(1180-A) Auto-tRNAseq: Driving throughput in tRNA library generation with firefly®

Abstract: Transfer RNAs (tRNAs) are the critical adaptor molecule that translates genetic information into functional protein sequences. While the genome defines a static set of tRNA gene sequences, the functional tRNA pool depends on the expression and aminoacylation (charging) levels of different tRNA species, as well as many modifications that fine-tune tRNA activity. This dynamically regulated tRNA pools dictate the fidelity of the translation process and proteome integrity of a cell. High-resolution sequencing-based methods for tRNA sequencing have provided transformative insights into the physiological impact of tRNA expression, modification, and aminoacylation levels on cellular stress and/or disease conditions while large scale genome engineering efforts have also employed tRNA sequencing to recapitulate the translational state of engineered cells. However, tRNA sequencing has not been readily adopted in research across the globe due to the time-consuming, arduous, and specialized library generation workflows for tRNA sequencing. Additionally, the workflows rely on multiple gel purification steps that incorporate experimental variability and limit experimental throughput. These limitations make the library generation for tRNA sequencing a desirable workflow to automate.

For the first time, we outline an automated library generation workflow for tRNA sequencing on the firefly® automation platform. SPT Labtech’s firefly® is a comprehensive and flexible automated platform, bringing together multiple liquid handling technologies that enable high-quality next generation sequencing library workflows. Moreover, the versatility of firefly® with two distinct liquid handling heads is ideal to develop customized bead-based RNA size selection methods to circumvent gel purification techniques. We have prototyped the crucial short RNA ( < 200 nt) enrichment step with consistent final yield from the same starting material of total RNA input with minimal variation across multiple wells. The combination of these technologies enables significant time savings in the laboratory while improving throughput and reducing experimental variation. We estimate that running 96-samples at once on firefly® would result in an 8-fold increase in throughput than when performed manually by a single user, whilst reducing a multi-day workflow to a single day. Additionally, we mitigate the risk of repetitive strain injuries (RSI) posed by such a substantial number of manual pipetting steps. Developing a robust automated library generation workflow for tRNA sequencing will create a reliable workflow to reduce the barrier of entry and scale-up to keep pace with rapid advances in genome engineering and genetic code expansion.