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The original version of this Comment contained errors in the legend of Figure 2, in which the locations of the fifteenth and sixteenth GBA members were incorrectly given as '(15) Australian Genome Foundry, Macquarie University; (16) Australian Foundry for Advanced Biomanufacturing, University of Queensland.'. The correct version replaces this with '(15) Australian Foundry for Advanced Biomanufacturing (AusFAB), University of Queensland and (16) Australian Genome Foundry, Macquarie University'. This has been corrected in both the PDF and HTML versions of the Comment.

Biofoundries provide an integrated infrastructure to enable the rapid design, construction, and testing of genetically reprogrammed organisms for biotechnology applications and research. Many biofoundries are being built and a Global Biofoundry Alliance has recently been established to coordinate activities worldwide.

Detecting alterations in plasmid structures is often performed using conventional molecular biology. However, these methods are laborious and time-consuming for studying the conditions inducing these mutations, which prevent real-time access to cell heterogeneity during bioproduction. In this work, we propose combining both flow cytometry and fluorescence-activated cell sorting, integrated with mechanistic modelling to study conditions that lead to plasmid recombination using a limonene-producing microbial system as a case study. A gene encoding GFP was introduced downstream of the key enzymes involved in limonene biosynthesis to enable real-time kinetics monitoring and the identification of cell heterogeneity according to microscopic and flow cytometric analyses. Three different plasmid configurations (one correct and two incorrect) were identified through cell sorting based on subpopulations expressing different levels of GFP at 10 and 50 µM IPTG. Higher limonene production (530 mg/L) and lower subpopulation proportion carrying the incorrect plasmid (12%) were observed for 10 µM IPTG compared to 50 µM IPTG (96 mg/L limonene and more than 70% of cell population carrying the incorrect plasmid, respectively) in 100 mL production culture. We also managed to derive exploratory hypotheses regarding the plasmid recombination region using the model and successfully validated them experimentally. Additionally, the results also showed that limonene production was proportional to GFP fluorescence intensity. This correlation could serve as an alternative to using biosensors for a high-throughput screening process. The developed method enables rapid identification of plasmid recombination at single-cell level and correlates the heterogeneity with bioproduction performance. Key points: • Strategy to study plasmid recombination during bioproduction. • Different plasmid structures can be identified and monitored by flow cytometry. • Mathematical modelling suggests specific alterations in plasmid structures. [ABSTRACT FROM AUTHOR]

Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π–π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction. [ABSTRACT FROM AUTHOR]