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In situ microscopy as online tool for detecting microbial contaminations in cell culture
Journal of Biotechnology 296 (2019) 53–60
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Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec
In situ microscopy as online tool for detecting microbial contaminations in cell culture
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R. Gustavssona, C.F. Mandeniusa, , S. Löfgrena, T. Scheperb, P. Lindnerb a b
Division of Biotechnology, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden Institute of Technical Chemistry, Leibniz University Hannover, 30167, Hanover, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Image analysis Mammalian cell culture Infection Hybridoma cells Candida Pichia
Microbial contamination in mammalian cell cultures causing rejected batches is costly and highly unwanted. Most methods for detecting a contamination are time-consuming and require extensive off-line sampling. To circumvent these efforts and provide a more convenient alternative, we used an online in situ microscope to estimate the cell diameter of the cellular species in the culture to distinguish mammalian cells from microbial cells depending on their size. A warning system was set up to alert the operator if microbial cells were present in the culture. Hybridoma cells were cultured and infected with either Candida utilis or Pichia stipitis as contaminant. The warning system could successfully detect the introduced contamination and alert the operator. The results suggest that in situ microscopy could be used as an efficient online tool for early detection of contaminations in cell cultures.
1. Introduction Contamination of mammalian cell cultures remains a major problem in bioprocess development and production causing losses in time, money and efforts. The consequence is estimated to cost cell culture users millions of dollars annually in the US alone (Ryan, 1998). Contamination occurs when unwanted microbial or higher eukaryotic cells enter the culture and could result in inhibited growth of the desired cell line and could also complicate identification of cell lines (Capes-Davis et al., 2010; Langdon, 2004). A frequently occurring problem is contamination by mycoplasma, which have been reported in up to 20% of cell cultures (Drexler and Uphoff, 2002; Mirjalili et al., 2005). Mycoplasma is hard to detect in a cell culture due to its small size (0.3–0.8 μm in diameter) but, on the other hand, does not cause problems such as reduced growth and poor cell adherence (Langdon, 2004). Contaminations of bacteria or yeasts are also problems although detection by cloudiness, pH and color changes of the media is easier, but first at a rather late stage of the contamination. To confirm an infection of mycoplasma or other microbial contaminations at an early stage, special kits or assays are available (Degeling et al., 2002; Uphoff and Drexler, 2011; Wehbe et al., 2018). However, offline sampling and substantial laboratory procedures are required for these assays as well as other methods, such as microscopic plate-culture identification and counting or using polymerase chain ⁎
reaction-based methods. Sometimes these methods require days before a result is delivered. Online detection of contaminations is therefore highly desirable. Attempts to use out gas analysis and observing shifts in carbon production rate have been applied (Aehle et al., 2001), which possibly could be applied for recognizing a disturbance in growth caused by contaminating species. Bacterial contamination has also been shown to be possible to detect online in the outlet gas using gas sensor arrays (Bachinger et al., 2002; Namdev et al.,1998) and by analyzing responses from electrode arrays with chemometric methods, so called electronic noses and tongues (Heras et al., 2010). Another online method with high potential for distinguishing contamination is in situ microscopy. It has the advantage of measuring noninvasively chemical and biotechnological processes due to the barrier of an optical window (Bittner et al., 1998; Brückerhoff, 2006; Camisard et al., 2002; Frerichs, 2000; Joeris et al., 2002; Suhr et al., 1995). The measurement system consists of an in situ microscope (ISM), a control software for the device and image analysis software (Lindner, 2006) that allows the automatic analysis of large amount of images that are typically generated during an experiment. The ISM setup allows monitoring in real-time of critical process parameters, such as particle number, particle size and particle morphology and has been used in a large variety of chemical and biological processes. Examples comprise monitoring of degradation of enzyme carriers, hydrolysis of cellulose or monitoring of crystallization using particle image analyzers (PIA) and
Corresponding author. E-mail address: [email protected] (C.F. Mandenius).
https://doi.org/10.1016/j.jbiotec.2019.03.011 Received 20 June 2018; Received in revised form 14 March 2019; Accepted 15 March 2019 Available online 18 March 2019 0168-1656/ © 2019 Elsevier B.V. All rights reserved.
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aeration of 1,5 L/min air and 1 L/min CO2. A scaled-up experiment was carried out in a 10 L in situ sterilized bioreactor (Model LMS 2002, Belach Bioteknik AB, Solna, Sweden) equipped with standard instrumentations. Temperature was controlled at 37 °C, stirring at 100 rpm, aeration at 1,5 L/min air and the pH at 7.2 by addition of carbon dioxide. Cell concentration was measured online using a dielectric probe (Standard Futura, Aber Instruments Ltd, Aberystwyth, UK)
particle vision microscopes (IVM) (Opitz et al., 2013; Prediger et al., 2011; Qu et al., 2006). However, the main applications for in situ microscopy have been in the field of biotechnological processes. For example, ISMs have been used successfully to monitor cultivations with CHO cells (Lüder et al., 2014), fibroblasts (Rudolph et al., 2008), hybridoma cells (Wiedemann et al., 2011), algae (Havlik et al., 2013), Pichia pastoris (Marquard et al., 2016) and E. coli (Marquard et al., 2017). Another in situ technique for online particle size measurement with potential for detection of contaminations, is the focused beam reflectance measurement (FBRM) spectroscopy. FBRM is based on measuring the backscattering laser light in the sample from which the particle size is calculated from a chord length distribution (Höpfner et al., 2010). FBRM has been applied to monitoring of the biomass concentration (Whelan et al., 2012) and yeast flocs in ethanol fermentations (Ge et al., 2005). The purpose of this study was to investigate if the ISM, in combination with a custom-made image processing algorithm, could be used as an on-line sensor to reliably detect contaminations in a cell culture at an early stage for alerting a process operator at the manufacturing plant. Contamination was limited to yeast due to the particle size required to be detected with the ISM.
2.3. In situ microscopy For the monitoring of all experiments an ISM of type III-XTF (Sartorius, Göttingen, Germany) was utilized (Bluma et al., 2010; Rudolph et al., 2008). This microscope was designed to be mounted to a bioreactor via a 25 mm port. In this case it was mounted using a custom-made spinner flask 25 mm connector. The cultivation broth can flow through a height-adjustable measurement zone which is separated from the reactor contents with two sapphire glass windows. The measurement zone is illuminated by an ultra-bright LED emitting at a wavelength of 505 nm. Images were acquired using a monochrome CCD camera of type Sony XCD SX-910. The image acquisition was done in cycles. At the start of the experiment the first cycle starts immediately with the image acquisition part of the cycle, that is, 1 image is taken every 10 seconds to generate 10 images per cycle (total image acquisition time is 100 s). Then the acquisition is paused for 20 min (minus the time for image acquisition) and the next acquisition cycle starts using the same sequence of operations as before. 72 of those cycles were made during a typical experiment. The image processing results for all images within a cycle were averaged to achieve a more statistically robust measure of cell count and size (see section below for details about the image processing). Thus, the whole procedure results in 72 data points for a cultivation with new data generated every 20 min. In the scaled-up experiment in the 10-L bioreactor, 1 image was taken every 2 s to generate 100 images per cycle with new data generated every 30 min. Data processing and communication were carried out by three software platforms: In Situ Control and Graphic Analyzer (developed and tailored to the ISM instrument by TCI, Hannover, Germany), and Mathematica (Wolfram Inc., USA).
2. Materials and methods 2.1. Cell culture and media Hybridoma cell line HB8696 (ATCC) was cultured in DMEM 25 mM glucose and 4 mM glutamine (Hyclone) with additives of 5% Fetal bovine serum (Gibco) 1% Pencillin/Streptamycin (Lonza), 1% MEM NEAA 100X (Hyclone). The inoculum was cultured in spinner flasks placed in an incubator at 37 °C and 10% CO2 level. Candida utilis CCUG 28,186 and Pichia stipitis CCUG 18,492 (Culture Collection, University of Gothenburg, Sweden) was used as contaminant. The strain was grown in shake flasks with 200 mL YP Broth (Sigma Aldrich) overnight at 30 °C and 200 rpm. 2.2. Cultivation Initial experiments were performed in a custom-made 1 L stainless steel bioreactor with a working volume of 500 mL. Due to the introduction of contamination, these experiments were not performed under optimal conditions in an incubator. The experiments were performed at room temperature (20 °C) with a stirring of 70 rpm and an
Fig. 1. The software controls the ISM device and automatically generates images of the cell in the measurement zone. Grayscale images are then transferred to and analyzed by the imaging software. Cells are identified, and their properties are then presented in Mathematica on a graphic user interface so that the operator can surveil and estimation cell concentration and be alerted if a contamination has occurred. The results could then be used for process control purpose via OPC. 54
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pixel as well as in μm²), compactness, eccentricity and others can be computed. The auto-thresholding is an iterative procedure that starts with T = 255, decreases T by 1 in each step and stops if the amount of image objects of pixel size 1 exceeds 50. At very high T only the bright kernels of the defocused cells show up in white in the binary images and thus can form an image object. With T approaching the image background value, tiny 1-pixel objects of that background form image objects as well. The stopping criterion of 50 such objects was optimized manually from test images used during image algorithm development. The reasoning of this procedure is, that the overall brightness of the image may change during an experiment (cell growth, cultivation broth turbidity, etc.) and thus the optimal threshold for separating cells and background pixels does as well. By the abovementioned operation an optimal threshold T opt can be computed that allows the reliable and robust separation of cell and background pixels which could not be achieved by other methods that were tested during algorithm development (Otsu thresholding, SIS thresholding, and others). In the next step (2C) all image objects are filtered by size, keeping only those, that are larger than 5 pixels. The next step (2D) is called dark ring analysis and was included to reduce the amount of mis-detected image objects (non-cellular particles or noise artifacts falsely evaluated as a cell) and increase the overall robustness of the algorithm. It is based on the assumption that a cell image object has a bright spot in the middle and a dark ring around it. This is the result of image acquisition in a defocused way. So, all image objects showing that property are those that must be analyzed further and all image objects that are in fact bright enough to remain after auto-thresholding, but do not have the dark ring, must be discarded. The dark ring analysis is done by drawing 16 line segments from the center to the outside of each image object. By evaluation of the gray values in the original image (2A) along each segment, it can be determined, if there is a dark ring present in that direction. In Fig. 2D those line segments are shown in green. Line segments for which no dark ring was found, are shown in red. If more than ¾ of the line segments of an object have the dark ring property, the image object itself is treated as having that property. 2E shows the image objects that remain after this step. All remaining image objects now are (i) small contaminant cells, (ii) hybridoma cells or (iii) cell clusters. The term cell cluster here means, cells that are in facts physically connected to each other as well as cells
3. Results and discussion 3.1. Image analysis To enable efficient online presentation of the ISM data a communication setup based on three software platforms was developed as shown in Fig. 1. All device operations (measurement zone height and focus settings, automatic image acquisition, etc.) were controlled by the ISM-tailored software (“In situ Control”) which had these operational functions. The images generated by the control software were then evaluated by a separate software (“Graphic Analyzer”) which had the capacity to carry out essential computations for interpreting the images and estimating the results (cell size, cell number, cell concentration). These data were then transferred online via a TCP/IP network connection to a graphic user interface created in the software Mathematica. The results could then be monitored online and alerts generated if contaminations have occurred. The option for online process control is also made possible via Open Platform Communications (OPC). For the automatic evaluation of the images, a custom-made image processing algorithm was developed. The input it receives are grayscale images showing hybridoma and contaminant cells that were acquired in a defocused fashion. That means, the focus setting of the ISM was (on purpose) adjusted such, that all cells are slightly out-of-focus. In this way cells show up with a very bright kernel and a dark ring around them (see Figs. 2 and 6). This is because spherical objects like cells, if lit from the background (as is done by the ISM), can act as a collecting lens making the center parts of the objects especially bright. It is assumed, that if there are non-cellular particles present in the cultivation broth, they do not show this effect and are therefore easily distinguishable from the cells. The image acquisition in a defocused fashion thus facilitates image analysis. The image analysis algorithm operates in 6 steps, as shown in Fig. 2. Starting from a grayscale image (2 A) the algorithm generates a binary image (2B) using an auto-thresholding method. Thresholding of a grayscale image at a threshold T ∈ [0, 255] yields a binary image where all pixels with gray values equal or larger than T become white (gray value 255) and all others become black (gray value 0). All collections of white pixels within a Moore neighborhood of each other can then be defined as an image object. For each image object properties like size (in
Fig. 2. The image analysis process: A: Original image, B: Binary image after auto-thresholding, C: Image objects after size filtering, D: Dark ring analysis, E: Objects remaining after ring analysis, F: Results of DBSCAN clustering, G: Processed cell clusters, H: Final resulting image (green objects are cells with a diameter > 4 μm and red objects are cells < 4 μm) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 55
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performed after the experiment. In this study a low number of images was used in each cycle for quick data analysis and to have a small data set to be sent between the software for a more rapid response and presentation online.
that are depicted very close to each other on an image just by coincidence. To robustly distinguish between the 3 image object classes a DBSCAN algorithm (Ester et al., 1996) was applied on a dataset generated from the size in pixel, the compactness and the eccentricity of each image object. In this way image objects that are cell clusters, and need further processing can be identified (2F, see image object highlighted in yellow). Those objects are split into single cell image objects using the Constricted-Site-Algorithm (Rüdinger, 2013). The results of that splitting step are shown in Fig. 2G. As a final step all image objects are categorized into either normal (non-contaminant) and contaminant cell based on a size threshold that is set by the experimenter. The final resulting image is shown in Fig. 2H.
3.3. Contamination with Candida utilis Fig. 4 presents an experiment where C. utilis broth (400,000 cells/ ml) was injected at 3 h to cause a contamination. In Fig. 4A the number of cells with a diameter less than 4 μm increased directly after the contamination occurred and at 6 h the number of cells reaches the threshold for generating an alert to the operator (i.e. at 2 mean counts per image). Fig. 4B presents estimations of cell concentration with the start concentration added to the reactor being 225,000 cells/ml. Samples for cell counting were not taken during the experiments conducted with contamination due to the introduction of contaminant in the sample which one would like to avoid applying in cell culture equipment. The accuracy in cell concentration was here also not adequate due to the low number of data generated in each cycle. The sudden increase in cell concentration estimation could here also be due to the increasing number of C. utilis cells that can range in size above 4 μm (Kockova-Kratochvilova, 1990; Pinheiro et al., 2014) and falsely be identified as hybridoma cells. The warning system was successful in detecting the contamination in an early stage. The injected quantity of C.utilis was rather high since it triggered an immediate response in detected cells smaller than 4 μm in diameter which is probably not the case in a naturally occurring contamination. Therefore, in the next experiment presented in Fig. 5, the amount of contaminant to the culture was reduced to one fifth (80,000 cells/ml) and was injected at 2.7 h. This time with a lower concentration of contaminant it takes about 3 h after contamination until an increase in number of cells smaller than 4 μm in diameter can be detected, presented in Fig. 5A. Although an increase of these cells can be detected early, the threshold value for triggering alerts is reached first after an additional 8 h. Fig. 5B presents estimations of cell concentration were the start concentration added to the reactor was 140,000 cells/ml. In Fig. 6 images from the stages of the experiment presented in Fig. 4 are shown: (A) Before contamination with two hybridoma cells present, (B) after contamination when cells < 4 μm is detected (C) at a
3.2. Reference experiment As stated, contamination is highly unwanted in cell cultures, so initial experiments were not performed with equipment normally used for cell cultures due to the risk of contaminating other cultures. Hence the experiments were not conducted in optimal conditions for cell growth but adequate for keeping cells viable during the experiments. First an experiment without the introduction of contaminant was performed, Fig. 3. In Fig. 3A, cells smaller than 4 μm in diameter are at a low level throughout the experiment but are however detected although no contamination had been introduced. This motivates the threshold for alert signal to be more than just one single cell as mean in each image since this is frequently occurring. In Fig. 3B cell concentration estimation, i.e. cells greater than 4 μm in diameter multiplied with a correlation factor is presented. Samples for cell counting were taken at 0 h (125,000 cells/ml) and 25 h (220,000 cells/ml). The relatively low accuracy and high variability in the estimation is most likely due to the low number of images in each cycle. Wiedemann et al. (2011) demonstrated high accuracy for hybridoma cells using a much more frequent data acquisition while Joeris et al. (2002) used a low number of images for analyzing CHO cells. A more frequent acquisition cycle with a higher number of images in each cycle would most likely have resulted in a lower variability in the results for cell concentration presented in Fig. 3B. In more recent studies (Lüder et al., 2014; Marquard et al., 2016) up to 300 images in each cycle have been used for high accuracy with data analysis however these analyses were
Fig. 3. Experiment conducted without contamination. Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows estimated concentration of cells with a diameter > 4 μm (●). 56
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Fig. 4. Contamination with C. utilis at 3 h (↓). Panel A shows the average number of cells with a diameter < 4 μm in (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●).
3.4. Contamination with other organisms
later stage where alert signal is triggered due to highly developed contamination. The measurement zone of the ISM is adjusted so that only a monolayer of hybridoma cells is measured meaning that all cells passing the measurement zone can be detected. The smaller C. utilis could however pass the measurement zone in different layers were not all cells are detected hence estimations of cell concentration for these cells are not calculated. This also means, cells regarded as contaminant cells could be present in the culture but need to pass in the right focal plane in the measurement zone to be detected.
In another ISM experiment the yeast Pichia stipitis was used to induce contamination of the hybridoma culture in the bioreactor. P. stipitis (100,000 cells/ml) was injected at 2.3 h, presented in Fig. 7. Fig. 7A depicts the mean number of cells smaller than 4 μm in diameter where an increase can be detected at 17 h, much later than in the previous experiments with C. utilis. The start concentration of hybridoma cells was 100,000 cells/ml and in Fig. 7B the estimated cell concentration by the ISM is presented. In this study the contaminations of the hybridoma culture were performed with the yeast strains C. utilis and P. stipitis. However,
Fig. 5. Contamination with C. utilis at 2.7 h (↓). Panel A shows the average number of cells with a diameter < 4 μm in (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●). 57
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Fig. 6. Images from the stages in experiment presented in Fig. 4. Black arrows mark hybridoma cells and white arrows the contamination cells. At 3 h (A), the cycle just before contamination. At 3.7 h (B), cells with diameter < 4 μm are detected. At 7 h (C), multiple cells with diameter < 4 μm are detected with average above threshold triggering alert to operator.
Fig. 7. Contamination with P. stipitis at 2.3 h (↓). Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B the estimated concentration of cells with diameter > 4 μm (●).
200,000 cells/mL and at 41 h the culture was contaminated by C. utilis added through the reactor port (80,000 cells/mL). The first alert to the operator was triggered at 50 h, which was followed by a rapid increase of cells smaller than 4 μm in diameter (Fig. 8A). When a microbial cell contaminates a mammalian cell culture, an increased oxygen demand and change in pH would be expected due to the faster growth rate of the microbial cells. In Fig. 9A, a change in pH can be observed at 52 h and in Fig. 9B the dissolved oxygen starts to drop at 48 h. The drop in dissolved oxygen can be explained by the increased oxygen demand of the growing yeast cells but also from the increased addition of carbon dioxide needed to adjust the pH to the set level. The contamination was here successfully detected by the ISM before a drop in pH could be observed and although the dissolved oxygen starts to decrease earlier, the ISM confirmed the contamination before the dissolved oxygen reaches a low level. The ISM could estimate the cell concentration (Fig. 8B) which also was confirmed by the online estimate of the dielectric probe (Fig. 9C). The cell concentration estimated by the ISM had good correlation with the dielectric probe estimation for the first 36 h. A sudden drop can be seen before the contamination, which could not have caused the drop. After the contamination the dielectric probe results are biased by the yeast cells which probably also explained the
bacterial contamination in mammalian cell cultures are sometimes also a severe problem. ISM experiments were therefore also done with Escherichia coli as contaminant (data not shown). Unfortunately, these smaller bacterial cells could not be detected with the current resolution of the ISM instrument. Although the highest magnification of the ISM was used, the E. coli cells did not depict as separate objects and thus could not be counted. Recent studies with a high-resolution ISM, however, indicate detectability of E. coli broth turbidity (Marquard et al., 2017). Thus, ISM detection of E. coli contamination seems feasible, if the alert is based on turbidity threshold limit. Mycoplasmas on the other hand, would most likely, due to its even smaller size (of 0.3–0.8 μm), be difficult to detect with the ISM technique. 3.5. Contamination with Candida utilis in bioprocess Contamination experiments with C. utilis using the ISM were also carried out in a scaled-up fully equipped bioreactor under optimal conditions (Fig. 8). Online measurements in this experimental setup are presented in Fig. 9. The number of images generated in each cycle was increased to 100 images to achieve a statistically more robust measure of cell count and cell size. The bioreactor was inoculated with 58
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Fig. 8. Contamination by C. utilis at 41 h (↓). Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●).
rapid increase in estimates (Fig. 9C) and which coincides with the increase in number of cells (Fig. 8A). 4. Conclusion This study has demonstrated the feasibility of using ISM as a realtime warning system for altering process operations of microbial batch contaminations in a bio-manufacturing plant producing proteins in a mammalian cell culture. Using an in situ microscope (ISM) in combination with a custom-made image processing algorithm, it is possible to reliably detect online a contaminating yeast species that invades the cell culture. The ISM measurement system could also conveniently be linked and integrated into a conventional process control system for directly alerting the process operator or sending signals automatically to actuators or other devices for control purposes. The network load on the communication network was in the initial experiments insignificant. By only utilizing 10 images every 20 min, a contamination (or its absence) could be detected even at an early stage of the cultivation. However, with a more frequent acquisition of images from the ISM by increasing the number of acquired images, the accuracy and information density of the ISM measurement system was further improved. Implemented in a bioprocess the ISM could successfully detect a contamination and alert the operator even before this could be seen in the online data of the process. If an operator would suspect a contamination from shifts in online data, this would still require an offline sample to confirm this. Thus, the results indicate that the estimation of cell concentration, both of target cells and contaminants, can be enhanced. For example, the calculation from the cell count on an image depends on (1) the field of the angular view of the ISM objective and (2) the height of the measurement zone (the flow-through space) of the ISM. The geometry of these can be adjusted in the design of the ISM. Also, due to mechanical clearance of the ISM components a limited accuracy of determining the measurement zone height reduces the cell concentration accuracy. With such improvements of the ISM design it is foreseeable that the online ISM technique has a promising prospect for online contamination control. However, additional testing and validation of the ISM at manufacturing scale and conditions are necessary, which would be the next step in adapting the ISM as an infection guard in bio-
Fig. 9. Contamination with C. utilis at 41 h. Panel A shows the pH during the process. In Panel B the level of dissolved oxygen is presented Panel C shows cell concentration estimate with the dielectric probe.
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industrial applications.
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Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Acknowledgements We thank Patrik Ekenberg for software communication development and Jenny Joensuu for valuable technical contributions. The authors also acknowledge funding from the Technical Faculty of Linköping University and from German Federal Ministry of Education and Research (BMBF-project: Knowledge-based process intelligence, BMBF-Bioeconomy) References Aehle, M., Kuprijanov, A., Schaepe, S., Simutis, R., Lübbert, A., 2001. Simplified off-gas analyses in animal cell cultures for process monitoring and control purposes. Biotechnol. Lett. 33, 2103–2110. https://doi.org/10.1007/s10529-011-0686-5. Bachinger, T., Riese, U., Eriksson, R., Mandenius, C.F., 2002. Gas sensor arrays for early detection of infection in mammalian cell culture. Biosens. Bioelectron. 17, 395–403. https://doi.org/10.1016/S0956-5663(01)00315-3. Bittner, C., Wehnert, G., Scheper, T., 1998. In situ microscopy for on-line determination of biomass. Biotechnol. Bioeng. 60, 24–35. Bluma, A., Höpfner, T., Lindner, P., Rehbock, C., Beutel, S., Riechers, D., Hitzmann, B., Scheper, T., 2010. In-situ imaging sensors for bioprocess monitoring: state of the art. Anal. Bioanal. Chem. 398, 2429–2438. https://doi.org/10.1007/s00216-010-4181-y. Brückerhoff, T., 2006. Bildbasiertes Inline-Monitoring von Kultivierungsprozessen mit einem optimierten In-situ Mikroskopsystem. Dissertation. University of Hannover. Camisard, V., Brienne, J.P., Baussart, H., Hammann, J., Suhr, H., 2002. Inline characterization of cell concentration and cell volume in agitated bioreactors using in situ microscopy: application to volume variation induced by osmotic stress. Biotechnol. Bioeng. 78, 73–80. https://doi.org/10.1002/bit.10178. Capes-Davis, A., Theodosopoulos, G., Atkin, I., Drexler, H., Kohara, A., Macleod, R., Masters, J., Nakamura, Y., Reid, Y., Reddel, R., Freshney, I., 2010. Check your cultures! A list of cross-contaminated or misidentified cell lines. Int. J. Cancer 127, 1–8. https://doi.org/10.1002/ijc.25242. Degeling, M.H., Maguire, C.A., Bovenberg, M.S.S., Tannous, B.A., 2002. Sensitive assay for mycoplasma detection in mammalian cell culture. Anal. Chem. 84, 4227–4232. https://doi.org/10.1021/ac2033112. Drexler, H.G., Uphoff, C.C., 2002. Mycoplasma contamination of cell cultures: incidence, sources, effects, detection, elimination, prevention. Cytotechnology 39, 75–90. https://doi.org/10.1023/A:1022913015916. Ester, M., Kriegel, H.P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) 226–231. Frerichs, J.G., 2000. Entwicklung eines in-situ Mikroskops zur bildgestützten OnlineÜberwachung Von Bioprozessen. Dissertation. University of Hannover. Ge, X.M., Zhao, X.Q., Bai, F.W., 2005. Online monitoring and characterization of flocculating yeast cell flocs during continuous ethanol fermentation. Biotechnol. Bioeng. 90, 523–531. https://doi.org/10.1002/bit.20391. Havlik, I., Reardon, K.F., Ünal, M., Lindner, P., Prediger, A., Babitzky, A., Beutel, S., Scheper, T., 2013. Monitoring of microalgal cultivations with on-line, flow-through microscopy. Algal Res. 2, 253–257. https://doi.org/10.1016/j.algal.2013.04.001. Heras, J.Y., Pallarola, D., Battaglini, F., 2010. Electronic tongue for simultaneous detection of endotoxins and other contaminants of microbiological origin. Biosens. Bioelectron. 25, 2470–2476. https://doi.org/10.1016/j.bios.2010.04.004.
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Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec
In situ microscopy as online tool for detecting microbial contaminations in cell culture
T
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R. Gustavssona, C.F. Mandeniusa, , S. Löfgrena, T. Scheperb, P. Lindnerb a b
Division of Biotechnology, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden Institute of Technical Chemistry, Leibniz University Hannover, 30167, Hanover, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Image analysis Mammalian cell culture Infection Hybridoma cells Candida Pichia
Microbial contamination in mammalian cell cultures causing rejected batches is costly and highly unwanted. Most methods for detecting a contamination are time-consuming and require extensive off-line sampling. To circumvent these efforts and provide a more convenient alternative, we used an online in situ microscope to estimate the cell diameter of the cellular species in the culture to distinguish mammalian cells from microbial cells depending on their size. A warning system was set up to alert the operator if microbial cells were present in the culture. Hybridoma cells were cultured and infected with either Candida utilis or Pichia stipitis as contaminant. The warning system could successfully detect the introduced contamination and alert the operator. The results suggest that in situ microscopy could be used as an efficient online tool for early detection of contaminations in cell cultures.
1. Introduction Contamination of mammalian cell cultures remains a major problem in bioprocess development and production causing losses in time, money and efforts. The consequence is estimated to cost cell culture users millions of dollars annually in the US alone (Ryan, 1998). Contamination occurs when unwanted microbial or higher eukaryotic cells enter the culture and could result in inhibited growth of the desired cell line and could also complicate identification of cell lines (Capes-Davis et al., 2010; Langdon, 2004). A frequently occurring problem is contamination by mycoplasma, which have been reported in up to 20% of cell cultures (Drexler and Uphoff, 2002; Mirjalili et al., 2005). Mycoplasma is hard to detect in a cell culture due to its small size (0.3–0.8 μm in diameter) but, on the other hand, does not cause problems such as reduced growth and poor cell adherence (Langdon, 2004). Contaminations of bacteria or yeasts are also problems although detection by cloudiness, pH and color changes of the media is easier, but first at a rather late stage of the contamination. To confirm an infection of mycoplasma or other microbial contaminations at an early stage, special kits or assays are available (Degeling et al., 2002; Uphoff and Drexler, 2011; Wehbe et al., 2018). However, offline sampling and substantial laboratory procedures are required for these assays as well as other methods, such as microscopic plate-culture identification and counting or using polymerase chain ⁎
reaction-based methods. Sometimes these methods require days before a result is delivered. Online detection of contaminations is therefore highly desirable. Attempts to use out gas analysis and observing shifts in carbon production rate have been applied (Aehle et al., 2001), which possibly could be applied for recognizing a disturbance in growth caused by contaminating species. Bacterial contamination has also been shown to be possible to detect online in the outlet gas using gas sensor arrays (Bachinger et al., 2002; Namdev et al.,1998) and by analyzing responses from electrode arrays with chemometric methods, so called electronic noses and tongues (Heras et al., 2010). Another online method with high potential for distinguishing contamination is in situ microscopy. It has the advantage of measuring noninvasively chemical and biotechnological processes due to the barrier of an optical window (Bittner et al., 1998; Brückerhoff, 2006; Camisard et al., 2002; Frerichs, 2000; Joeris et al., 2002; Suhr et al., 1995). The measurement system consists of an in situ microscope (ISM), a control software for the device and image analysis software (Lindner, 2006) that allows the automatic analysis of large amount of images that are typically generated during an experiment. The ISM setup allows monitoring in real-time of critical process parameters, such as particle number, particle size and particle morphology and has been used in a large variety of chemical and biological processes. Examples comprise monitoring of degradation of enzyme carriers, hydrolysis of cellulose or monitoring of crystallization using particle image analyzers (PIA) and
Corresponding author. E-mail address: [email protected] (C.F. Mandenius).
https://doi.org/10.1016/j.jbiotec.2019.03.011 Received 20 June 2018; Received in revised form 14 March 2019; Accepted 15 March 2019 Available online 18 March 2019 0168-1656/ © 2019 Elsevier B.V. All rights reserved.
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aeration of 1,5 L/min air and 1 L/min CO2. A scaled-up experiment was carried out in a 10 L in situ sterilized bioreactor (Model LMS 2002, Belach Bioteknik AB, Solna, Sweden) equipped with standard instrumentations. Temperature was controlled at 37 °C, stirring at 100 rpm, aeration at 1,5 L/min air and the pH at 7.2 by addition of carbon dioxide. Cell concentration was measured online using a dielectric probe (Standard Futura, Aber Instruments Ltd, Aberystwyth, UK)
particle vision microscopes (IVM) (Opitz et al., 2013; Prediger et al., 2011; Qu et al., 2006). However, the main applications for in situ microscopy have been in the field of biotechnological processes. For example, ISMs have been used successfully to monitor cultivations with CHO cells (Lüder et al., 2014), fibroblasts (Rudolph et al., 2008), hybridoma cells (Wiedemann et al., 2011), algae (Havlik et al., 2013), Pichia pastoris (Marquard et al., 2016) and E. coli (Marquard et al., 2017). Another in situ technique for online particle size measurement with potential for detection of contaminations, is the focused beam reflectance measurement (FBRM) spectroscopy. FBRM is based on measuring the backscattering laser light in the sample from which the particle size is calculated from a chord length distribution (Höpfner et al., 2010). FBRM has been applied to monitoring of the biomass concentration (Whelan et al., 2012) and yeast flocs in ethanol fermentations (Ge et al., 2005). The purpose of this study was to investigate if the ISM, in combination with a custom-made image processing algorithm, could be used as an on-line sensor to reliably detect contaminations in a cell culture at an early stage for alerting a process operator at the manufacturing plant. Contamination was limited to yeast due to the particle size required to be detected with the ISM.
2.3. In situ microscopy For the monitoring of all experiments an ISM of type III-XTF (Sartorius, Göttingen, Germany) was utilized (Bluma et al., 2010; Rudolph et al., 2008). This microscope was designed to be mounted to a bioreactor via a 25 mm port. In this case it was mounted using a custom-made spinner flask 25 mm connector. The cultivation broth can flow through a height-adjustable measurement zone which is separated from the reactor contents with two sapphire glass windows. The measurement zone is illuminated by an ultra-bright LED emitting at a wavelength of 505 nm. Images were acquired using a monochrome CCD camera of type Sony XCD SX-910. The image acquisition was done in cycles. At the start of the experiment the first cycle starts immediately with the image acquisition part of the cycle, that is, 1 image is taken every 10 seconds to generate 10 images per cycle (total image acquisition time is 100 s). Then the acquisition is paused for 20 min (minus the time for image acquisition) and the next acquisition cycle starts using the same sequence of operations as before. 72 of those cycles were made during a typical experiment. The image processing results for all images within a cycle were averaged to achieve a more statistically robust measure of cell count and size (see section below for details about the image processing). Thus, the whole procedure results in 72 data points for a cultivation with new data generated every 20 min. In the scaled-up experiment in the 10-L bioreactor, 1 image was taken every 2 s to generate 100 images per cycle with new data generated every 30 min. Data processing and communication were carried out by three software platforms: In Situ Control and Graphic Analyzer (developed and tailored to the ISM instrument by TCI, Hannover, Germany), and Mathematica (Wolfram Inc., USA).
2. Materials and methods 2.1. Cell culture and media Hybridoma cell line HB8696 (ATCC) was cultured in DMEM 25 mM glucose and 4 mM glutamine (Hyclone) with additives of 5% Fetal bovine serum (Gibco) 1% Pencillin/Streptamycin (Lonza), 1% MEM NEAA 100X (Hyclone). The inoculum was cultured in spinner flasks placed in an incubator at 37 °C and 10% CO2 level. Candida utilis CCUG 28,186 and Pichia stipitis CCUG 18,492 (Culture Collection, University of Gothenburg, Sweden) was used as contaminant. The strain was grown in shake flasks with 200 mL YP Broth (Sigma Aldrich) overnight at 30 °C and 200 rpm. 2.2. Cultivation Initial experiments were performed in a custom-made 1 L stainless steel bioreactor with a working volume of 500 mL. Due to the introduction of contamination, these experiments were not performed under optimal conditions in an incubator. The experiments were performed at room temperature (20 °C) with a stirring of 70 rpm and an
Fig. 1. The software controls the ISM device and automatically generates images of the cell in the measurement zone. Grayscale images are then transferred to and analyzed by the imaging software. Cells are identified, and their properties are then presented in Mathematica on a graphic user interface so that the operator can surveil and estimation cell concentration and be alerted if a contamination has occurred. The results could then be used for process control purpose via OPC. 54
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pixel as well as in μm²), compactness, eccentricity and others can be computed. The auto-thresholding is an iterative procedure that starts with T = 255, decreases T by 1 in each step and stops if the amount of image objects of pixel size 1 exceeds 50. At very high T only the bright kernels of the defocused cells show up in white in the binary images and thus can form an image object. With T approaching the image background value, tiny 1-pixel objects of that background form image objects as well. The stopping criterion of 50 such objects was optimized manually from test images used during image algorithm development. The reasoning of this procedure is, that the overall brightness of the image may change during an experiment (cell growth, cultivation broth turbidity, etc.) and thus the optimal threshold for separating cells and background pixels does as well. By the abovementioned operation an optimal threshold T opt can be computed that allows the reliable and robust separation of cell and background pixels which could not be achieved by other methods that were tested during algorithm development (Otsu thresholding, SIS thresholding, and others). In the next step (2C) all image objects are filtered by size, keeping only those, that are larger than 5 pixels. The next step (2D) is called dark ring analysis and was included to reduce the amount of mis-detected image objects (non-cellular particles or noise artifacts falsely evaluated as a cell) and increase the overall robustness of the algorithm. It is based on the assumption that a cell image object has a bright spot in the middle and a dark ring around it. This is the result of image acquisition in a defocused way. So, all image objects showing that property are those that must be analyzed further and all image objects that are in fact bright enough to remain after auto-thresholding, but do not have the dark ring, must be discarded. The dark ring analysis is done by drawing 16 line segments from the center to the outside of each image object. By evaluation of the gray values in the original image (2A) along each segment, it can be determined, if there is a dark ring present in that direction. In Fig. 2D those line segments are shown in green. Line segments for which no dark ring was found, are shown in red. If more than ¾ of the line segments of an object have the dark ring property, the image object itself is treated as having that property. 2E shows the image objects that remain after this step. All remaining image objects now are (i) small contaminant cells, (ii) hybridoma cells or (iii) cell clusters. The term cell cluster here means, cells that are in facts physically connected to each other as well as cells
3. Results and discussion 3.1. Image analysis To enable efficient online presentation of the ISM data a communication setup based on three software platforms was developed as shown in Fig. 1. All device operations (measurement zone height and focus settings, automatic image acquisition, etc.) were controlled by the ISM-tailored software (“In situ Control”) which had these operational functions. The images generated by the control software were then evaluated by a separate software (“Graphic Analyzer”) which had the capacity to carry out essential computations for interpreting the images and estimating the results (cell size, cell number, cell concentration). These data were then transferred online via a TCP/IP network connection to a graphic user interface created in the software Mathematica. The results could then be monitored online and alerts generated if contaminations have occurred. The option for online process control is also made possible via Open Platform Communications (OPC). For the automatic evaluation of the images, a custom-made image processing algorithm was developed. The input it receives are grayscale images showing hybridoma and contaminant cells that were acquired in a defocused fashion. That means, the focus setting of the ISM was (on purpose) adjusted such, that all cells are slightly out-of-focus. In this way cells show up with a very bright kernel and a dark ring around them (see Figs. 2 and 6). This is because spherical objects like cells, if lit from the background (as is done by the ISM), can act as a collecting lens making the center parts of the objects especially bright. It is assumed, that if there are non-cellular particles present in the cultivation broth, they do not show this effect and are therefore easily distinguishable from the cells. The image acquisition in a defocused fashion thus facilitates image analysis. The image analysis algorithm operates in 6 steps, as shown in Fig. 2. Starting from a grayscale image (2 A) the algorithm generates a binary image (2B) using an auto-thresholding method. Thresholding of a grayscale image at a threshold T ∈ [0, 255] yields a binary image where all pixels with gray values equal or larger than T become white (gray value 255) and all others become black (gray value 0). All collections of white pixels within a Moore neighborhood of each other can then be defined as an image object. For each image object properties like size (in
Fig. 2. The image analysis process: A: Original image, B: Binary image after auto-thresholding, C: Image objects after size filtering, D: Dark ring analysis, E: Objects remaining after ring analysis, F: Results of DBSCAN clustering, G: Processed cell clusters, H: Final resulting image (green objects are cells with a diameter > 4 μm and red objects are cells < 4 μm) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 55
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performed after the experiment. In this study a low number of images was used in each cycle for quick data analysis and to have a small data set to be sent between the software for a more rapid response and presentation online.
that are depicted very close to each other on an image just by coincidence. To robustly distinguish between the 3 image object classes a DBSCAN algorithm (Ester et al., 1996) was applied on a dataset generated from the size in pixel, the compactness and the eccentricity of each image object. In this way image objects that are cell clusters, and need further processing can be identified (2F, see image object highlighted in yellow). Those objects are split into single cell image objects using the Constricted-Site-Algorithm (Rüdinger, 2013). The results of that splitting step are shown in Fig. 2G. As a final step all image objects are categorized into either normal (non-contaminant) and contaminant cell based on a size threshold that is set by the experimenter. The final resulting image is shown in Fig. 2H.
3.3. Contamination with Candida utilis Fig. 4 presents an experiment where C. utilis broth (400,000 cells/ ml) was injected at 3 h to cause a contamination. In Fig. 4A the number of cells with a diameter less than 4 μm increased directly after the contamination occurred and at 6 h the number of cells reaches the threshold for generating an alert to the operator (i.e. at 2 mean counts per image). Fig. 4B presents estimations of cell concentration with the start concentration added to the reactor being 225,000 cells/ml. Samples for cell counting were not taken during the experiments conducted with contamination due to the introduction of contaminant in the sample which one would like to avoid applying in cell culture equipment. The accuracy in cell concentration was here also not adequate due to the low number of data generated in each cycle. The sudden increase in cell concentration estimation could here also be due to the increasing number of C. utilis cells that can range in size above 4 μm (Kockova-Kratochvilova, 1990; Pinheiro et al., 2014) and falsely be identified as hybridoma cells. The warning system was successful in detecting the contamination in an early stage. The injected quantity of C.utilis was rather high since it triggered an immediate response in detected cells smaller than 4 μm in diameter which is probably not the case in a naturally occurring contamination. Therefore, in the next experiment presented in Fig. 5, the amount of contaminant to the culture was reduced to one fifth (80,000 cells/ml) and was injected at 2.7 h. This time with a lower concentration of contaminant it takes about 3 h after contamination until an increase in number of cells smaller than 4 μm in diameter can be detected, presented in Fig. 5A. Although an increase of these cells can be detected early, the threshold value for triggering alerts is reached first after an additional 8 h. Fig. 5B presents estimations of cell concentration were the start concentration added to the reactor was 140,000 cells/ml. In Fig. 6 images from the stages of the experiment presented in Fig. 4 are shown: (A) Before contamination with two hybridoma cells present, (B) after contamination when cells < 4 μm is detected (C) at a
3.2. Reference experiment As stated, contamination is highly unwanted in cell cultures, so initial experiments were not performed with equipment normally used for cell cultures due to the risk of contaminating other cultures. Hence the experiments were not conducted in optimal conditions for cell growth but adequate for keeping cells viable during the experiments. First an experiment without the introduction of contaminant was performed, Fig. 3. In Fig. 3A, cells smaller than 4 μm in diameter are at a low level throughout the experiment but are however detected although no contamination had been introduced. This motivates the threshold for alert signal to be more than just one single cell as mean in each image since this is frequently occurring. In Fig. 3B cell concentration estimation, i.e. cells greater than 4 μm in diameter multiplied with a correlation factor is presented. Samples for cell counting were taken at 0 h (125,000 cells/ml) and 25 h (220,000 cells/ml). The relatively low accuracy and high variability in the estimation is most likely due to the low number of images in each cycle. Wiedemann et al. (2011) demonstrated high accuracy for hybridoma cells using a much more frequent data acquisition while Joeris et al. (2002) used a low number of images for analyzing CHO cells. A more frequent acquisition cycle with a higher number of images in each cycle would most likely have resulted in a lower variability in the results for cell concentration presented in Fig. 3B. In more recent studies (Lüder et al., 2014; Marquard et al., 2016) up to 300 images in each cycle have been used for high accuracy with data analysis however these analyses were
Fig. 3. Experiment conducted without contamination. Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows estimated concentration of cells with a diameter > 4 μm (●). 56
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Fig. 4. Contamination with C. utilis at 3 h (↓). Panel A shows the average number of cells with a diameter < 4 μm in (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●).
3.4. Contamination with other organisms
later stage where alert signal is triggered due to highly developed contamination. The measurement zone of the ISM is adjusted so that only a monolayer of hybridoma cells is measured meaning that all cells passing the measurement zone can be detected. The smaller C. utilis could however pass the measurement zone in different layers were not all cells are detected hence estimations of cell concentration for these cells are not calculated. This also means, cells regarded as contaminant cells could be present in the culture but need to pass in the right focal plane in the measurement zone to be detected.
In another ISM experiment the yeast Pichia stipitis was used to induce contamination of the hybridoma culture in the bioreactor. P. stipitis (100,000 cells/ml) was injected at 2.3 h, presented in Fig. 7. Fig. 7A depicts the mean number of cells smaller than 4 μm in diameter where an increase can be detected at 17 h, much later than in the previous experiments with C. utilis. The start concentration of hybridoma cells was 100,000 cells/ml and in Fig. 7B the estimated cell concentration by the ISM is presented. In this study the contaminations of the hybridoma culture were performed with the yeast strains C. utilis and P. stipitis. However,
Fig. 5. Contamination with C. utilis at 2.7 h (↓). Panel A shows the average number of cells with a diameter < 4 μm in (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●). 57
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Fig. 6. Images from the stages in experiment presented in Fig. 4. Black arrows mark hybridoma cells and white arrows the contamination cells. At 3 h (A), the cycle just before contamination. At 3.7 h (B), cells with diameter < 4 μm are detected. At 7 h (C), multiple cells with diameter < 4 μm are detected with average above threshold triggering alert to operator.
Fig. 7. Contamination with P. stipitis at 2.3 h (↓). Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B the estimated concentration of cells with diameter > 4 μm (●).
200,000 cells/mL and at 41 h the culture was contaminated by C. utilis added through the reactor port (80,000 cells/mL). The first alert to the operator was triggered at 50 h, which was followed by a rapid increase of cells smaller than 4 μm in diameter (Fig. 8A). When a microbial cell contaminates a mammalian cell culture, an increased oxygen demand and change in pH would be expected due to the faster growth rate of the microbial cells. In Fig. 9A, a change in pH can be observed at 52 h and in Fig. 9B the dissolved oxygen starts to drop at 48 h. The drop in dissolved oxygen can be explained by the increased oxygen demand of the growing yeast cells but also from the increased addition of carbon dioxide needed to adjust the pH to the set level. The contamination was here successfully detected by the ISM before a drop in pH could be observed and although the dissolved oxygen starts to decrease earlier, the ISM confirmed the contamination before the dissolved oxygen reaches a low level. The ISM could estimate the cell concentration (Fig. 8B) which also was confirmed by the online estimate of the dielectric probe (Fig. 9C). The cell concentration estimated by the ISM had good correlation with the dielectric probe estimation for the first 36 h. A sudden drop can be seen before the contamination, which could not have caused the drop. After the contamination the dielectric probe results are biased by the yeast cells which probably also explained the
bacterial contamination in mammalian cell cultures are sometimes also a severe problem. ISM experiments were therefore also done with Escherichia coli as contaminant (data not shown). Unfortunately, these smaller bacterial cells could not be detected with the current resolution of the ISM instrument. Although the highest magnification of the ISM was used, the E. coli cells did not depict as separate objects and thus could not be counted. Recent studies with a high-resolution ISM, however, indicate detectability of E. coli broth turbidity (Marquard et al., 2017). Thus, ISM detection of E. coli contamination seems feasible, if the alert is based on turbidity threshold limit. Mycoplasmas on the other hand, would most likely, due to its even smaller size (of 0.3–0.8 μm), be difficult to detect with the ISM technique. 3.5. Contamination with Candida utilis in bioprocess Contamination experiments with C. utilis using the ISM were also carried out in a scaled-up fully equipped bioreactor under optimal conditions (Fig. 8). Online measurements in this experimental setup are presented in Fig. 9. The number of images generated in each cycle was increased to 100 images to achieve a statistically more robust measure of cell count and cell size. The bioreactor was inoculated with 58
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Fig. 8. Contamination by C. utilis at 41 h (↓). Panel A shows the average number of cells with a diameter < 4 μm (○) that were detected in each cycle using a threshold value at 2 cells. Panel B shows the estimated concentration of cells with a diameter > 4 μm (●).
rapid increase in estimates (Fig. 9C) and which coincides with the increase in number of cells (Fig. 8A). 4. Conclusion This study has demonstrated the feasibility of using ISM as a realtime warning system for altering process operations of microbial batch contaminations in a bio-manufacturing plant producing proteins in a mammalian cell culture. Using an in situ microscope (ISM) in combination with a custom-made image processing algorithm, it is possible to reliably detect online a contaminating yeast species that invades the cell culture. The ISM measurement system could also conveniently be linked and integrated into a conventional process control system for directly alerting the process operator or sending signals automatically to actuators or other devices for control purposes. The network load on the communication network was in the initial experiments insignificant. By only utilizing 10 images every 20 min, a contamination (or its absence) could be detected even at an early stage of the cultivation. However, with a more frequent acquisition of images from the ISM by increasing the number of acquired images, the accuracy and information density of the ISM measurement system was further improved. Implemented in a bioprocess the ISM could successfully detect a contamination and alert the operator even before this could be seen in the online data of the process. If an operator would suspect a contamination from shifts in online data, this would still require an offline sample to confirm this. Thus, the results indicate that the estimation of cell concentration, both of target cells and contaminants, can be enhanced. For example, the calculation from the cell count on an image depends on (1) the field of the angular view of the ISM objective and (2) the height of the measurement zone (the flow-through space) of the ISM. The geometry of these can be adjusted in the design of the ISM. Also, due to mechanical clearance of the ISM components a limited accuracy of determining the measurement zone height reduces the cell concentration accuracy. With such improvements of the ISM design it is foreseeable that the online ISM technique has a promising prospect for online contamination control. However, additional testing and validation of the ISM at manufacturing scale and conditions are necessary, which would be the next step in adapting the ISM as an infection guard in bio-
Fig. 9. Contamination with C. utilis at 41 h. Panel A shows the pH during the process. In Panel B the level of dissolved oxygen is presented Panel C shows cell concentration estimate with the dielectric probe.
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industrial applications.
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