Ribo Mega-SEC: Efficient analysis of mammalian polysomes and ribosome subunits by Size Exclusion Chromatography

DOI
  • Yoshikawa Harunori
    Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee
  • Lamond Angus I.
    Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee

Bibliographic Information

Other Title
  • Ribo Mega-SEC:サイズ排除クロマトグラフィーによる簡便なリボソームの分離法

Description

<p>The ribosome is a large RNA-protein complex, comprising four ribosomal RNAs and >80 ribosomal proteins. In human cells fully assembled ribosomes have a molecular weight of ~4.3 MDa and a diameter of 250–300 Å. During translation, multiple ribosomes can simultaneously engage the same mRNA to form ‘polysomes’. Centrifugation-based methods, particularly sucrose density gradients, have previously been used as the gold standard approach for separating polysomes from monosomes and free ribosome subunits. However, these gradient-based methods are technically complicated and time-consuming. We have developed an efficient new approach for mammalian polysome fractionation, called Ribo Mega-SEC. This separates polysomes and free ribosomal subunits by uHPLC, using Size Exclusion Chromatography (SEC) columns with a pore size of 2,000 Å. Using extracts from either cells, or tissues, polysomes can be separated, with high reproducibility, within 15 min from sample injection to fraction collection. Ribo Mega-SEC is readily combined with downstream analyses, including either electron microscopy, high-throughput MS-based proteomics, or RNA-Seq. The efficiency and reproducibility of the Ribo Mega-SEC method therefore facilitates a wide range of biochemical studies on polysomes and translation complexes in mammalian cells and tissues.</p>

Journal

  • Proteome Letters

    Proteome Letters 5 (1), 13-22, 2020

    Japanese Proteomics Society

Details 詳細情報について

  • CRID
    1390566775161618432
  • NII Article ID
    130007891647
  • DOI
    10.14889/jpros.5.1_13
  • ISSN
    24322776
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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