Predicting cell-specific productivity from CHO gene expression

Clarke, Colin; Doolan, Padraig; Barron, Niall; O’Sullivan, Finbarr; Gammell, Patrick; Melville, Mark; Leonard, Mark; Clynes, Martin

doi:10.1016/j.jbiotec.2010.11.016

Cited by 79 publications

(56 citation statements)

References 49 publications

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“…Moreover, pleiotropy, the phenomenon where a single gene may have multiple functions, which may not all be known [31], can make the interpretation of results difficult. Consequently, recent publications have applied complementary computational strategies in the analysis of large datasets, such as coexpression analysis [32] or partial least squares analysis [33]. Other, more advanced computational approaches, that have not yet been applied to CHO, might become feasible once either larger data sets become available, such as independent component analysis [34,35], or methods that exploit a supervised setting, such as deep learning algorithms [36].…”

Section: Introductionmentioning

confidence: 99%

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

Harreither

Hackl

Pichler

et al. 2015

Biotechnology Journal

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Over the last three decades, product yields from CHO cells have increased dramatically, yet specific productivity (qP) remains a limiting factor. In a previous study, using repeated cell-sorting, we have established different host cell subclones that show superior transient qP over their respective parental cell lines (CHO-K1, CHO-S). The transcriptome of the resulting six cell lines in different biological states (untransfected, mock transfected, plasmid transfected) was first explored by hierarchical clustering and indicated that gene activity associated with increased qP did not stem from a certain cellular state but seemed to be inherent for a high qP host line. We then performed a novel gene regression analysis identifying drivers for an increase in qP. Genes significantly implicated were first systematically tested for enrichment of GO terms using a Bayesian approach incorporating the hierarchical structure of the GO term tree. Results indicated that specific cellular components such as nucleus, ER, and Golgi are relevant for cellular productivity. This was complemented by targeted GSA that tested functionally homogeneous, manually curated subsets of KEGG pathways known to be involved in transcription, translation, and protein processing. Significantly implicated pathways included mRNA surveillance, proteasome, protein processing in the ER and SNARE interactions in vesicular transport.

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Section: Introductionmentioning

confidence: 99%

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

Harreither

Hackl

Pichler

et al. 2015

Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

“…In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al, 2011;Clarke et al, 2012;Doolan et al, 2013;Jacob et al, 2010;Mead et al, 2009;Mead et al, 2012;Meleady et al, 2011;Sanchez et al, 2014;Selvarasu et al, 2012), however these models are usually developed from cell line data at the end of the cell line development process.The goal of this work was to develop a screening system that would allow the selection of highly productive cell lines for monoclonal antibody (mAb) production early in the cell line development process that would use substantially less resource to achieve the same or better success rate as current methods. The vision was to be able to select a small number of cell lines based upon the analysis of data generated in multi-well plates, and take these straight to a lab-scale bioreactorevaluation stage (10 L) with a high probability that the selected cell lines were highly productive.…”

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confidence: 99%

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Povey

O'Malley

Root

et al. 2014

Journal of Biotechnology

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Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data is used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10 L bioreactor model of Lonza's large-scale (up to 20,000 L) fed-batch cell culture processes.Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10 L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10 L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements.Keywords: Cell line development, Chinese hamster ovary cells, whole cell MALDI-ToF mass spectrometry, PLS-DA modelling, cell line prediction 3 IntroductionThe majority of recombinant protein biopharmaceuticals are produced from cultured mammalian cells (Walsh, 2010), with the most commonly used industrial mammalian cell host being the Chinese hamster ovary (CHO) cell (Kim et al., 2012). Despite the development of high throughput methods that allow the screening of many recombinant cell lines to isolate those with desirable phenotypes (e.g. high growth and productivity), the ability of such methods to select or predict the performance of a given cell line at manufacturing scale remains limited with the best cell lines at a manufacturing scale often distributed across the phenotypic performance of cell lines at smaller-scale (Porter et al., 2010a;Porter et al., 2010b). As a result, early use of a simple productivity based approach does not necessarily allow the identification of high producers in the population of cell lines, and some potentially high producers are discarded early in the process (Porter et al., 2010b). In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al., 2011;Clarke...

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“…Uncertainties related to cross species hybridization [38,39] have largely restricted transcriptomic analysis of CHO cultures to self-made cDNA arrays and custom-made CHO chips. In each case only a small percentage of the complete transcriptome was covered (e.g., the Affymetrix Wyehamster2a gene chip included only 10-15% of all transcripts) and the percentage of well-annotated sequences was even lower [40]. Fortunately, the genome of CHO-K1 has now been sequenced and is publicly available [41].…”

Section: Technical Limitationsmentioning

confidence: 99%

Mammalian cells as biopharmaceutical production hosts in the age of omics

Dietmair

Nielsen

Timmins

2011

Biotechnology Journal

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Mammalian cells are important hosts for the production of a wide range of biopharmaceuticals due to their ability to produce correctly folded and glycosylated proteins. Compared to microbes and yeast, however, the productivity of mammalian cells is low because of their comparatively slow growth rate, tendency to undergo apoptosis, and low production capacities. While much effort has been invested in the engineering of mammalian cells with superior production characteristics, the success of these approaches has been limited to date. One factor responsible for this lack of success is our limited understanding of the cellular basis for high productivity, and of how discrete mechanisms within a cell contribute to the overall phenotype. Aiming to measure and characterize all cellular components at different functional levels, omics technologies have the potential to improve our understanding of mammalian cell physiology, elucidating new targets for the generation of a superior host cell line. This review provides a comprehensive analysis of recent examples of omics studies in the context of mammalian cells as production hosts, highlighting both the challenges and successes in the application of these powerful technologies.

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Predicting cell-specific productivity from CHO gene expression

Cited by 79 publications

References 49 publications

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Mammalian cells as biopharmaceutical production hosts in the age of omics

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