Automation of RNA-Seq Sample Preparation and Miniaturized Parallel Bioreactors Enable High-Throughput Differential Gene Expression Studies.

A powerful strategy to accelerate bioprocess development is to complement parallel bioreactor systems with an automated approach, often achieved using liquid handling stations. The benefit of such high-throughput experiments is determined by the employed monitoring procedures. To gain a molecular understanding of the microbial production strains in miniaturized parallel single-use bioreactors, we extended the at-line monitoring procedures to transcriptome analysis in a parallel approach using RNA-Seq. To perform automated RNA-Seq experiments, we developed a sample preparation workflow consisting of at-line cell disruption by enzymatic cell lysis, total RNA extraction, nucleic acid concentration normalization, and Nanopore cDNA Library preparation. The pH-controlled aerobic batch growth of was studied with six different carbon sources (glucose, pyruvate, fructose, galactose, sucrose, and mannose) on a 11 mL scale using 24 parallel stirred tank bioreactors integrated into a liquid handling station while performing at-line sample preparation for RNA-Seq on the same deck. With four biological replicates per condition, 24 cDNA libraries were prepared over 11.5 h. Off-line Nanopore sequencing yielded 20.97 M classified reads with a Q-score > 9. Differential gene expression analysis revealed significant differences in transcriptomic profiles when comparing growth with glucose (exponential growth) to growth with pyruvate (stress conditions), allowing identification of 674 downregulated and 709 upregulated genes. Insignificant changes in gene expression patterns were measured when comparing growth with glucose and fructose, yielding only 64 differentially expressed genes. The expected differences in cellular responses identified in this study show a promising approach for transcriptomic profiling of bioreactor cultures, providing valuable insights on a molecular level at-line in a high-throughput fashion.