# Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors
## 6 Applications of RNA-Based Sensors for Intracellular Metabolites  
Sensors for intracellular metabolites have proved their utility in metabolic engineering. The construction of multi-step pathways requires screening enzymatic activities from multiple sources, including enzyme libraries or biological samples from different origins. Quantification of a specific enzyme-product yield is usually performed by analytical methods, such as HPLC, which is time-consuming and only allows for low to medium throughput. Using an RNA-based sensor for this specific product may expedite the identification of a specific enzymatic activity in biological samples or the variant with the highest activity in large enzyme libraries [102]. [PERSON] et al. utilized a well-characterized fluorescent aptazyme-based theophylline sensor to set up an in vivo high throughput screening of enzyme variants in yeast. Here, a caffeine de-methylase library generated by error-prone PCR was transformed into a strain that had the sensor integrated into the genome. A few consecutive cycles of positive and negative selection with FACS led to the identification of an enzyme variant with increased activity and selectivity [103]. [PERSON] et al. utilized the light-up c-di-AMP sensor (Table 1) to screen for the activity of putative diadenylate cyclases co-expressed heterologously in _E. coli_. With this approach, they experimentally validated two predicted diadenylate cyclases from the human pathogen _Clostridium difficile_ and the archaea _Methanocaldococcus jannaschii_[77]. The enzymes necessary to generate a product of interest are assembled into a synthetic pathway to create a consolidated cell factory, which will still require further optimization. Cell factory optimization entails substantial metabolic engineering, and along this process, the host strain may accumulate mutations, from which only a subset of them contribute to higher product yields, the rest being superfluous or counterproductive. [PERSON] et al. constructed a whole-cell aptazyme FMN sensor to screen for vitamin B2-secreting cell factories. The authors generated a library of 9000 clones in which a number of DNA segments had been crossed over with wild-type host strain, such that in each clone, a number of the accumulated mutations had been reverted. Individual clones were then co-cultivated with the sensor strain in nanoliter reactor capsules, and B12 producers were selected by FACS [104]. This work benchmarks the utility of RNA-based sensors for the efficient selection of producers after genetic modifications performed during cell factory optimization (e.g., for balancing the expression levels of involved enzymes and modifying cell host metabolism). Finally, along the course of industrial fermentations, cell factories typically lose productivity due to the appearance of sub-optimal phenotypes. The fermentation process pursues a high product yield, which is usually in conflict with the selection pressure for an increased growth rate of clones within the cell factory population [105,106]. RNA-based sensors could be used to monitor the appearance of such phenotypic heterogeneity. [PERSON] et al. combined five fluorescent biosensors to monitor the yeast intracellular environment along the fermentation process [107]. These included the aptazyme FBP sensor that reports glycolytic flux (Table 1) [62], along with protein sensors of intracellular pH, ATP levels, oxidative stress, and ribosome production. These biosensors inform about yeast physiology and stress at the single-cell level, providing new means for tracking and controlling the rise of sub-populations. In this regard, the modular architecture of RNA-based sensors that actuate gene expression (e.g., aptazyme) allows coupling a specific functionality (other than fluorescence emission) to a metabolite concentration. Thus, it might be possible to control cell fate in response to a metabolite concentration by replacing the reporter gene. For instance, [PERSON] et al.