Literature DB >> 26873251

A Biologist's Field Guide to Multiplexed Quantitative Proteomics.

Corey E Bakalarski1, Donald S Kirkpatrick2.   

Abstract

High-throughput genomic and proteomic studies have generated near-comprehensive catalogs of biological constituents within many model systems. Nevertheless, static catalogs are often insufficient to fully describe the dynamic processes that drive biology. Quantitative proteomic techniques address this need by providing insight into closely related biological states such as the stages of a therapeutic response or cellular differentiation. The maturation of quantitative proteomics in recent years has brought about a variety of technologies, each with their own strengths and weaknesses. It can be difficult for those unfamiliar with this evolving landscape to match the experiment at hand with the best tool for the job. Here, we outline quantitative methods for proteomic mass spectrometry and discuss their benefits and weaknesses from the perspective of the biologist aiming to generate meaningful data and address mechanistic questions.
© 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Mesh:

Year:  2016        PMID: 26873251      PMCID: PMC4858934          DOI: 10.1074/mcp.O115.056986

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  119 in total

1.  Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers.

Authors:  Mikhail M Savitski; Gavain Sweetman; Manor Askenazi; Jarrod A Marto; Manja Lang; Nico Zinn; Marcus Bantscheff
Journal:  Anal Chem       Date:  2011-11-04       Impact factor: 6.986

2.  Addressing accuracy and precision issues in iTRAQ quantitation.

Authors:  Natasha A Karp; Wolfgang Huber; Pawel G Sadowski; Philip D Charles; Svenja V Hester; Kathryn S Lilley
Journal:  Mol Cell Proteomics       Date:  2010-04-10       Impact factor: 5.911

3.  Super-SILAC mix for quantitative proteomics of human tumor tissue.

Authors:  Tamar Geiger; Juergen Cox; Pawel Ostasiewicz; Jacek R Wisniewski; Matthias Mann
Journal:  Nat Methods       Date:  2010-04-04       Impact factor: 28.547

4.  The SILAC fly allows for accurate protein quantification in vivo.

Authors:  Matthias D Sury; Jia-Xuan Chen; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2010-06-05       Impact factor: 5.911

Review 5.  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

6.  SILAC zebrafish for quantitative analysis of protein turnover and tissue regeneration.

Authors:  Ann Westman-Brinkmalm; Alexandra Abramsson; Josef Pannee; Chen Gang; Mikael K Gustavsson; Malin von Otter; Kaj Blennow; Gunnar Brinkmalm; Hermann Heumann; Henrik Zetterberg
Journal:  J Proteomics       Date:  2011-08-18       Impact factor: 4.044

Review 7.  Phosphoproteomics for the masses.

Authors:  Paul A Grimsrud; Danielle L Swaney; Craig D Wenger; Nicole A Beauchene; Joshua J Coon
Journal:  ACS Chem Biol       Date:  2010-01-15       Impact factor: 5.100

8.  Stable-isotope labeling with amino acids in nematodes.

Authors:  Mark Larance; Aymeric P Bailly; Ehsan Pourkarimi; Ronald T Hay; Grant Buchanan; Sarah Coulthurst; Dimitris P Xirodimas; Anton Gartner; Angus I Lamond
Journal:  Nat Methods       Date:  2011-08-28       Impact factor: 28.547

9.  Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging.

Authors:  Craig D Wenger; M Violet Lee; Alexander S Hebert; Graeme C McAlister; Douglas H Phanstiel; Michael S Westphall; Joshua J Coon
Journal:  Nat Methods       Date:  2011-10-02       Impact factor: 28.547

10.  MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics.

Authors:  Lily Ting; Ramin Rad; Steven P Gygi; Wilhelm Haas
Journal:  Nat Methods       Date:  2011-10-02       Impact factor: 28.547

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  9 in total

Review 1.  Proteomics of nucleocytoplasmic partitioning.

Authors:  Thao Nguyen; Nishant Pappireddi; Martin Wühr
Journal:  Curr Opin Chem Biol       Date:  2018-11-23       Impact factor: 8.822

2.  Establishment of Dimethyl Labeling-based Quantitative Acetylproteomics in Arabidopsis.

Authors:  Shichang Liu; Fengchao Yu; Zhu Yang; Tingliang Wang; Hairong Xiong; Caren Chang; Weichuan Yu; Ning Li
Journal:  Mol Cell Proteomics       Date:  2018-02-13       Impact factor: 5.911

3.  Quantitative proteomics identify Tenascin-C as a promoter of lung cancer progression and contributor to a signature prognostic of patient survival.

Authors:  Vasilena Gocheva; Alexandra Naba; Arjun Bhutkar; Talia Guardia; Kathryn M Miller; Carman Man-Chung Li; Talya L Dayton; Francisco J Sanchez-Rivera; Caroline Kim-Kiselak; Noor Jailkhani; Monte M Winslow; Amanda Del Rosario; Richard O Hynes; Tyler Jacks
Journal:  Proc Natl Acad Sci U S A       Date:  2017-06-26       Impact factor: 11.205

4.  A SILAC-Based Method for Quantitative Proteomic Analysis of Intestinal Organoids.

Authors:  Alexis Gonneaud; Christine Jones; Naomie Turgeon; Dominique Lévesque; Claude Asselin; François Boudreau; François-Michel Boisvert
Journal:  Sci Rep       Date:  2016-11-30       Impact factor: 4.379

Review 5.  Technologies for Proteome-Wide Discovery of Extracellular Host-Pathogen Interactions.

Authors:  Nadia Martinez-Martin
Journal:  J Immunol Res       Date:  2017-02-22       Impact factor: 4.818

Review 6.  Proteomic Substrate Identification for Membrane Proteases in the Brain.

Authors:  Stephan A Müller; Simone D Scilabra; Stefan F Lichtenthaler
Journal:  Front Mol Neurosci       Date:  2016-10-13       Impact factor: 5.639

7.  Noise Exposures Causing Hearing Loss Generate Proteotoxic Stress and Activate the Proteostasis Network.

Authors:  Nopporn Jongkamonwiwat; Miguel A Ramirez; Seby Edassery; Ann C Y Wong; Jintao Yu; Tirzah Abbott; Kwang Pak; Allen F Ryan; Jeffrey N Savas
Journal:  Cell Rep       Date:  2020-11-24       Impact factor: 9.423

8.  MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.

Authors:  Ting Huang; Meena Choi; Manuel Tzouros; Sabrina Golling; Nikhil Janak Pandya; Balazs Banfai; Tom Dunkley; Olga Vitek
Journal:  Mol Cell Proteomics       Date:  2020-07-17       Impact factor: 5.911

9.  Proposing a minimal set of metrics and methods to predict probabilities of amyloidosis disease and onset age in individuals.

Authors:  Richard S Criddle; Hsien-Jung L Lin; Isabella James; Ji Sun Park; Lee D Hansen; John C Price
Journal:  Aging (Albany NY)       Date:  2020-11-18       Impact factor: 5.682

  9 in total

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