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Tuning Mitotic Recombination with Patterned DNA Nicks for Precision Mosaic Analysis

Shen, Y.; Yeung, A. T.; Wang, B.; Ditchfield, P.; Korn, E.; Han, C.

2025-10-31 developmental biology
10.1101/2025.10.30.685647 bioRxiv
Show abstract

CRISPR/Cas9-based mosaic analysis is a powerful tool for in vivo genetics but is limited by cytotoxicity and mutagenesis associated with DNA double-strand breaks (DSBs). Here, we establish Cas9-derived nickases as safer and more reliable alternatives for inducing mitotic recombination in Drosophila. We demonstrate that single-strand nicks are sufficient to generate mosaic clones and systematically dissect the parameters governing this process. We find that clone frequency can be controlled by the gRNA nicking pattern, with two distant nicks on the same DNA strand synergistically enhancing recombination by over nine-fold compared to a single nick. Based on these findings, we propose a mechanistic model for nick-induced crossover and provide a versatile toolkit for generating tissue-specific nickases. This work establishes nickase-based MAGIC as a superior method for high-fidelity clonal analysis, enabling more precise investigation of gene function in development and disease. SIGNIFICANCE STATEMENTThe CRISPR/Cas9-based mosaic technique, MAGIC, is a versatile tool for in vivo biological investigations. However, its reliance on DNA double-strand breaks (DSBs) can cause significant, unintended cell damage. Here we establish that Cas9-derived nickases, which create gentler single-strand nicks, are a superior alternative. We show that nickases safely induce genetic mosaics in Drosophila by avoiding this cellular toxicity. By systematically dissecting the process, we discovered principles of gRNA design that allow clone frequencies to be tuned for different experimental needs. This work provides a new mechanistic model for nick-induced genetic exchange, a high-fidelity "nickase-MAGIC" method, and a versatile toolkit for precision clonal analysis.

Published in Proceedings of the National Academy of Sciences (predicted rank #1) · training set

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