Online monitoring of cell concentration in high cell density  Escherichia coli  cultivations using in situ Microscopy

Marquard, Daniel; Schneider-Barthold, Caroline; Düsterloh, S.; Scheper, Thomas; Lindner, Patrick

doi:10.1016/j.jbiotec.2017.08.003

Cited by 13 publications

(14 citation statements)

References 12 publications

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“…The proper detection of smaller cells like many bacteria remains still a challenge and clearly limits currently ISM for the application in such bioprocesses. Although some studies have investigated the cell concentration of bacteria like Escherichia coli [ 33 ], the determination of the dried biomass concentration was only recently performed on the basis of a grayscale intensity measurement of captures. Single-cell analysis as described for cell line cultivation, algae or yeast has not been conducted yet for bacteria.…”

Section: Discussionmentioning

confidence: 99%

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Marbà-Ardébol

Emmerich²,

Muthig³

et al. 2018

Microb Cell Fact

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BackgroundThe morphology of yeast cells changes during budding, depending on the growth rate and cultivation conditions. A photo-optical microscope was adapted and used to observe such morphological changes of individual cells directly in the cell suspension. In order to obtain statistically representative samples of the population without the influence of sampling, in situ microscopy (ISM) was applied in the different phases of a Saccharomyces cerevisiae batch cultivation. The real-time measurement was performed by coupling a photo-optical probe to an automated image analysis based on a neural network approach.ResultsAutomatic cell recognition and classification of budding and non-budding cells was conducted successfully. Deviations between automated and manual counting were considerably low. A differentiation of growth activity across all process stages of a batch cultivation in complex media became feasible. An increased homogeneity among the population during the growth phase was well observable. At growth retardation, the portion of smaller cells increased due to a reduced bud formation. The maturation state of the cells was monitored by determining the budding index as a ratio between the number of cells, which were detected with buds and the total number of cells. A linear correlation between the budding index as monitored with ISM and the growth rate was found.ConclusionIt is shown that ISM is a meaningful analytical tool, as the budding index can provide valuable information about the growth activity of a yeast cell, e.g. in seed breeding or during any other cultivation process. The determination of the single-cell size and shape distributions provided information on the morphological heterogeneity among the populations. The ability to track changes in cell morphology directly on line enables new perspectives for monitoring and control, both in process development and on a production scale.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-0922-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Marbà-Ardébol

Emmerich²,

Muthig³

et al. 2018

Microb Cell Fact

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“…With this study, we demonstrated the proof of principle that an online sensor array combined with PLS modeling can be used for the prediction of multiple quality parameters during a preparative chromatography run. Other studies dealing with online monitoring of an antibody capture process have been published using only one online sensor . Großhans et al separated an antibody from lysozyme with a cation exchanger and predicted the elution behavior via an FTIR detector yielding an RMSE of prediction of the antibody concentration of 2.42 g L −1 (max.…”

Section: Discussionmentioning

confidence: 99%

“…While in upstream processing many different online monitoring systems have been developed in the past few years, fewer approaches have been published for monitoring in the downstream processing (DSP) of biopharmaceuticals. This might be due to measurement time constraints.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time

Walch

Scharl

Felföldi

et al. 2019

Biotechnology Journal

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Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid‐infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real‐time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time‐ and labor‐intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

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“…In all applications of bioreactor technology, the on-line monitoring of the metabolic activity of microorganisms should be seriously considered to avoid undesired, time-consuming, and cost-intensive interventions, which can reduce the yield at the end of the production chain. Related fields of application include: cell health monitoring in bioreactors [5], continuous non-invasive monitoring of cell growth in disposable bioreactors [6], on-line near-infrared bioreactor monitoring of cell density [7], online monitoring of cell concentration in high-cell density Escherichia coli cultivations [8], sensing metabolites for monitoring the tissue-engineered cellularity in perfusion bioreactors [9], and micro-biosensors for fed-batch fermentation with integrated online monitoring [10]. In all of those examples, analytical sensors are the enabling element for rapid, sensitive, and cost-effective detection of various parameters (e.g., health, growth, and density of cells in bioreactors).…”

Section: Introductionmentioning

confidence: 99%

A LAPS-Based Differential Sensor for Parallelized Metabolism Monitoring of Various Bacteria

Dantism

Röhlen

Wagner

et al. 2019

Sensors

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Monitoring the cellular metabolism of bacteria in (bio)fermentation processes is crucial to control and steer them, and to prevent undesired disturbances linked to metabolically inactive microorganisms. In this context, cell-based biosensors can play an important role to improve the quality and increase the yield of such processes. This work describes the simultaneous analysis of the metabolic behavior of three different types of bacteria by means of a differential light-addressable potentiometric sensor (LAPS) set-up. The study includes Lactobacillus brevis, Corynebacterium glutamicum, and Escherichia coli, which are often applied in fermentation processes in bioreactors. Differential measurements were carried out to compensate undesirable influences such as sensor signal drift, and pH value variation during the measurements. Furthermore, calibration curves of the cellular metabolism were established as a function of the glucose concentration or cell number variation with all three model microorganisms. In this context, simultaneous (bio)sensing with the multi-organism LAPS-based set-up can open new possibilities for a cost-effective, rapid detection of the extracellular acidification of bacteria on a single sensor chip. It can be applied to evaluate the metabolic response of bacteria populations in a (bio)fermentation process, for instance, in the biogas fermentation process.

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Online monitoring of cell concentration in high cell density Escherichia coli cultivations using in situ Microscopy

Cited by 13 publications

References 12 publications

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time

A LAPS-Based Differential Sensor for Parallelized Metabolism Monitoring of Various Bacteria

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