Literature DB >> 34486781

Discovery proteomics of human placental tissue.

Allyson L Mellinger1, Krista McCoy2, Duy An T Minior3, Taufika Islam Williams1,4.   

Abstract

We describe a label-free proteomics protocol for the interrogation of the placental proteome. Step-by-step directions, including tissue cleanup and preparation, proteolytic digestion, nanoLC-MS/MS data collection and data analysis, are provided. The workflow has been applied toward exploring differential protein expression patterns in placentas from women who have been exposed to drugs during pregnancy relative to those who have not. We collected 20 tissue specimens, each representing a combination of spatially diverse sections across the placenta. These specimens were analyzed in the work described here, to survey information across the entire organ. This protocol can be scaled up or down as needed.
© 2021 John Wiley & Sons Ltd.

Entities:  

Year:  2021        PMID: 34486781      PMCID: PMC9218992          DOI: 10.1002/rcm.9189

Source DB:  PubMed          Journal:  Rapid Commun Mass Spectrom        ISSN: 0951-4198            Impact factor:   2.586


  10 in total

Review 1.  Taking tissue samples from the placenta: an illustration of principles and strategies.

Authors:  T M Mayhew
Journal:  Placenta       Date:  2007-07-23       Impact factor: 3.481

Review 2.  Proteomics of the human placenta: promises and realities.

Authors:  J M Robinson; W E Ackerman; D A Kniss; T Takizawa; D D Vandré
Journal:  Placenta       Date:  2008-01-28       Impact factor: 3.481

3.  Universal sample preparation method for proteome analysis.

Authors:  Jacek R Wiśniewski; Alexandre Zougman; Nagarjuna Nagaraj; Matthias Mann
Journal:  Nat Methods       Date:  2009-04-19       Impact factor: 28.547

4.  Proteomics analysis of human placenta reveals glutathione metabolism dysfunction as the underlying pathogenesis for preeclampsia.

Authors:  Xiaohan Jin; Zhongwei Xu; Jin Cao; Ping Shao; Maobin Zhou; Zhe Qin; Yan Liu; Fang Yu; Xin Zhou; Wenjie Ji; Wei Cai; Yongqiang Ma; Chengyan Wang; Nana Shan; Ning Yang; Xu Chen; Yuming Li
Journal:  Biochim Biophys Acta Proteins Proteom       Date:  2017-07-10       Impact factor: 3.036

5.  NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis.

Authors:  Jakob Willforss; Aakash Chawade; Fredrik Levander
Journal:  J Proteome Res       Date:  2018-10-15       Impact factor: 4.466

6.  An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments.

Authors:  Michael S Bereman; Joshua Beri; Vagisha Sharma; Cory Nathe; Josh Eckels; Brendan MacLean; Michael J MacCoss
Journal:  J Proteome Res       Date:  2016-10-04       Impact factor: 4.466

7.  Optimising sample collection for placental research.

Authors:  G J Burton; N J Sebire; L Myatt; D Tannetta; Y-L Wang; Y Sadovsky; A C Staff; C W Redman
Journal:  Placenta       Date:  2013-11-19       Impact factor: 3.481

8.  Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS.

Authors:  Yangyang Bian; Runsheng Zheng; Florian P Bayer; Cassandra Wong; Yun-Chien Chang; Chen Meng; Daniel P Zolg; Maria Reinecke; Jana Zecha; Svenja Wiechmann; Stephanie Heinzlmeir; Johannes Scherr; Bernhard Hemmer; Mike Baynham; Anne-Claude Gingras; Oleksandr Boychenko; Bernhard Kuster
Journal:  Nat Commun       Date:  2020-01-09       Impact factor: 14.919

9.  Pilot study of placental tissue collection, processing, and measurement procedures for large scale assessment of placental inflammation.

Authors:  Lindsey A Sjaarda; Katherine A Ahrens; Daniel L Kuhr; Tiffany L Holland; Ukpebo R Omosigho; Brian T Steffen; Natalie L Weir; Hannah K Tollman; Robert M Silver; Michael Y Tsai; Enrique F Schisterman
Journal:  PLoS One       Date:  2018-05-11       Impact factor: 3.240

  10 in total

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