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Improving gene editing success with an optimized cell line screening procedure

There exist now a range of techniques to perform genome editing, such as ZFN, CRISPR, TALENS and AAV, each with their own strengths and weaknesses. However, one consistent element that has a significant impact on the success of that editing event when generating an isogenic cell line is the choice of parental cell line to be engineered.

Below we detail Horizon's characterization approach to select cell lines which not only improves the chance of gene editing success, but can also drive the selection of which engineering technique to use.

In particular, we highlight the benefits of assessing

  • conditions required to generate clonal populations of a cell line
  • copy number of the genetic locus of interest
  • ability of the cell line to tolerate delivery of the targeting reagents by a variety of methods.
Assessing conditions required to generate clonal populations of cell line

In order to reliably assess the functional effects of any genome editing event, it is important to ensure a clonal population of cells is achieved. Many cell lines do not tolerate growth from a single cell under standard conditions, and it is necessary to trial a range of conditions to find one which will yield sufficient clonal populations. Growth of colonies, following low density plate-out, can be monitored by eye or using an automated imaging system.

Figure 1. Visualization of growth of cells following single cell dilution to assess growth and clonality of populations. (A) Cells are plated at a variety of densities and conditions in 384W plate format. Cells can be visualized by eye or using an automated imaging system such as the Solentim Cell Metric. (B) Close up images from highlighted well in (A). Clonality can be investigated by tracking images taken at varying time points following plating.

A robust approach has been developed which facilitates interpretation of such data. This involves scoring growth in wells where cells have been plated at a variety of densities and in a variety of media conditions. Growth is scored in each well as 2 (good), 1 (limited) or 0 (no growth), and averaged across all wells of each density/condition combination. Heat maps are then drawn indicating the growth of cells across a variety of conditions, from which the most suitable can easily be discerned.

Figure 2. A robust approach to assess conditions suitable for generation of clonal cell populations for any cell line. Heat map indicates the growth of cells under 13 different media conditions and 14 cell densities. Numbers between 0.5-1.5 indicate limited growth, whereas above 1.5 indicate good growth. For this cell line, condition 7 was selected for use during targeting.

Assessing copy number of the genetic locus of interest in a cell line

Depending on the genome editing method used, copy number of the genetic locus of interest can affect targeting rate. For example, a high copy number would increase the difficulty of achieving a full knock-out using CRISPR based methods. In addition, up front knowledge of the copy number of the genetic locus ensures accuracy in prediction of genotype, and also aids deconvolution of genotyping data following targeting. Use of multiple methods of copy number analysis in parallel increases confidence in results.

Figure 3. Copy number of a genetic locus of interest can be assessed by different methods. (A) Affymetrix Genome-Wide Human SNP Array 6.0 data for a cell line can be analysed using the PICNIC algorithm1 to generate genome wide copy number data (green line). Plots are generated for each individual chromosome with position along the chromosome on the x axis, and copy number on the y axis. (B) Specific copy number of a genetic locus of interest can also be assessed using Droplet Digital PCR. Here a TaqManTM CNV assay for gene X was used in combination with an RNaseP reference assay, using a Bio-Rad QX100 Droplet Digital PCR system.

Assessing ability to deliver a variety of targeting reagents to a cell line

In order to target a cell line, the reagents required for genome editing (usually DNA, RNA or virus) must be able to be effectively delivered into cells. The percentage efficacy of delivery can also inform experimental details for targeting - for example, low transfection rate may demand a method of enrichment for transfected cells, such as use of an antibiotic. In addition, the effect of reagent delivery on cell viability can impact on feasibility of targeting by a particular method.

Assessment of ability to deliver targeting reagents into a cell line, can, in the case of DNA, RNA or virus, be assessed using a fluorescent read-out such as GFP. Cell viability can be monitored by eye, or by using a fluorescent viability marker.

Figure 4. Assessment of delivery of targeting reagents by fluorescent read-outs. Delivery of DNA, RNA or virus into cells can be monitored by a fluorescent read-out, provided each encodes expression of a fluorescent protein such as GFP. Read-out can be via fluorescent microscopy of negative control (A) and test (B) populations. Light microscopic images can also be used to gain an understanding of cell viability. Alternatively, flow cytometry can be used to quantify level of delivery in negative control (C) and test (D) populations. Quantification of cell viability using a fluorescent viability marker is also possible by such a method (data not shown).

  • A variety of assessments can be used to analyse suitability of a cell line for genome editing
  • Comparison of data from all of these assessments for a range of cell lines allows informed choices to be made regarding the most suitable cell line or the most appropriate targeting method to be used
Cell line Cell line 1 Cell line 2 Cell line 3 Cell line 4

Gene copy number (SNP6.0)





Gene copy number (ddPCR)







No growth



Infection test





Transfection Test





  • Investing time to characterize cell lines prior to commencing targeting can save significant resource downstream

Optimisation of cell culture parameters for gene-editing can have a significant impact on the amount of time at bench required downstream, as well as the likelihood of recovering a targeted clone.

If you're just getting started with this process check out downloadable protocols such as our single cell dilution protocol below, or visit our gene-editing resources page.

Download the original poster version of this data

  1. Greenman et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 11(1), 164-175 (2010)