# Optimization of nanopore sequencing for surveillance of antimicrobial resistance in low-resource settings

## TL;DR

This study finds the best settings for using portable nanopore sequencing to track antibiotic resistance in low-resource areas.

## Contribution

The study identifies optimal multiplexing and DNA input levels for cost-effective and reliable nanopore sequencing in AMR surveillance.

## Key findings

- 12- and 24-plex runs with ≤100 ng DNA input achieved high sequencing success (92–100% and 79–82%).
- 36-plex runs and high DNA input significantly reduced sequencing success (as low as 2.8%).
- Lower DNA input (50 ng) improved performance and reduced negative effects of multiplexing.

## Abstract

Whole-genome sequencing (WGS) is emerging as a valuable tool for antimicrobial resistance (AMR) surveillance, yet implementation in low-resource settings remains limited by prohibitory costs and infrastructure constraints. Oxford Nanopore Technologies (ONT) offers portable sequencing platforms that can overcome these barriers, but optimal workflows for bacterial WGS are not fully standardized. We evaluated the impact of multiplexing level (12-, 24-, and 36-plex) and input DNA amounts (50 ng, 100 ng, and 200 ng) on sequencing performance using ONT’s Rapid Barcoding Kit v14 and R10.4.1 flow cells. Sequencing success was defined as assemblies with ≥30 × depth of coverage, complete MLST assignment, and full AMR gene detection. Across nine run configurations, sequencing success was highest for 12-plex runs (92–100% success) and 24-plex runs (79–82% success) when using ≤100 ng DNA input. 36-plex configurations and high DNA input markedly reduced performance (as low as 2.8% success). Lower DNA input (50 ng) did not compromise outcomes and mitigated negative effects of multiplexing. Cost analysis showed per-sample costs decreased with higher multiplexing, but excessive batching compromised data quality. These findings support practical ONT workflows for decentralized AMR surveillance, recommending ≤24 samples per flow cell and ≤100 ng DNA input to balance cost-effectiveness and sequencing success in low-resource laboratory settings.