Shifting to Oxford Nanopore for Clinical 16S rRNA Gene Sequencing

  • Home
  • Metagenomics
  • Shifting to Oxford Nanopore for Clinical 16S rRNA Gene Sequencing

Trish Simner is the Director of Bacteriology and Infectious Disease Sequencing Laboratories. They work at the Johns Hopkins University School of Medicine in Baltimore, Maryland. They presented at London Calling 2024. The session’s title was “Bringing Nanopore Sequencing into the Clinical Microbiology Setting with Targeted Approaches.” Simner spoke about the advantages of long reads and quick turnarounds thanks to Oxford Nanopore Technologies (ONT) and automation. Some limitations are higher error rate, large data storage requirements, and updates. Simner explained how 16S rRNA gene sequencing is shifting from Sanger to ONT. The clinical labs tested ONT using the Zymo Quick -DNA Fungal/Bacterial miniprep and 16S barcoding kit 1-24. They compared the ONT 16S rRNA gene sequencing to the standard ~500 bp Sanger sequencing from the boil lysis DNA. SmartGene App and report were used for identification. With ONT, they covered the entire gene and obtained higher taxonomic resolution at the genus level. A maximum of 6,000 reads was required to create a stable consensus, sometimes taking a day to generate. The team has since automated the extraction with same-day results. With 24 indices, the team saw cost savings compared to Sanger sequencing. Next, Simner spoke about targeted 16S rRNA and ITS sequencing from sterile fluids. The results were initially not great, and the team decided to amplify 16S and ITS along with controls. Interestingly, the team evaluated Flongle flow cells. Limit-of-detection studies and extraction controls were included. Several case studies were explained, including culture-negative pleural fluid analysis. Increased host response reduced the ability to detect organisms. Simner concluded by highlighting the potential of ONT for clinical diagnosis. It is currently an option for when the standard of care is unrevealing.

How can ONT improve bacterial identification in clinical lab settings? AI-generated image.