Quantitative profiling of bacterial communities via full length 16S rRNA gene sequencing with internal controls: optimization and validation across diverse human microbiomes

Accurate estimation of the microbial load is crucial for diagnosing infections and guiding treatment decisions. While traditional culture methods are informative, they are limited by their inability to grow all organisms. Next-generation sequencing offers a more comprehensive alternative for identifying and quantifying microbial communities. This study explored the application of full-length 16S rRNA gene sequencing for bacterial quantification by incorporating internal controls. We optimized full-length16S rRNA gene sequencing using nanopore technology, on commercially available mock community standards. We varied DNA input, PCR cycles, and spike-in proportions. The method was then validated using human samples from the stool, saliva, nose, and skin, and a spike-in control for quantification. Community profiling was done with Emu. Emu performed well at providing genus and species-level resolution. The use of spike-in provided robust quantification across varying DNA inputs and sample origin. However, challenges remained in detecting low-abundance taxa and differentiating closely related species. Human samples with varying microbial loads showed high concordance between sequencing estimates and culture methods. These findings demonstrate that full-length 16S rRNA gene sequencing, combined with spike-ins, offers a reliable and scalable approach for microbial quantification. The method’s performance across diverse human microbiomes supports its potential use in clinical diagnostics where bacterial identification and load estimation are critical. However, further refinement is needed to address limitations in detecting low-abundance and closely related species.