Capture and analysis of quantitative proteomic data

Lau, King Wai; Jones, Andrew R.; Swainston, Neil; Siepen, Jennifer A.; Hubbard, Simon J.

doi:10.1002/pmic.200700127

Cited by 47 publications

(31 citation statements)

References 53 publications

(109 reference statements)

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“…Recently, focus has centered on the ability to assess multiple samples within a single MS experiment. Binary sample labeling techniques such as 16 O/ 18 O (1) , ICAT™ (2) , and SILAC (3) were developed to evaluate paired samples whereas iTRAQ™ (4) was developed to simultaneously analyze four, and more recently, eight samples (5) . The binary labeling techniques add complexity to the acquired spectra and to their interpretation by introducing additional peaks into the mass spectra.…”

Section: A Introductionmentioning

confidence: 99%

Statistical Analysis of Relative Labeled Mass Spectrometry Data from Complex Samples Using ANOVA

et al. 2008

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Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation and useful visualization tools are demonstrated via case study of complex biological samples assessed using the iTRAQ™ relative labeling protocol. KeywordsProteomics; ANOVA; iTRAQ™; Normalization; relative labeling protocol; Missing data; GaussSiedel; Backfitting; Fixed effects model; Mixed effects model A. INTRODUCTIONThe objective of global proteomics via mass spectrometry is to detect and quantify all proteins present in a biological sample. Proteins that exhibit an increase/decrease in abundance between two or more groups of interest, (e.g., diseased and non-diseased) are considered candidate CORRESPONDING AUTHOR FOOTNOTE Ann L. Oberg, Mayo Clinic, Cancer Center Statistics, 200 First St SW, Rochester, MN 55905. Telephone (507) 538-1556; Fax (507) biomarkers. However, experimental factors such as differences in sample collection, sample characteristics such as cellular concentration, variations in sample processing and the experimental process add variability to the observed abundances. Experimental variability hinders the comparison of effects of interest, and if not accounted for during the design and analysis stages, can lead the researcher down an erroneous path of discovery.Several mass spectrometry (MS) techniques have been developed that allow greater control over experimental factors that introduce variability and ultimately decrease the quality of the data. Recently, focus has centered on the ability to assess multiple samples within a single MS experiment. Binary sample labeling techniques such as 16 O/ 18 O (1) , ICAT™ (2) , and SILAC (3) were developed to evaluate paired samples whereas iTRAQ™ (4) was developed to simultaneously analyze four, and more recently, eight samples (5) . The binary labeling techniques add complexity to the acquired spectra and to their interpretation by introducing additional peaks into the mass spectra. Furthermore, overlapping isotopic clusters require further analytical techniques to deconvolute the resulting spectrum and the associated protein/ peptide abundances (4,6,7) . The iTRAQ™ labeling system overcomes this to some extent since the labeled species are isobaric and protein abundances are measured only in the resulting MS/ MS fragmentation spectra.Although sample labeling techniques allow greater control over experimental variability within an MS experiment, the analysis of multiple MS experiments remains difficult. Within an experiment, it is important that equal amounts of total protein are labeled under each labeling condition to ensure that the observed abundances are not influenced by total protein concentration. Once the samples are labeled and mixed together for MS analysis, labeling methods naturally control for instrument variability. The same principles apply when performing multiple experiments; with th...

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Section: A Introductionmentioning

confidence: 99%

Statistical Analysis of Relative Labeled Mass Spectrometry Data from Complex Samples Using ANOVA

et al. 2008

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“…Online coupling of a liquid chromatography (LC) system with fast MS spectra acquisition and high-mass-accuracy ESI instruments in conjunction with fractionation on both protein and peptide levels allows the analysis of very complex samples in a relatively short period of time (3). The identification and quantification of data can be done in an automatic or semi-automatic manner with a variety of well-established software packages (4).…”

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Global Relative Quantification with Liquid Chromatography–Matrix-assisted Laser Desorption Ionization Time-of-flight (LC-MALDI-TOF)—Cross–validation with LTQ-Orbitrap Proves Reliability and Reveals Complementary Ionization Preferences

Heßling

Büttner

Hecker

et al. 2013

Molecular & Cellular Proteomics

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Quantitative LC-MALDI is an underrepresented method, especially in large-scale experiments. The additional fractionation step that is needed for most MALDI-TOF-TOF instruments, the comparatively long analysis time, and the very limited number of established software tools for the data analysis render LC-MALDI a niche application for large quantitative analyses beside the widespread LCelectrospray ionization workflows.Here, we used LC-MALDI in a relative quantification analysis of Staphylococcus aureus for the first time on a proteome-wide scale. Samples were analyzed in parallel with an LTQ-Orbitrap, which allowed cross-validation with a well-established workflow. With nearly 850 proteins identified in the cytosolic fraction and quantitative data for more than 550 proteins obtained with the MASCOT Distiller software, we were able to prove that LC-MALDI is able to process highly complex samples. The good correlation of quantities determined via this method and the LTQ-Orbitrap workflow confirmed the high reliability of our LC-MALDI approach for global quantification analysis.Because the existing literature reports differences for MALDI and electrospray ionization preferences and the respective experimental work was limited by technical or methodological constraints, we systematically compared biochemical attributes of peptides identified with either instrument. This genome-wide, comprehensive study revealed biases toward certain peptide properties for both MALDI-TOF-TOF-and LTQ-Orbitrap-based approaches. These biases are based on almost 13,000 peptides and result in a general complementarity of the two approaches that should be exploited in future experiments. One-dimensional gel-based liquid chromatography mass spectrometry (GeLC-MS) 1 is a well-established technique in life science. In combination with in vivo labeling approaches such as stable isotope labeling by amino acids in cell culture (SILAC) (1) or 15 N labeling (2), it allows the relative quantification of large numbers of proteins in a complex sample. Mass spectrometry (MS) measurements in such workflows are predominantly performed with electrospray ionization (ESI)-based mass spectrometers. Online coupling of a liquid chromatography (LC) system with fast MS spectra acquisition and high-mass-accuracy ESI instruments in conjunction with fractionation on both protein and peptide levels allows the analysis of very complex samples in a relatively short period of time (3). The identification and quantification of data can be done in an automatic or semi-automatic manner with a variety of well-established software packages (4).In proteomic research, matrix-assisted laser desorption ionization time-of-flight tandem mass spectrometry (MALDI-TOF-TOF) is mainly used for the analysis of non-complex protein samples that do not require fractionation on the peptide level by means of liquid chromatography. In particular, the analysis of single protein spots resulting from the twodimensional PAGE separation of complex samples is primarily carried out with this MS tec...

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“…Several open source/academic and commercial software for quantitative analysis of proteomics MS/MS data are available supporting different MS instruments and labeling methods (20,(22)(23)(24)(25).…”

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Enhanced Information Output From Shotgun Proteomics Data by Protein Quantification and Peptide Quality Control (PQPQ)

Forshed

Johansson

Pernemalm

et al. 2011

Molecular & Cellular Proteomics

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We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.

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Capture and analysis of quantitative proteomic data

Cited by 47 publications

References 53 publications

Statistical Analysis of Relative Labeled Mass Spectrometry Data from Complex Samples Using ANOVA

Statistical Analysis of Relative Labeled Mass Spectrometry Data from Complex Samples Using ANOVA

Global Relative Quantification with Liquid Chromatography–Matrix-assisted Laser Desorption Ionization Time-of-flight (LC-MALDI-TOF)—Cross–validation with LTQ-Orbitrap Proves Reliability and Reveals Complementary Ionization Preferences

Enhanced Information Output From Shotgun Proteomics Data by Protein Quantification and Peptide Quality Control (PQPQ)

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