Dataset Information

Monitoring and external control of pH in microfluidic droplets during microbial culturing.

ABSTRACT: BACKGROUND:Cell-based experimentation in microfluidic droplets is becoming increasingly popular among biotechnologists and microbiologists, since inherent characteristics of droplets allow high throughput at low cost and space investment. The range of applications for droplet assays is expanding from single cell analysis toward complex cell-cell incubation and interaction studies. As a result of cellular metabolism in these setups, relevant physicochemical alterations frequently occur before functional assays are conducted. However, to use droplets as truly miniaturized bioreactors, parameters like pH and oxygen availability should be controlled similar to large-scale fermentation to ensure reliable research. RESULTS:Here, we introduce a comprehensive strategy to monitor and control pH for large droplet populations during long-term incubation. We show the correlation of fluorescence intensity of 6-carboxyfluorescein and pH in single droplets and entire droplet populations. By taking advantage of inter-droplet transport of pH-mediating molecules, the average pH value of several million droplets is simultaneously adjusted in an a priori defined direction. To demonstrate the need of pH control in practice, we compared the fermentation profiles of two E. coli strains, a K12-strain and a B-strain, in unbuffered medium with 5 g/L glucose for standard 1 L bioreactors and 180 pL droplets. In both fermentation formats, the commonly used B-strain E. coli BL21 is able to consume glucose until depletion and prevent a pH drop, while the growth of the K12-strain E. coli MG1655 is soon inhibited by a low pH caused by its own high acetate production. By regulating the pH during fermentation in droplets with our suggested strategy, we were able to prevent the growth arrest of E. coli MG1655 and obtained an equally high biomass yield as with E. coli BL21. CONCLUSION:We demonstrated a comparable success of pH monitoring and regulation for fermentations in 1 L scale and 180 pL scale for two E. coli strains. This strategy has the potential to improve cell-based experiments for various microbial systems in microfluidic droplets and opens the possibility for new functional assay designs.

SUBMITTER: Tovar M

PROVIDER: S-EPMC6990587 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Monitoring and external control of pH in microfluidic droplets during microbial culturing.

Tovar Miguel M Mahler Lisa L Buchheim Stefanie S Roth Martin M Rosenbaum Miriam A MA

Microbial cell factories 20200129 1

<h4>Background</h4>Cell-based experimentation in microfluidic droplets is becoming increasingly popular among biotechnologists and microbiologists, since inherent characteristics of droplets allow high throughput at low cost and space investment. The range of applications for droplet assays is expanding from single cell analysis toward complex cell-cell incubation and interaction studies. As a result of cellular metabolism in these setups, relevant physicochemical alterations frequently occur be ...[more]

PMID: 31996234

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Project description:Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization.

Dataset Information

Monitoring and external control of pH in microfluidic droplets during microbial culturing.

Publications

Monitoring and external control of pH in microfluidic droplets during microbial culturing.

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