Research Article: Intraclonal Protein Expression Heterogeneity in Recombinant CHO Cells

Date Published: December 23, 2009

Publisher: Public Library of Science

Author(s): Warren Pilbrough, Trent P. Munro, Peter Gray, Grzegorz Kudla.

Abstract: Therapeutic glycoproteins have played a major role in the commercial success of biotechnology in the post-genomic era. But isolating recombinant mammalian cell lines for large-scale production remains costly and time-consuming, due to substantial variation and unpredictable stability of expression amongst transfected cells, requiring extensive clone screening to identify suitable high producers. Streamlining this process is of considerable interest to industry yet the underlying phenomena are still not well understood. Here we examine an antibody-expressing Chinese hamster ovary (CHO) clone at single-cell resolution using flow cytometry and vectors, which couple light and heavy chain transcription to fluorescent markers. Expression variation has traditionally been attributed to genetic heterogeneity arising from random genomic integration of vector DNA. It follows that single cell cloning should yield a homogeneous cell population. We show, in fact, that expression in a clone can be surprisingly heterogeneous (standard deviation 50 to 70% of the mean), approaching the level of variation in mixed transfectant pools, and each antibody chain varies in tandem. Phenotypic variation is fully developed within just 18 days of cloning, yet is not entirely explained by measurement noise, cell size, or the cell cycle. By monitoring the dynamic response of subpopulations and subclones, we show that cells also undergo slow stochastic fluctuations in expression (half-life 2 to 11 generations). Non-genetic diversity may therefore play a greater role in clonal variation than previously thought. This also has unexpected implications for expression stability. Stochastic gene expression noise and selection bias lead to perturbations from steady state at the time of cloning. The resulting transient response as clones reestablish their expression distribution is not ordinarily accounted for but can contribute to declines in median expression over timescales of up to 50 days. Noise minimization may therefore be a novel strategy to reduce apparent expression instability and simplify cell line selection.

Partial Text: Protein biologics are an important and growing segment of the drug industry with over US$80 billion in sales worldwide. Many protein biologics, including monoclonal antibodies, are large, structurally-complex glycoproteins requiring functional human-like post-translational modifications for their in vivo activity [1]. Cultured mammalian cells, and particularly Chinese hamster ovary (CHO) cells [2], are generally employed as production hosts because simpler prokaryotic and eukaryotic expression systems lack suitable native glycosylation machinery and may not fold and secrete these biomolecules efficiently [3]. Yet despite their widespread use and commercial significance, two major issues remain unresolved in establishing productive mammalian cell lines, namely clonal heterogeneity [4] and expression instability [5].

When establishing stable cell lines, considerable variation is observed between clones, which has traditionally been attributed to genetic heterogeneity in the transfectant pools from which the clones are isolated. We show that in addition to genetic heterogeneity, a significant fraction of total variation may arise from phenotypic differences between cells in each pure clone making up a pool. This, in turn, appears to result from random expression fluctuations in individual cells over time, as elegantly demonstrated in the landmark study of Sigal et al. [39]. Since phenotypic variation is ultimately non-heritable, the exploitable diversity in transfectant pools may be less than previously thought. The combination of novel intracellular transcription markers and high-throughput single-cell analysis, along with the simplicity and sensitivity of our method, was crucial to this advance.



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