Revealing minor subpopulations in mixed infections with targeted sequencing | LC26

Abstract
Recent advances in long-read sequencing enable high-resolution analysis of genetic diversity. Nanopore sequencing provides real-time, high-depth analysis of targeted loci, allowing detection of low-frequency variants that are difficult to resolve. When combined with targeted sequencing and tailored bioinformatic analysis, this capability enables identification of minor subpopulations within complex samples. In tuberculosis (TB), such minor subpopulations may carry drug-resistance mutations that remain undetected but can expand during treatment. Heteroresistance underscores the need for approaches that overcome slow culture-based methods and insufficient sequencing depth, which can mask clinically relevant resistant subpopulations within a single patient sample. Here, we applied targeted nanopore sequencing together with a bioinformatic pipeline designed to resolve minor resistant subpopulations. We benchmarked the pipeline across four nanopore sequencing approaches: standard whole-genome sequencing (n=21), Adaptive Sampling (n=21), amplicon sequencing in a sputum matrix (n=21), and amplicon sequencing from culture (n=21), using synthetic mixtures of drug-resistant and drug-susceptible strains spanning 1 to 100% resistant allele frequencies. Sequencing was performed on R10 Flow Cells with Dorado super-accuracy basecalling and variant calling optimised for nanopore data. This enabled reliable detection of resistant subpopulations down to 1% allele frequency, with increased depth at resistance-associated loci. Application of the workflow to pulmonary TB sputum samples (n=90) using direct nanopore amplicon sequencing revealed micro-heteroresistance and mixed infections that were absent or substantially attenuated after culture. Overall, targeted nanopore sequencing combined with tailored bioinformatic analysis enables sensitive, scalable, and culture-free detection of low-frequency Mycobacterium tuberculosis (Mtb) subpopulations, supporting improved TB diagnosis and surveillance.

Biography
Carla Apaza is an early-career researcher in bioinformatics focused on applying omics technologies to the study of infectious and tropical diseases, particularly tuberculosis. Her work centres on genomic, epigenomic, and transcriptomic analyses, including the development and application of nanopore-based sequencing and computational workflows to characterise genetic diversity, antimicrobial resistance, and within-host heterogeneity directly from clinical samples. Carla is based at Universidad Peruana Cayetano Heredia in Lima, Peru.