An RNA-seq based transcriptomic investigation into the productivity and growth variants with Chinese hamster ovary cells

Sha, Sha; Bhatia, Hemlata; Yoon, Seongkyu

doi:10.1016/j.jbiotec.2018.02.008

Cited by 32 publications

(30 citation statements)

References 47 publications

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“…The results of the GO analysis are interesting in the light of previous studies for e.g., study by Sha et al demonstrated that CHO high producers expresses high amount of genes related to secretion and protein transportation. Further, work by Clark et al performed on CHO‐K1 and CHO‐DUX producer cell lines indicated that the rate limiting step in the secretion of the protein might lie in the translational and post‐translational processes.…”

Section: Resultsmentioning

confidence: 82%

An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures

Singh

Kildegaard

Andersen

2018

Biotechnology Journal

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Chinese hamster ovary (CHO) cell lines can fold, assemble, and modify proteins post-translationally to produce human-like proteins; as a consequence, it is the single most common expression systems for industrial production of recombinant therapeutic proteins. A thorough knowledge of cultivation conditions of different CHO cell lines has been developed over the last decade, but comprehending gene or pathway-specific distinctions between CHO cell lines at transcriptome level remains a challenge. To address these challenges, a compendium of 23 RNA-Seq studies from public and in-house data on CHO cell lines, i.e., CHO-S, CHO-K1, and DG44 is compiled. Significantly differentially expressed (DE) genes particularly related to subcellular structure and macromolecular categories are used to identify differences between the cell lines. A R-based web application is developed specifically for CHO cell lines to further visualize expression values across different cell lines, and make available the normalized full CHO data set graphically as a CHO research community resource. This study quantitatively categorizes CHO cell lines based on patterns at transcriptomic level and detects gene and pathway specific key distinctions among sibling cell lines. Studies such as this can be used to select desired characteristics across various CHO cell lines. Furthermore, the availability of the data as an internet-based application can be applied to broad range of CHO engineering applications.

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Section: Resultsmentioning

confidence: 82%

An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures

Singh

Kildegaard

Andersen

2018

Biotechnology Journal

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“…These three cell lines were derived from the same parental cell line. The detailed information on cell culture experiments can be found in the previous publication [23]. Cells were cultured in shake flasks for seven days with an inoculation density of 2 × 10 5 cells/mL and a working volume of 50 mL.…”

Section: Experimental Datamentioning

confidence: 99%

“…For each cell line, triplicate flasks were conducted in batch mode. Time-series gene expression profiles were determined daily using RNA-sequencing (RNA-Seq) from day 3 to day 6 [23]. A total of four data points of gene expression were collected.…”

Section: Experimental Datamentioning

confidence: 99%

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Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Huang

Yoon

2020

Processes

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Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

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“…In particular, our ability to analyse the CHO cell transcriptome has improved markedly in the postgenomic era (Monger et al, 2015). RNA sequencing (RNASeq) approaches have enabled the accurate quantitation of variations in gene expression associated with critical industrial phenotypes such as cellular growth rate, cell specific productivity (Sha, Bhatia, & Yoon, 2018) and product quality (Clarke et al, 2019). The application of RNASeq has also played a pivotal role in the identification of promoter regions in the CHO cell genome, improving the annotation of small non-coding RNAs (Gerstl, Hackl, Graf, Borth, & Grillari, 2013;Hackl et al, 2012) and opening new routes for genetic engineering to improve CHO cell line performance (Raab et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sub physiological temperature induces pervasive alternative splicing in Chinese hamster ovary cells

Tzani

Monger

Motheramgari

et al. 2019

Preprint

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AbstractRNA sequencing (RNASeq) has been widely used to associate alterations in Chinese hamster ovary (CHO) cell gene expression with bioprocess phenotypes, however alternative mRNA splicing, has thus far, received little attention. In this study, we utilised RNASeq for transcriptomic analysis of a monoclonal antibody producing CHOK1 cell line subjected to a temperature shift. More than 2,365 instances of differential splicing were observed 24hrs after the reduction of cell culture temperature. 1,163 of these alternative splicing events were identified in genes where no changes in abundance were detected by standard differential expression analysis. Ten examples of alternative splicing were selected for independent validation using qPCR in the monoclonal antibody producing CHOK1 line used for RNASeq and a further 2 CHOK1 cell lines. This analysis provided evidence that exon skipping and mutually exclusive splicing events occur in genes linked to the cellular response to changes in temperature and mitochondrial function. While further work is required to determine the impact of changes in mRNA sequence on cellular phenotype, this study demonstrates that alternative splicing analysis can be utilised to gain a deeper understanding of post-transcriptional regulation in CHO cells during biopharmaceutical production.

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An RNA-seq based transcriptomic investigation into the productivity and growth variants with Chinese hamster ovary cells

Cited by 32 publications

References 47 publications

An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures

An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Sub physiological temperature induces pervasive alternative splicing in Chinese hamster ovary cells

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