CRISPR Biosensing for Environmental Monitoring: Workflow Design and Performance Benchmarking

CRISPR-based biosensing has rapidly emerged as a promising platform for environmental monitoring due to its high specificity, programmability, and compatibility with portable readouts. However, translation from biomedical diagnostics to environmental matrices remains challenging because of diverse sample types, complex inhibitors, and the breadth of biological and chemical targets. This Review provides a comprehensive analysis of CRISPR-based sensing technologies tailored for environmental contaminant detection, spanning both biological and chemical targets. We systematically evaluate published studies across target classes, Cas effectors, recognition mediators, sample matrices, pretreatment strategies, preamplification or signal-gain approaches, readout modalities, and reported performance metrics. To support practical implementation, we summarize a five-step experimental framework for environmental CRISPR sensing. We then propose a decision-guided design flowchart that links monitoring goals and matrix constraints to the selection of effectors, mediator-enabled transduction routes, pretreatment modules, amplification strategies, readouts, and validation controls. We further benchmark reported detection limits by normalizing units and comparing trends across preamplification-aided versus preamplification-free designs and by contextualizing performance against relevant regulatory or guideline thresholds when available. Across the literature, most studies rely on spiked-matrix validation, highlighting the need for broader nonspiked real environmental sample testing and more transparent reporting of sampling, pretreatment, and performance evaluation. Finally, we advocate standardized data reporting, including consistent units, workflow metadata, and matrix-matched validation, to enable cross-study comparison and accelerate the deployment of CRISPR-based sensors for real-world environmental monitoring.