Abstract
Abstract
Rapid and accurate diagnostics are essential for controlling emerging infectious diseases and antimicrobial resistance, yet CRISPR assays with exceptional analytical sensitivity often remain difficult to translate into deployable tests. Existing reviews have commonly organized CRISPR diagnostics by Cas effector, biosensor format, or application, offering limited guidance on how design decisions interact across complete workflows or why promising systems remain at the proof-of-concept stage. We introduce an integrated framework that connects five coupled design layers with five levels of translational evidence. The design layers comprise guide RNA engineering, signal amplification circuits, molecular adaptors, computational design support, and specimen and intended-use implementation. The evidence levels progress from purified-target characterization and spiked-matrix testing to retrospective specimen evaluation, prospective integrated-workflow evaluation, and intended-use operation by target users supported by controls, usability assessment, real-time stability, manufacturing consistency, cost per valid result, and claim-specific regulatory preparation. This dual framework shows how improvements at one layer can shift the dominant bottleneck to another. For example, preamplification can lower the analytical limit of detection while increasing contamination risk and workflow burden, whereas guide designs that improve sequence discrimination may reduce cognate-target activation. The framework supports bottleneck-guided selection of effectors, signal-gain strategies, adaptors, computational tools, and multiplexing formats according to target type and abundance, specimen matrix, intended decision, target users, and deployment setting. High analytical sensitivity or single-nucleotide discrimination under controlled conditions does not establish clinical utility unless such performance improves classification at the intended decision threshold within the complete workflow. Translation requires standardized benchmarking, matrix-aware validation, appropriate controls, stability and manufacturability assessment, prospective intended-use studies, and economic evaluation suited to the intended claim and setting. This framework shifts the evaluation of CRISPR diagnostics from isolated analytical performance toward evidence-based workflow co-design.