Protein Identification Technology (MudPIT)* , 2006, Molecular & Cellular Proteomics.

[97]  John Quackenbush,et al.  Genesis: cluster analysis of microarray data , 2002, Bioinform..

[98]  Michael J MacCoss,et al.  Improving tandem mass spectrum identification using peptide retention time prediction across diverse chromatography conditions. , 2007, Analytical chemistry.

[99]  K. Verhoeckx,et al.  Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples. , 2006, Analytical chemistry.

[100]  W. Weckwerth,et al.  Metabolomics: from pattern recognition to biological interpretation. , 2005, Drug discovery today.

[101]  C. A. Hastings,et al.  New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data. , 2002, Rapid communications in mass spectrometry : RCM.

[102]  Matej Oresic,et al.  MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data , 2006, Bioinform..

[103]  Pierre Geurts,et al.  Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..

[104]  Joachim Selbig,et al.  Visualization and analysis of molecular data. , 2007, Methods in molecular biology.

[105]  Marco Grzegorczyk,et al.  Statistics for Proteomics: A Review of Tools for Analyzing Experimental Data , 2006, Proteomics.

[106]  Hua Tang,et al.  A statistical method for chromatographic alignment of LC-MS data. , 2007, Biostatistics.

[107]  A. Smilde,et al.  How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics , 2007, Proteomics.

[108]  Jae K Lee,et al.  Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry , 2007, Proteomics.

[109]  Iqbal Gondal,et al.  Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data , 2005, Bioinform..

[110]  Jeffrey Whiteaker,et al.  Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles. , 2006, Journal of proteome research.

[111]  M. Mann,et al.  Exponentially Modified Protein Abundance Index (emPAI) for Estimation of Absolute Protein Amount in Proteomics by the Number of Sequenced Peptides per Protein*S , 2005, Molecular & Cellular Proteomics.

[112]  Jeffrey S. Morris,et al.  The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. , 2005, Briefings in functional genomics & proteomics.

[113]  Yongyi Mao,et al.  Informatics Platform for Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass Spectrometry*S , 2004, Molecular & Cellular Proteomics.

[114]  Ruedi Aebersold,et al.  A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid Chromatography-Mass Spectrometry*S , 2005, Molecular & Cellular Proteomics.

[115]  Ying Xu,et al.  Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. , 2006, Analytical chemistry.

[116]  Daniel B. Martin,et al.  Advances in quantitative proteomics using stable isotope tags. , 2002, Trends in biotechnology.

[117]  Knut Reinert,et al.  TOPP - the OpenMS proteomics pipeline , 2007, Bioinform..

[118]  Robert Tibshirani,et al.  Sample classification from protein mass spectrometry, by 'peak probability contrasts' , 2004, Bioinform..

[119]  Michael K. Coleman,et al.  Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. , 2006, Journal of proteome research.

[120]  S. Dudoit,et al.  Multiple Hypothesis Testing in Microarray Experiments , 2003 .

[121]  Jeffrey S. Morris,et al.  Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet‐Based Functional Mixed Models , 2008, Biometrics.

[122]  J. Yates,et al.  Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. , 2004, Analytical chemistry.

[123]  M. Washburn,et al.  Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors , 2006, Proceedings of the National Academy of Sciences.

[124]  Hua Lin,et al.  Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum , 2004, Bioinform..

[125]  Lennart Björkesten,et al.  Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC‐MS/MS data , 2006, Proteomics.

[126]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[127]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[128]  M. Gorenstein,et al.  Quantitative proteomic analysis by accurate mass retention time pairs. , 2005, Analytical chemistry.

[129]  Pan Du,et al.  Bioinformatics Original Paper Improved Peak Detection in Mass Spectrum by Incorporating Continuous Wavelet Transform-based Pattern Matching , 2022 .

[130]  J. Yates,et al.  Large-scale analysis of the yeast proteome by multidimensional protein identification technology , 2001, Nature Biotechnology.

[131]  K. Resing,et al.  Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S , 2005, Molecular & Cellular Proteomics.

[132]  Knut Reinert,et al.  A geometric approach for the alignment of liquid chromatography - mass spectrometry data , 2007, ISMB/ECCB.

[133]  W Windig,et al.  Chemometric analysis of complex hyphenated data. Improvements of the component detection algorithm. , 2007, Journal of chromatography. A.

[134]  D. C. Simpson,et al.  Proteomic profiling of intact proteins using WAX-RPLC 2-D separations and FTICR mass spectrometry. , 2007, Journal of proteome research.

[135]  Benno Schwikowski,et al.  Signal Maps for Mass Spectrometry-based Comparative Proteomics* , 2006, Molecular & Cellular Proteomics.