Proteomics in Cell Culture: From Genomics to Combined ‘Omics for Cell Line Engineering and Bioprocess Development

Kelley Heffner, Christian Schrøder Kaas, Amit Kumar, Deniz Baycin-Hizal, Michael Betenbaugh

    Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

    Abstract

    The genetic sequencing of Chinese hamster ovary cells has initiated a systems biology era for biotechnology applications. In addition to genomics, critical omics data sets also include proteomics, transcriptomics and metabolomics. Recently, the use of proteomics in cell lines for recombinant protein production has increased significantly because proteomics can track changes in protein levels for different cell lines over time, which can be advantageous for bioprocess development and optimization. Specifically, the identification of proteins that affect cell culture processes can aid efforts in media development and cell line engineering to improve growth or productivity, delay the onset of apoptosis, or utilize nutrients efficiently. Mass-spectrometry based and other proteomics methods can provide for the detection of thousands of proteins from cell culture and bioinformatics analysis serves to identify and quantify protein levels. Optimizations of sample preparations and database development, including a detailed CHO proteome now available, have improved the quantity and accuracy of identified proteins. The applications are widespread and expanding, thus suggesting numerous applications of proteomics and combined ‘omics experiments in coming years.
    Original languageEnglish
    Title of host publicationAnimal Cell Culture
    Number of pages24
    Volume9
    PublisherSpringer
    Publication date2015
    Pages591-614
    ChapterChapter 19
    ISBN (Electronic)9783319103204
    Publication statusPublished - 2015
    SeriesCell Engineering
    ISSN1389-6946

    Keywords

    • Proteomics
    • Genomics
    • Transcriptomics
    • Metabolomics
    • Bioprocess development
    • Cell line engineering
    • Bioinformatics

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