Step-by-Step Guide for Analyzing CRISPR Editing Results with ICE

Determine Editing Efficiency and Types of Edits with Low-Cost Sanger Sequencing

ICE uses Sanger sequencing data to produce quantitative, NGS-quality analysis of CRISPR editing, enabling a ~100-fold reduction in cost relative to NGS-based amplicon sequencing.

To use the tool, simply upload your Sanger sequencing files, either one at a time or as a batch of hundreds, enter your guide RNA (gRNA) sequence, and select the nuclease you used in your CRISPR experiment. The ICE tool will calculate the overall editing efficiency, and determine the profiles and relative abundances of all of the different types of edits present in the sample.

Analyze Complex CRISPR Edits

Traditional Sanger sequencing-based analysis tools cannot detect or analyze complex CRISPR edits, such as those generated by delivering multiple gRNAs to the cells simultaneously, or edits created using non-SpCas9 nucleases. We addressed this in the ICE algorithm by enabling the analysis of edits resulting from multiple gRNA targets and from a curated list of nucleases like SpCas9, hfCas12Max, Cas12a, and MAD7. The tool also includes visual representations of all detected edit types in the sample.

Once the CRISPR components are delivered into target cells, they should result in desired genomic edits - to maximize your CRISPR editing potential, see our troubleshooting sections in our protocols for assistance. To verify the CRISPR components performed genomic editing, researchers often resort to affordable Sanger sequencing methods. Preparing DNA samples for sequencing is an important process in itself, which involves primer design, extraction of genomic DNA, and PCR amplification. Download our detailed Genotyping Protocol below for more sample preparation and Sanger sequencing recommendations.

After sequencing, researchers can upload the Sanger sequence files, gRNA target sequence, and select the nuclease used in their experiments into the ICE software. The tool identifies the percentage of the target genomic sequence that has been successfully modified with insertions or deletions (indels), and then characterizes the sequence and abundance of each particular indel.

Once the analysis is complete, a new screen will appear with a visualization of the results and a list of the analyzed samples. If the analysis is completed without issues, the analysis window will show a green checked circle before the sample name. If there was a minor error during processing, the window will show a yellow checked circle. If there are no results or there was a processing error, you will see a red exclamation point in front of that sample. You can hover over the yellow or red circles for details about any issues associated with each sample. Refer to our ICE Knockout and Knock-In Analysis Protocols for further details on those issues and how to resolve them in their respective Troubleshooting sections.

Successfully analyzed samples will display the following parameters:

• Sample – The unique label name that you provided for each sample.
• Guide Target – This is the user-defined nucleotide sequence of the DNA-targeting region of the gRNA, excluding the PAM sequence.
• PAM Sequence – The Protospacer Adjacent Motif (PAM) sequence for the nuclease used. ICE is currently configured for SpCas9, hfCas12Max, Cas12a, and MAD7.
• Indel Percentage – The editing efficiency (percentage of the edited sample with non-wild type sequence) is determined by comparing the edited trace to the control trace.
• Model Fit (R²) Score – When the ICE linear regression is computed during the generation of the ICE Score, the Pearson correlation coefficient (r) is also computed and reported. The higher the R² value, the more confident you can be in the ICE score.
• Knockout Score – The proportion of cells with either a frameshift or 21+ bp indel. This score is a useful measure for those who are interested in understanding how many of the contributing indels are likely to result in a functional Knockout (KO) of the targeted gene.
• Knock-in Score – The proportion of sequences with the desired knock-in edit.

The analysis can be sorted by any of the parameters in the summary table. You can also use your browser’s “Control+F” (or “Command+F” on a Mac) functionality to search for a particular sequence or name. You can perform more in-depth analyses on each sample by clicking on the sample name or on its corresponding bar graph entry. Clicking to initiate the in-depth analysis will open up a new window with four tabs. Each of the four tabs - traces, discord & indel, contributions, alignment - provides particular details about the indel profile of the edited sample.

You can download the entire analysis as a ZIP file by clicking the “Download Analysis Data” button on the bottom right of the analysis screen.

Using the ICE tool for knockout data analysis is quick and easy! Simply upload your Sanger sequencing files for edited and control samples, gRNA sequence, select the appropriate nuclease from the dropdown menu, and ICE will do the rest. There are no parameters that need optimizing and no complicated steps to learn.

The software can analyze indels resulting from individual or multiple gRNA cleavage events by SpCas9, hfCas12Max, Cas12a, and MAD7. For increased flexibility and scalability, the ICE software has two analysis formats: “sample by sample” analysis, which we recommend for analyzing up to five editing experiments at a time, and “batch” analysis, which compares hundreds of samples simultaneously.

ICE outputs metrics to judge editing efficacy and the quality of sequencing data. The primary metric of success for knockout edits is the Knockout Score (Knockout-Score or KO Score), which represents the proportion of cells that have either a frameshift or 21+ bp indel. This score is useful for those who are interested in understanding how many of the contributing indels are likely to result in a functional knockout of the targeted gene. The R² value (Model Fit) indicates how well the sequencing data follows a predicted model for indel distribution. Detailed results are displayed across multiple tabs that allow visualization of contributions, indels, and traces. Following your KO ICE analysis assessment, we strongly recommend performing protein assessments like western blots or flow cytometry to validate your protein expression levels.

Check out this video for a step-by-step guide to analyzing CRISPR knockouts with ICE.

The process for analyzing knock-in data with the ICE tool is similar to the process for knockout analysis. Upload your Sanger sequencing files for edited and control samples, gRNA sequence, and donor sequence (up to 300 bp), select your nuclease from the dropdown menu, and complete your ICE analysis.

The Knock-in Score (KI Score), a measure of the proportion of sequences with the desired knock-in edit, is the key measure of knock-in edit success. Similar to knockout analysis, the results are displayed across multiple tabs that allow visualization of contributions, indels, and traces. After completing your KI ICE analysis assessment, it is best practice to perform functional assays specific to your KI edit to validate that the insertion was successful.

For a detailed walk-through of CRISPR knock-in analysis using ICE, please view the video below.

ICE generates NGS-quality CRISPR editing analysis from Sanger sequencing data. Furthermore, ICE can analyze more types of editing experiments than other Sanger sequencing-based software tools and is also faster and easier to use. Explore how other researchers are using ICE in their CRISPR experiments by selecting ‘ICE CRISPR Analysis Tool’ under PRODUCT on our CRISPR publication webpage. Read more details about the ICE tool in this article published in The CRISPR Journal.

We invite you to try ICE today and let us know how it works for you! Please contact us by easily submitting a Technical Support Ticket with your technical or scientific questions about ICE or your CRISPR experiment.