Blums Karlis, Herzog Josha, Costa Jonathan, Quirico Lara, Turber Jonas, Weuster-Botz Dirk
Biochemical Engineering, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching, Germany.
Microorganisms. 2025 Apr 8;13(4):849. doi: 10.3390/microorganisms13040849.
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.
加速生物工艺开发的一个有效策略是采用自动化方法来补充并行生物反应器系统，这通常通过液体处理工作站来实现。此类高通量实验的益处取决于所采用的监测程序。为了在小型化并行一次性生物反应器中从分子层面了解微生物生产菌株，我们将在线监测程序扩展至采用RNA测序的并行转录组分析。为了进行自动化RNA测序实验，我们开发了一种样品制备工作流程，包括通过酶解细胞裂解进行在线细胞破碎、总RNA提取、核酸浓度归一化以及纳米孔cDNA文库制备。在11毫升规模上，使用集成到液体处理工作站中的24个并行搅拌罐生物反应器，研究了在六种不同碳源（葡萄糖、丙酮酸、果糖、半乳糖、蔗糖和甘露糖）上的pH控制好氧分批培养，同时在同一平台上进行用于RNA测序的在线样品制备。在每个条件下进行四次生物学重复，在11.5小时内制备了24个cDNA文库。离线纳米孔测序产生了2097万个分类读数，Q分数>9。差异基因表达分析显示，将葡萄糖培养（指数生长）与丙酮酸培养（应激条件）进行比较时，转录组图谱存在显著差异，从而鉴定出674个下调基因和709个上调基因。将葡萄糖培养与果糖培养进行比较时，基因表达模式变化不显著，仅产生64个差异表达基因。本研究中确定的细胞反应预期差异显示了一种用于生物反应器培养转录组分析的有前景的方法，以高通量方式在线提供了分子水平上的宝贵见解。
