Preliminary clinical performance of a Cas13a-based lateral flow assay for detecting Neisseria gonorrhoeae in urine specimens.

Abstract

Nucleic acid amplification testing (NAAT) for N. gonorrhoeae is unavailable in resource-limited settings. We previously developed a CRISPR-based lateral flow assay for detecting N. gonorrhoeae. We aimed to pair that assay with point-of-care DNA extraction, assess performance in clinical urine specimens, and optimize assay kinetics. We collected urine specimens among men presenting with urethritis enrolling in a clinical trial at the Massachusetts General Hospital Sexual Health Clinic. We assessed the quantified DNA yield of detergent-based extractions with and without heat. We selected one detergent for extracting all specimens, paired with isothermal recombinase polymerase amplification for 90 minutes and lateral flow Cas13a detection, interpreted via pixel intensity analysis. We also trained a smartphone-based machine-learning model on 1,008 images to classify lateral flow results. We used the model to interpret lateral flow results from the clinical specimens. We also tested a modified amplification chemistry with a second forward primer lacking the T7-promoter to accelerate reaction kinetics. Extraction with 0.02% Triton X resulted in an average DNA yield of 2.6 × 10⁶ copies/µL (SD ± 6.7 × 10⁵). We treated 40 urine specimens (n = 12 positive) with 0.02% Triton X, and using quantified pixel intensity analysis, the Cas13a-based assay correctly classified all specimens (100% agreement; 95% CI 91.2%-100%). The machine-learning model correctly classified 45/45 strips in the validation data set and all 40 lateral flow strips from clinical specimens. Including the second forward primer reduced incubation time to 60 minutes. Using point-of-care DNA extraction, our Cas13a-based lateral flow N. gonorrhoeae assay demonstrated promising performance among clinical urine specimens.IMPORTANCEUsing a CRISPR-based assay we previously developed for Neisseria gonorrhoeae detection, we developed new techniques to facilitate point-of-care use. We then demonstrated the promising performance of that assay in clinical specimens. Furthermore, we developed a smartphone-based machine learning application for assisting interpretation of lateral flow strip results. Such an assay has the potential to transform the care of sexually transmitted infections in low-resource settings where diagnostic tests are unavailable. A point-of-care pathogen-specific assay, paired with the connectivity offered by a smartphone application, can also support public health surveillance efforts in such areas.