Ideal methods for detecting pathogens should be sensitive, specific, rapid, cost-effective and instrument-free. Conventional nucleic acid pathogen detection strategies, mostly PCR-based techniques, have various limitations, such as expensive equipment, reagents and skilled performance. Recently, CRISPR/Cas-based methods have burst onto the scene, with the potential to power the pathogen detection field. Here we introduce these unique methods and discuss its hurdles and promises.

Cas12a‐based systems, which detect specific nucleic acids via collateral cleavage of reporter DNA, display huge potentials for rapid diagnosis of infectious diseases. Here, the Manganese‐enhanced Cas12a (MeCas12a) system is described, where manganese is used to increase the detection sensitivity up to 13‐fold, enabling the detection of target RNAs as low as five copies. MeCas12a is also highly specific, and is able to distinguish between single nucleotide polymorphisms (SNPs) differing by a single nucleotide. MeCas12a can detect severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in clinical samples and distinguish between SARS‐CoV‐2 and Middle East respiratory syndrome coronavirus (MERS‐CoV) RNA in simulated samples, thus offering an attractive alternative to other methods for the diagnosis of infectious diseases including COVID‐19 and MERS.

Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a‐based detection system has emerged as a promising solution for on‐site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a‐based detection remains challenging and time‐consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a‐mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a‐based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web‐based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a‐based detection, which could serve as a valuable tool for clinical diagnostics and research applications.

Genetic testing is crucial for precision cancer medicine. However, detecting multiple same-site insertions or deletions (indels) is challenging. Here, we introduce CoHIT (Cas12a-based One-for-all High-speed Isothermal Test), a one-pot CRISPR-based assay for indel detection. Leveraging an engineered AsCas12a protein variant with high mismatch tolerance and broad PAM scope, CoHIT can use a single crRNA to detect multiple NPM1 gene c.863_864 4-bp insertions in acute myeloid leukemia (AML). After optimizing multiple parameters, CoHIT achieves a detection limit of 0.01% and rapid results within 30 minutes, without wild-type cross-reactivity. It successfully identifies NPM1 mutations in 30 out of 108 AML patients and demonstrates potential in monitoring minimal residual disease (MRD) through continuous sample analysis from three patients. The CoHIT method is also competent for detecting indels of KIT, BRAF, and EGFR genes. Integration with lateral flow test strips and microfluidic chips highlights CoHIT’s adaptability and multiplexing capability, promising significant advancements in clinical cancer diagnostics.