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<p>While innovative, current CRISPR-Cas9 systems face safety concerns and practical hurdles, notably sequence-independent, noncanonical off-targeting. We demonstrate that the crRNA:tracrRNA duplex in guide RNAs (gRNA) is both splittable and reprogrammable. This property, however, enables endogenous RNAs with crRNA-like sequences to hijack any gRNAs, causing low-frequency yet pervasive tracrRNA-dependent off-target (TDO) effects. Using machine learning trained on high-throughput gRNA variant screens, we derive optimal gRNA-designing rules and engineer crRNA variants mismatched to the human/mouse transcriptomes, thereby minimizing TDO. By leveraging splittability and reprogrammability, we develop reprogrammable tracrRNAs for CRISPRa-based mRNA detection and redesign scaffolds to curb PAM-less Cas9-mediated “self-editing”. We further create a separately expressed gRNA (segRNA) platform featuring split tracrRNAs and non-repetitive tandem crRNAs, enabling multiplexed editing of up to six genes and functional enhancer annotation in stem cells. Our findings uncover a previously overlooked off-target mechanism and offer versatile strategies to enhance the safety and utility of CRISPR systems.</p>

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Guide RNA reprogramming facilitates minimized tracrRNA-dependent off-target and versatile CRISPR/Cas9 engineering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月17日

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  • Wenxia Yu,
  • Jun Chen,
  • Junfan Guo,
  • Fang Yu,
  • Ge Wang,
  • Jianxiang Lin,
  • Xiangping Dai,
  • Xinyi Tan,
  • Peixiang Ma,
  • Ligang Wu,
  • Yu Zhang,
  • Shisheng Huang,
  • Pengfei Lan,
  • Qian Bian,
  • Xingxu Huang,
  • Jiao Wei,
  • Tianlin Cheng,
  • Xiaoguo Zheng,
  • Yunbo Qiao

摘要

While innovative, current CRISPR-Cas9 systems face safety concerns and practical hurdles, notably sequence-independent, noncanonical off-targeting. We demonstrate that the crRNA:tracrRNA duplex in guide RNAs (gRNA) is both splittable and reprogrammable. This property, however, enables endogenous RNAs with crRNA-like sequences to hijack any gRNAs, causing low-frequency yet pervasive tracrRNA-dependent off-target (TDO) effects. Using machine learning trained on high-throughput gRNA variant screens, we derive optimal gRNA-designing rules and engineer crRNA variants mismatched to the human/mouse transcriptomes, thereby minimizing TDO. By leveraging splittability and reprogrammability, we develop reprogrammable tracrRNAs for CRISPRa-based mRNA detection and redesign scaffolds to curb PAM-less Cas9-mediated “self-editing”. We further create a separately expressed gRNA (segRNA) platform featuring split tracrRNAs and non-repetitive tandem crRNAs, enabling multiplexed editing of up to six genes and functional enhancer annotation in stem cells. Our findings uncover a previously overlooked off-target mechanism and offer versatile strategies to enhance the safety and utility of CRISPR systems.