Dynamic crRNA reconfiguration enhances nucleic acid detection sensitivity and specificity in diagnostics, indicating a new method is available.
Current one-pot CRISPR diagnostics necessitate meticulous control of nucleic acid hybridization parameters or extensive modification of CRISPR components to achieve complete enzymatic silencing, a fundamental bottleneck limiting their robustness and generalizability. Here, we resolve this challenge through dynamic crRNA reconfiguration, a paradigm-shifting strategy that exploits the intrinsic structural duality of CRISPR RNA. We present CONVERT (CRISPR One-Pot Nucleic acid detection Via Engineered crRNA Reconfiguration Technology), a universal platform where nontarget intact crRNA acts as a universal suppressor, achieving complete Cas12a inhibition during RPA (recombinase polymerase amplification) by irreversible enzyme sequestration. Target detection is initiated through programmable conversion to split crRNA activators, bypassing sequence-specific design constraints entirely. This crRNA conformational switching mechanism is implemented via photocleavable linker cleavage and subsequent assembly with presupplied truncated crRNA (tcrRNA), activating trans-cleavage signal amplification. Three transformative advances emerge: First, the endogenous crRNA engineering eliminates dependency on exogenous nucleic acid blockers or chemical modifications, reducing optimization costs and time. Second, near-total enzymatic suppression ensures zero cross-talk between amplification and detection phases. Third, the unified workflow enables contamination-resistant operation through spatiotemporal control, requiring no physical partitioning. Validated for Enterocytozoon bieneusi detection, the system demonstrates attomolar sensitivity of 1 aM, 100% diagnostic concordance with qPCR, and absolute specificity against related pathogens. By effectively decoupling CRISPR regulation from sequence-context constraints through rational crRNA structural plasticity, this work establishes a generalized framework for next-generation molecular diagnostics.
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Zhuang et al. (2025) studied this question.
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