Pathogen to read: a rapid, automated workflow for public health-ready microbial sequencing

Next Generation Sequencing (NGS) based microbial surveillance has become a critical tool for public health initiatives, providing insights into complex interactions within microbial communities. This understanding can be used to facilitate outbreak investigations, pathogen surveillance, and resistance prevention. However, current sample preparation methods are prone to human error and sample bias resulting in skewed Gram +/- coverages and low yields. Methods that have been optimized for non-bias extraction, like mechanical bead beating, are not easily integrated with automation, preventing widespread scalability. Here, we present an innovative, fully automated method that integrates enzymatic lysis, extraction, and library preparation in a single-operator, single-cartridge workflow. The workflow was designed to substantially reduce hands-on time and minimize Gram-stain-related biased extraction.

A capillary-based liquid handler is leveraged to reduce hands-on time from 8 to 10 h to less than 45 min. Using mixed microbial communities, we demonstrated that the workflow consistently produces high-quality sequencing libraries with an average yield of 80.5 ng/µL, average quality score of 33.6, and using the ZymoBIOMICS Microbial Community Standard, a 2.46-fold improvement in Gram-positive representation relative to a standard lysis protocol.

To our knowledge, this is the first demonstration of a fully integrated capillary-based workflow combining enzymatic lysis, extraction, and library preparation compatible with low-throughput platforms. While automated NGS preparation systems exist, they predominantly rely on high-throughput liquid handling robotics, require separate equipment/workflows for extraction and library preparation, or do not support unified workflows for Gram-positive and Gram-negative extraction. The present approach addresses the low-throughput, cost-sensitive, rapid-turnaround requirements of public health and clinical microbiology settings. This proof-of-concept study demonstrates a fully automated workflow for microbial NGS sample preparation, benchmarked on a standardized mock community. This work offers a transformational engineering foundation with the potential to increase efficiency and reproducibility of pathogen genomics workflows, enabling faster outbreak detection and more comprehensive surveillance programs.