Characterizing Cell-Free Transcription and Translation Dynamics with Nucleic Acid–Based Assays

Characterization of cell-free expression (CFE) systems must expand beyond single spectrophotometric measurements of a green fluorescent protein to provide meaningful metrics of system performance during a CFE reaction and enable the development of predictable and reproducible CFE technologies. To date, comprehensive characterization of these systems has posed a formidable measurement challenge, as it requires time-course measurements of reactions involving endogenous components in addition to transcription and translation of a target genetic circuit added exogenously to the CFE reaction. To provide more informative characterization that is still easy to conduct and complements current practices, we demonstrate a measurement framework for transcription and translation dynamics. We use different nucleic acid templates to characterize a suite of Escherichia coli extracts prepared in-house, as well as extracts and reconstituted systems available commercially. Notably, we include measurements of low-performing systems to assess the sensitivity of our measurement framework and elucidate metrics indicative of system performance. For all these CFE systems, we compute reaction metrics to enable quantitative comparison. We believe this is an accessible measurement framework that can complement existing characterization, provide informative data for developing CFE technologies, and be adopted for routine characterization.

无细胞表达（cell-free expression, CFE）系统的表征不能仅局限于针对绿色荧光蛋白（green fluorescent protein）的单一分光光度测量，而应拓展至可反映CFE反应进程中系统性能的有效指标，从而支撑可预测、可重复的CFE技术研发。迄今为止，对这类系统开展全面表征仍面临极具挑战性的测量难题：其不仅需要对反应进行时序动态监测，还需同时分析CFE反应中外源添加的目标基因回路（genetic circuit）的转录与翻译过程，以及体系内的内源性组分。为构建信息含量更丰富、易于实施且可补充现有表征方案的分析方法，我们提出了一种可用于表征转录与翻译动态过程的测量框架。我们借助不同的核酸模板，对实验室自制的一系列大肠杆菌（Escherichia coli）提取物，以及商业化可得的提取物与重构体系（reconstituted systems）完成了表征。值得关注的是，我们纳入了对低性能系统的测量，以评估该测量框架的灵敏度，并阐明可反映系统性能的核心指标。针对所有上述CFE系统，我们均计算了相关反应指标以实现定量对比。我们认为，该测量框架具备普适易用性，可作为现有表征手段的有效补充，为CFE技术的开发提供高信息价值的数据，并可被应用于常规的系统表征工作中。