# Techniques for Validating CRISPR Changes Using RNA-Sequencing Data

## TL;DR

This paper shows how RNA-sequencing can reveal unexpected changes caused by CRISPR gene editing that DNA-based methods miss.

## Contribution

The study introduces RNA-seq-based techniques to detect unintended CRISPR effects like fusions and exon skipping.

## Key findings

- RNA-seq identified inter-chromosomal fusions and exon skipping not seen with DNA methods.
- Unintended transcriptional changes in neighboring genes were detected in CRISPR experiments.
- Guidelines are provided for using RNA-seq to validate CRISPR modifications comprehensively.

## Abstract

The use of CRISPR to knockdown or knockout genes is a powerful tool for understanding the specific role of a gene in disease development. However, it can cause many unanticipated changes to the transcriptome that are not detected by DNA amplification and Sanger sequencing of the target site. Various RNA-sequencing techniques can be used to identify these changes and effectively gauge the full impact of the CRISPR knockout, thereby providing a means of selecting appropriate clones for further experimentation. Background/Objectives: RNA-seq data from 4 CRISPR knockout experiments were analyzed and techniques developed to both confirm the success of the CRISPR modifications and identify potential issues. Methods: A broad-based analysis of RNA-sequencing data identified many CRISPR-based changes not identified by PCR amplification of DNA around the CRISPR target site. These changes included an inter-chromosomal fusion event, exon skipping, chromosomal truncation, and the unintentional transcriptional modification and amplification of a neighboring gene. Conclusions: The inadvertent modifications identified by the evaluation of 4 CRISPR experiments highlight the value of using RNA-seq to identify transcriptional changes to cells altered by CRISPR, many of which cannot be recognized by evaluating DNA alone. Specific guidelines are presented for designing and analyzing CRISPR experiments using RNA-seq data.