Literature DB >> 21888428

A novel alignment method and multiple filters for exclusion of unqualified peptides to enhance label-free quantification using peptide intensity in LC-MS/MS.

Xianyin Lai1, Lianshui Wang, Haixu Tang, Frank A Witzmann.   

Abstract

Though many software packages have been developed to perform label-free quantification of proteins in complex biological samples using peptide intensities generated by LC-MS/MS, two critical issues are generally ignored in this field: (i) peptides have multiple elution patterns across runs in an experiment, and (ii) many peptides cannot be used for protein quantification. To address these two key issues, we have developed a novel alignment method to enable accurate peptide peak retention time determination and multiple filters to eliminate unqualified peptides for protein quantification. Repeatability and linearity have been tested using six very different samples, i.e., standard peptides, kidney tissue lysates, HT29-MTX cell lysates, depleted human serum, human serum albumin-bound proteins, and standard proteins spiked in kidney tissue lysates. At least 90.8% of the proteins (up to 1,390) had CVs ≤ 30% across 10 technical replicates, and at least 93.6% (up to 2,013) had R(2) ≥ 0.9500 across 7 concentrations. Identical amounts of standard protein spiked in complex biological samples achieved a CV of 8.6% across eight injections of two groups. Further assessment was made by comparing mass spectrometric results to immunodetection, and consistent results were obtained. The new approach has novel and specific features enabling accurate label-free quantification.

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Year:  2011        PMID: 21888428      PMCID: PMC3216047          DOI: 10.1021/pr2005633

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  33 in total

1.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

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Review 2.  Approaches for the quantification of protein concentration ratios.

Authors:  Bernd Moritz; Helmut E Meyer
Journal:  Proteomics       Date:  2003-11       Impact factor: 3.984

Review 3.  Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments.

Authors:  Jason W H Wong; Matthew J Sullivan; Gerard Cagney
Journal:  Brief Bioinform       Date:  2007-09-28       Impact factor: 11.622

4.  Correlation metric for generalized feature extraction.

Authors:  Yun Fu; Shuicheng Yan; Thomas S Huang
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Review 5.  Current trends in quantitative proteomics.

Authors:  Monica H Elliott; Derek S Smith; Carol E Parker; Christoph Borchers
Journal:  J Mass Spectrom       Date:  2009-12       Impact factor: 1.982

Review 6.  Less label, more free: approaches in label-free quantitative mass spectrometry.

Authors:  Karlie A Neilson; Naveid A Ali; Sridevi Muralidharan; Mehdi Mirzaei; Michael Mariani; Gariné Assadourian; Albert Lee; Steven C van Sluyter; Paul A Haynes
Journal:  Proteomics       Date:  2011-01-17       Impact factor: 3.984

7.  Model of pediatric pituitary hormone deficiency separates the endocrine and neural functions of the LHX3 transcription factor in vivo.

Authors:  Stephanie C Colvin; Raleigh E Malik; Aaron D Showalter; Kyle W Sloop; Simon J Rhodes
Journal:  Proc Natl Acad Sci U S A       Date:  2010-12-13       Impact factor: 11.205

8.  Characterization of the renal cyst fluid proteome in autosomal dominant polycystic kidney disease (ADPKD) patients.

Authors:  Xianyin Lai; Robert L Bacallao; Bonnie L Blazer-Yost; David Hong; Stephen B Mason; Frank A Witzmann
Journal:  Proteomics Clin Appl       Date:  2008-07-01       Impact factor: 3.494

9.  Software platform for rapidly creating computational tools for mass spectrometry-based proteomics.

Authors:  Damon May; Wendy Law; Matt Fitzgibbon; Qiaojun Fang; Martin McIntosh
Journal:  J Proteome Res       Date:  2009-06       Impact factor: 4.466

Review 10.  Mass spectrometry-based label-free quantitative proteomics.

Authors:  Wenhong Zhu; Jeffrey W Smith; Chun-Ming Huang
Journal:  J Biomed Biotechnol       Date:  2009-11-10
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  28 in total

1.  Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

Authors:  Bobbie-Jo M Webb-Robertson; Melissa M Matzke; Susmita Datta; Samuel H Payne; Jiyun Kang; Lisa M Bramer; Carrie D Nicora; Anil K Shukla; Thomas O Metz; Karin D Rodland; Richard D Smith; Mark F Tardiff; Jason E McDermott; Joel G Pounds; Katrina M Waters
Journal:  Mol Cell Proteomics       Date:  2014-12       Impact factor: 5.911

Review 2.  Protein analysis by shotgun/bottom-up proteomics.

Authors:  Yaoyang Zhang; Bryan R Fonslow; Bing Shan; Moon-Chang Baek; John R Yates
Journal:  Chem Rev       Date:  2013-02-26       Impact factor: 60.622

3.  Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns.

Authors:  Susan C Tilton; Norman J Karin; Ana Tolic; Yumei Xie; Xianyin Lai; Raymond F Hamilton; Katrina M Waters; Andrij Holian; Frank A Witzmann; Galya Orr
Journal:  Nanotoxicology       Date:  2013-06-07       Impact factor: 5.913

4.  Multi-walled carbon nanotube directed gene and protein expression in cultured human aortic endothelial cells is influenced by suspension medium.

Authors:  Achini K Vidanapathirana; Xianyin Lai; Susana C Hilderbrand; Josh E Pitzer; Ramakrishna Podila; Susan J Sumner; Timothy R Fennell; Christopher J Wingard; Frank A Witzmann; Jared M Brown
Journal:  Toxicology       Date:  2012-09-28       Impact factor: 4.221

5.  Comparison of nanotube-protein corona composition in cell culture media.

Authors:  Jonathan H Shannahan; Jared M Brown; Ran Chen; Pu Chun Ke; Xianyin Lai; Somenath Mitra; Frank A Witzmann
Journal:  Small       Date:  2013-01-16       Impact factor: 13.281

6.  Exogenous Gene Transmission of Isocitrate Dehydrogenase 2 Mimics Ischemic Preconditioning Protection.

Authors:  Alexander L Kolb; Peter R Corridon; Shijun Zhang; Weimin Xu; Frank A Witzmann; Jason A Collett; George J Rhodes; Seth Winfree; Devin Bready; Zechariah J Pfeffenberger; Jeremy M Pomerantz; Takashi Hato; Glenn T Nagami; Bruce A Molitoris; David P Basile; Simon J Atkinson; Robert L Bacallao
Journal:  J Am Soc Nephrol       Date:  2018-01-25       Impact factor: 10.121

7.  Reproducible method to enrich membrane proteins with high purity and high yield for an LC-MS/MS approach in quantitative membrane proteomics.

Authors:  Xianyin Lai
Journal:  Electrophoresis       Date:  2013-02-25       Impact factor: 3.535

8.  Developmental analysis and influence of genetic background on the Lhx3 W227ter mouse model of combined pituitary hormone deficiency disease.

Authors:  Kelly L Prince; Stephanie C Colvin; Soyoung Park; Xianyin Lai; Frank A Witzmann; Simon J Rhodes
Journal:  Endocrinology       Date:  2013-01-03       Impact factor: 4.736

9.  Perivascular adipose tissue potentiates contraction of coronary vascular smooth muscle: influence of obesity.

Authors:  Meredith Kohr Owen; Frank A Witzmann; Mikaela L McKenney; Xianyin Lai; Zachary C Berwick; Steven P Moberly; Mouhamad Alloosh; Michael Sturek; Johnathan D Tune
Journal:  Circulation       Date:  2013-05-17       Impact factor: 29.690

10.  Proteomic profiling of halloysite clay nanotube exposure in intestinal cell co-culture.

Authors:  Xianyin Lai; Mangilal Agarwal; Yuri M Lvov; Chetan Pachpande; Kody Varahramyan; Frank A Witzmann
Journal:  J Appl Toxicol       Date:  2013-04-22       Impact factor: 3.446

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