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

作者：Lao‐Tzu Allan‐Blitz, Gordon Adams, Gabriela Sanders, Palak Shah, Kumar Ramesh, Jana Jarolimova, Kevin L. Ard, John A. Branda, Jeffrey D. Klausner, Pardis C. Sabeti, Jacob E. Lemieux · 发表于：mSphere · 年份：2024 · DOI：10.1128/msphere.00677-24 · 被引用次数：7 · 研究领域：Reproductive tract infections research、Syphilis Diagnosis and Treatment、Bacterial Infections and Vaccines

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 × 10 6 copies/µL (SD ± 6.7 × 10 5 ). 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 st...