Literature DB >> 31241912

Automated Nanoflow Two-Dimensional Reversed-Phase Liquid Chromatography System Enables In-Depth Proteome and Phosphoproteome Profiling of Nanoscale Samples.

Maowei Dou1, Chia-Feng Tsai2, Paul D Piehowski2, Yang Wang2, Thomas L Fillmore1, Rui Zhao1, Ronald J Moore2, Pengfei Zhang2, Wei-Jun Qian2, Richard D Smith2, Tao Liu2, Ryan T Kelly1,3, Tujin Shi2, Ying Zhu1.   

Abstract

Two-dimensional reversed-phase capillary liquid chromatography (2D RPLC) separations have enabled comprehensive proteome profiling of biological systems. However, milligram sample quantities of proteins are typically required due to significant losses during offline fractionation. Such a large sample requirement generally precludes the application samples in the nanogram to low-microgram range. To achieve in-depth proteomic analysis of such small-sized samples, we have developed the nanoFAC (nanoflow Fractionation and Automated Concatenation) 2D RPLC platform, in which the first dimension high-pH fractionation was performed on a 75-μm i.d. capillary column at a 300 nL/min flow rate with automated fraction concatenation, instead of on a typically used 2.1 mm column at a 200 μL/min flow rate with manual concatenation. Each fraction was then fully transferred to the second-dimension low-pH nanoLC separation using an autosampler equipped with a custom-machined syringe. We have found that using a polypropylene 96-well plate as collection device as well as the addition of n-Dodecyl β-d-maltoside (0.01%) in the collection buffer can significantly improve sample recovery. We have demonstrated the nanoFAC 2D RPLC platform can achieve confident identifications of ∼49,000-94,000 unique peptides, corresponding to ∼6,700-8,300 protein groups using only 100-1000 ng of HeLa tryptic digest (equivalent to ∼500-5,000 cells). Furthermore, by integrating with phosphopeptide enrichment, the nanoFAC 2D RPLC platform can identify ∼20,000 phosphopeptides from 100 μg of MCF-7 cell lysate.

Entities:  

Year:  2019        PMID: 31241912      PMCID: PMC6741344          DOI: 10.1021/acs.analchem.9b01248

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  41 in total

1.  High-efficiency on-line solid-phase extraction coupling to 15-150-microm-i.d. column liquid chromatography for proteomic analysis.

Authors:  Yufeng Shen; Ronald J Moore; Rui Zhao; Josip Blonder; Deanna L Auberry; Christophe Masselon; Ljiljana Pasa-Tolić; Kim K Hixson; Ken J Auberry; Richard D Smith
Journal:  Anal Chem       Date:  2003-07-15       Impact factor: 6.986

Review 2.  Strategies for revealing lower abundance proteins in two-dimensional protein maps.

Authors:  Nuzhat Ahmed; Gregory E Rice
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2005-02-05       Impact factor: 3.205

3.  Automated 20 kpsi RPLC-MS and MS/MS with chromatographic peak capacities of 1000-1500 and capabilities in proteomics and metabolomics.

Authors:  Yufeng Shen; Rui Zhang; Ronald J Moore; Jeongkwon Kim; Thomas O Metz; Kim K Hixson; Rui Zhao; Eric A Livesay; Harold R Udseth; Richard D Smith
Journal:  Anal Chem       Date:  2005-05-15       Impact factor: 6.986

4.  Comparative evaluation of tandem MS search algorithms using a target-decoy search strategy.

Authors:  Brian M Balgley; Tom Laudeman; Li Yang; Tao Song; Cheng S Lee
Journal:  Mol Cell Proteomics       Date:  2007-05-28       Impact factor: 5.911

5.  Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.

Authors:  Chunxia Song; Mingliang Ye; Guanghui Han; Xinning Jiang; Fangjun Wang; Zhiyuan Yu; Rui Chen; Hanfa Zou
Journal:  Anal Chem       Date:  2010-01-01       Impact factor: 6.986

6.  Ultra-high-pressure RPLC hyphenated to an LTQ-Orbitrap Velos reveals a linear relation between peak capacity and number of identified peptides.

Authors:  Thomas Köcher; Remco Swart; Karl Mechtler
Journal:  Anal Chem       Date:  2011-03-09       Impact factor: 6.986

7.  A systematic strategy for proteomic analysis of chloroplast protein complexes in wheat.

Authors:  Qingshi Meng; Liqun Rao; Xiaocong Xiang; Chunxi Zhou; Xinyi Zhang; Yinghong Pan
Journal:  Biosci Biotechnol Biochem       Date:  2011-11-07       Impact factor: 2.043

8.  Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines.

Authors:  Andrew R Jones; Jennifer A Siepen; Simon J Hubbard; Norman W Paton
Journal:  Proteomics       Date:  2009-03       Impact factor: 3.984

9.  IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering.

Authors:  Ze-Qiang Ma; Surendra Dasari; Matthew C Chambers; Michael D Litton; Scott M Sobecki; Lisa J Zimmerman; Patrick J Halvey; Birgit Schilling; Penelope M Drake; Bradford W Gibson; David L Tabb
Journal:  J Proteome Res       Date:  2009-08       Impact factor: 4.466

10.  Phosphoprotein analysis: from proteins to proteomes.

Authors:  Frédéric Delom; Eric Chevet
Journal:  Proteome Sci       Date:  2006-07-19       Impact factor: 2.480

View more
  8 in total

Review 1.  Single-cell Proteomics: Progress and Prospects.

Authors:  Ryan T Kelly
Journal:  Mol Cell Proteomics       Date:  2020-08-26       Impact factor: 5.911

2.  Determining protein polarization proteome-wide using physical dissection of individual Stentor coeruleus cells.

Authors:  Athena Lin; Paul D Piehowski; Chia-Feng Tsai; Tatyana Makushok; Lian Yi; Ulises Diaz; Connie Yan; Diana Summers; Pranidhi Sood; Richard D Smith; Tao Liu; Wallace F Marshall
Journal:  Curr Biol       Date:  2022-04-20       Impact factor: 10.900

Review 3.  Review of Three-Dimensional Liquid Chromatography Platforms for Bottom-Up Proteomics.

Authors:  Van-An Duong; Jong-Moon Park; Hookeun Lee
Journal:  Int J Mol Sci       Date:  2020-02-23       Impact factor: 5.923

4.  An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics.

Authors:  Chia-Feng Tsai; Rui Zhao; Sarah M Williams; Ronald J Moore; Kendall Schultz; William B Chrisler; Ljiljana Pasa-Tolic; Karin D Rodland; Richard D Smith; Tujin Shi; Ying Zhu; Tao Liu
Journal:  Mol Cell Proteomics       Date:  2020-03-03       Impact factor: 5.911

5.  Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis.

Authors:  Yusuke Kawashima; Eiichiro Watanabe; Taichi Umeyama; Daisuke Nakajima; Masahira Hattori; Kenya Honda; Osamu Ohara
Journal:  Int J Mol Sci       Date:  2019-11-26       Impact factor: 5.923

6.  Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality.

Authors:  James A Sanford; Yang Wang; Joshua R Hansen; Marina A Gritsenko; Karl K Weitz; Tyler J Sagendorf; Cristina E Tognon; Vladislav A Petyuk; Wei-Jun Qian; Tao Liu; Brian J Druker; Karin D Rodland; Paul D Piehowski
Journal:  J Am Soc Mass Spectrom       Date:  2021-11-23       Impact factor: 3.109

7.  Hanging drop sample preparation improves sensitivity of spatial proteomics.

Authors:  Yumi Kwon; Paul D Piehowski; Rui Zhao; Ryan L Sontag; Ronald J Moore; Kristin E Burnum-Johnson; Richard D Smith; Wei-Jun Qian; Ryan T Kelly; Ying Zhu
Journal:  Lab Chip       Date:  2022-07-26       Impact factor: 7.517

8.  Deep Profiling of Microgram-Scale Proteome by Tandem Mass Tag Mass Spectrometry.

Authors:  Danting Liu; Shu Yang; Kanisha Kavdia; Jeffrey M Sifford; Zhiping Wu; Boer Xie; Zhen Wang; Vishwajeeth R Pagala; Hong Wang; Kaiwen Yu; Kaushik Kumar Dey; Anthony A High; Geidy E Serrano; Thomas G Beach; Junmin Peng
Journal:  J Proteome Res       Date:  2020-11-11       Impact factor: 4.466

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.