Developing an Automated System for Yeast Culture Cultivation and Control using Flow Cytometry

Abstract

Flow cytometry is a versatile tool for monitoring a microbial population at a single-cell level. Multiple parameters can be monitored based on the users’ requirements through the utilization of different dyes, biosensors etc., and the use of FCM for on-line measurements during fed-batch cultivations has the potential to be used for precise process control.
This project consisted of the development and testing of a Python program for on-line bioprocess regulation based on flow cytometry data. Specifically, the program was built to process FCS files resulting from at-line measurement of pentose-fermenting S. cerevisiae using the OnCyt autosampler connected to a BD Accuri C6+ flow cytometer. The program was designed to automatically start and stop a peristaltic pump through serial communication, by issuing commands based on changes within the microbial population.
The yeast strain used was the TMBRP011 strain, which was previously engineered for bioethanol production from pentose sugars and carries a previously developed redox biosensor. The biosensor reacts to cellular redox imbalance caused by inhibitory substances released during lignocellulose pretreatments through an increase in fluorescence. The program was designed to achieve higher volumes of inhibitory substances as the cells acclimatized to a set rate, indicated with a decrease in fluorescence. However, since these inhibitory substances are toxic in nature, the pump should stop pumping if the percentage of PI-stained cells within a sample increased, indicating cell membrane damage.
The program was successful in regulating a fed-batch process based on the prementioned parameters, and it managed to reinduce the cells by injecting higher volumes of inhibitory compounds per hour. The program was developed in a way that fulfilled the initial scope in terms of functionality and provides a roadmap for implementing on-line FCM as a basis for process control.

Popular Abstract

This project had the aim of developing a Python program capable of controlling a pump containing a toxic substrate based on on-line analysis of a yeast cultivation on a single-cell level. Yeast cultivation is used within a number of industries, including foods, pharmaceuticals, and fuels. The program was specifically developed with a yeast strain used for bioethanol production from lignocellulose in mind, which has a partial resistance to this toxic substrate.
The program was developed to be used for process control by monitoring the trends of two separate statistics which indicated cell viability, and induction level due to the stress caused by the toxic substrate respectively. The program was designed to start the pump when the induction level decreased, which for the specific yeast-strain used indicated an adaptation to the rate of the toxic substance present in a constant feed, the separate pump would then inject a higher concentration of the toxic substance to further induce the cells as they addapt. The reason for this was that a higher induction level had previously been linked to a higher production rate of bioethanol when using the same yeast strain.
Cell viability was monitored and a decrease in this statistic would indicate that the culture was not able to handle the amount of the toxic substrate injected without cell damage, which is why the increase of the volume of the toxic substrate injected into the culture was designed to be gradual. Should this statistic decrease, the pump was designed to stop and allow the cells to acclimatize further.
This project was furthermore done to automize a cultivation process through single cell monitoring by using flow cytometry data. This is a relatively novel concept that has not been applied previously. Basing process control on flow cytometry data enables many options for monitoring that is not available traditionally, as it is currently done on a culture-wide basis with less precise tools. This method could introduce more precise control systems and enable the use of specifically genetically engineered yeast strains for large-scale processes, without the need for additional analysis steps. Automation as a whole is also needed within the life-science space to enable researchers to develop more elaborate experiments that are less laborious, while also reducing sources of error.
The program was created as planned and was able to cause a reinduction of the yeast culture automatically without user input during the experimental phase, based on induction level and cell viability. The amount of toxic substance that could be inserted into the culture increased by over 120% with the program as opposed to without, and these results proved reproducible. The outcome of this thesis provides a roadmap for the implementation of single cell monitoring as a basis for bioprocess control.

• author: Magnusson, Sara
• supervisor: Magnus Carlquist; Raquel Perruca Foncillas; Marie Gorwa Grauslund; Ola Wallberg
• organization: Applied Microbiology
• course: KMBM05 20232
• year: 2023
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering; Biology and Life Sciences
• keywords: Yeast, flow cytometry, applied microbiology
• language: English
• id: 9139429
• date added to LUP: 2024-03-25 09:26:44
• date last changed: 2024-03-25 09:26:44