Cell Line Development

Incorporate New Technology to Streamline Cell Line Characterization and Deliver Better Data

Rethink your therapeutic antibody cell line development process with copy number measurement and genome edit detection that’s accurate, precise, and requires fewer cells than qPCR.

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One of the bottlenecks in bringing a therapeutic antibody to IND,1 is that stable cell line development can take anywhere from several months to an entire year.1 Thus, any efficiencies that can speed up the cell line development process can have a big impact on the speed of your entire development program.

However, changing well-established workflows can feel daunting. You need to achieve significant benefits to feel like the change is worthwhile.

Making the switch from qPCR to Droplet Digital™ PCR (ddPCR™) for measuring the copy number of your antibody expressing gene and detecting genome edits is one process change that is definitely worth considering.

The switch to ddPCR does more than deliver accurate and reliable data. With the ability to provide absolute quantification using much less sample and accuracy over a wide range of copy numbers, ddPCR can streamline your cell line development workflows and help you choose the best clones earlier to accelerate development.

  • lightbulb

    Did you know?

    A sample of the first monoclonal antibody approved for therapeutic use in humans, Orthoclone OKT3 Muromonab-CD3, is stored in the National Museum of American History. The sample was donated by the company that discovered and developed the mAb, Ortho Pharmaceuticals.2

How ddPCR Technology Delivers Better Data

Compared to qPCR, ddPCR offers a number of technical advantages that can provide more accurate and reliable data for making critical decisions in cell line development:

  • Higher sensitivity

    Higher sensitivity

    With higher sensitivity and absolute quantitation, you can find the best clones earlier in the process, speeding development

  • Increased signal-to-noise

    Increased signal-to-noise

    Enrich for rare targets by reducing competition that comes from high-copy templates

  • Measurement accuracy regardless of copy number

    Measurement accuracy regardless of copy number

    Reducing competition from high-copy templates also ensures accurate quantification regardless of target copy number

  • Removal of PCR efficiency bias

    Removal of PCR efficiency bias

    Error rates are reduced by removing the amplification efficiency reliance of qPCR, enabling accurate quantification of targets

  • Simplified quantification

    Simplified quantification

    A standard curve is not required for absolute quantification

  •  Less sample required

    Less sample required

    Because a standard curve is not needed for absolute quantification, you can obtain reliable copy number measurement with fewer reactions and, thus, fewer cells

How ddPCR Technology Simplifies Cell Line Development

Compared to qPCR, ddPCR can streamline stable cell line characterization, improving efficiency and accelerating development:

Make decisions earlier

With higher sensitivity and the ability to obtain accurate quantification without the need for a standard curve, quantification with ddPCR requires less input sample than qPCR. As a result, you require much fewer cells — ddPCR can detect events at frequencies as low as 0.05% with a minimum input of 6000 cells — and can analyze your sample at an earlier stage than with qPCR.

Another advantage of not needing to set up a standard curve is that you need fewer reactions to measure the copy number of your target gene. While ddPCR does include an additional droplet generation step and read-out step, these steps are highly automated and are compatible with 96-well plate formats.

Spend less on growth media and reagents

Along with the time savings you achieve with the need for fewer cells, you also save on the costs for culture media and related reagents and consumables.

Using ddPCR in Cell Line Development



Systematic Quantification of HDR and NHEJ Reveals Effects of Locus, Nuclease, and Cell Type on Genome-Editing. Miyaoka Y. et al. Sci Rep. 2016 Mar 31;6:23549. DOI: 10.1038/srep23549. PMID: 27030102; PMCID: PMC4814844.

Transcriptome and Proteome Analysis of Steady-State in a Perfusion CHO Cell Culture Process. Bertrand V. et al. Biotechnol Bioeng. 2019 Aug;116(8):1959-1972. DOI: 10.1002/bit.26996. Epub 2019 May 7. PMID: 30997936.



  1. Vuksanaj K. Mapping the Future of Cell Culture and Cell Line Development. GEN - Genetic Engineering and Biotechnology News. Published August 2, 2021. Accessed December 8, 2021. https://www.genengnews.com/insights/mapping-the-future-of-cell-culture-and-cell-line-development/
  2. Orthoclone OKT3 Muromonab-CD3. National Museum of American History. Accessed December 7, 2021. https://americanhistory.si.edu/collections/search/object/nmah_1000965

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