The authors are thankful for the support of the German Federal Ministry of Economics and Energy within the framework ZIM-Koop, project "Smart Process Inspection", grant no. ZF 4184201CR5.

NOTE: The following steps are necessary to adapt the parameters to the respective microorganism and culture conditions. The adjustment of probe settings lasts about 20 min for an experienced user. A detailed description of tools and steps is given in the corresponding probe manual from SOPAT GmbH. In general, the tools that are presented in the following protocol are needed: (i) Probe Controller for probe adjustments and image acquisition; (ii) Fiji (ImageJ) for annotations on acquired images; (iii) <em...

<ol>
	<li>Maa&#223;, S., Rojahn, J., H&#228;nsch, R., Kraume, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+drop+detection+using+image+analysis+for+online+particle+size+monitoring+in+multiphase+systems.">Automated drop detection using image analysis for online particle size monitoring in multiphase systems.</a> Computers &amp; Chemical Engineering. 45, 27-37 (2012).</li><li>Lemoine, A., Delvigne, F., Bockisch, A., Neubauer, P., Junne, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tools+for+the+determination+of+population+heterogeneity+caused+by+inhomogeneous+cultivation+conditions.">Tools for the determination of population heterogeneity caused by inhomogeneous cultivation conditions.</a> Journal of biotechnology. 251, 84-93 (2017).</li><li>Marb&#224;-Ard&#233;bol, A. M., Bockisch, A., Neubauer, P., Junne, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sterol+synthesis+and+cell+size+distribution+under+oscillatory+growth+conditions+in+Saccharomyces+cerevisiae+scale-down+cultivations.">Sterol synthesis and cell size distribution under oscillatory growth conditions in Saccharomyces cerevisiae scale-down cultivations.</a> Yeast. 35 (2), 213-223 (2017).</li><li>Xiao, Y., Bowen, C. H., Liu, D., Zhang, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploiting+nongenetic+cell-to-cell+variation+for+enhanced+biosynthesis.">Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis.</a> Nature chemical biology. 12 (5), 339-344 (2016).</li><li>Beutel, S., Henkel, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+sensor+techniques+in+modern+bioprocess+monitoring.">In situ sensor techniques in modern bioprocess monitoring.</a> Applied microbiology and biotechnology. 91 (6), 1493 (2011).</li><li>Belini, V. L., Wiedemann, P., Suhr, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+microscopy:+A+perspective+for+industrial+bioethanol+production+monitoring.">In situ microscopy: A perspective for industrial bioethanol production monitoring.</a> Journal of microbiological methods. 93 (3), 224-232 (2013).</li><li>Havlik, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+of+microalgal+cultivations+with+on-line,+flow-through+microscopy.">Monitoring of microalgal cultivations with on-line, flow-through microscopy.</a> Algal Research. 2 (3), 253-257 (2013).</li><li>Suhr, H., Herkommer, A. 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M., Emmerich, J., Neubauer, P., Junne, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell-based+monitoring+of+fatty+acid+accumulation+in+Crypthecodinium+cohnii+with+three-dimensional+holographic+and+in+situ+microscopy.">Single-cell-based monitoring of fatty acid accumulation in Crypthecodinium cohnii with three-dimensional holographic and in situ microscopy.</a> Process Biochemistry. 52, 223-232 (2017).</li><li>Porro, D., Vai, M., Vanoni, M., Alberghina, L., Hatzis, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+and+modeling+of+growing+budding+yeast+populations+at+the+single+cell+level.">Analysis and modeling of growing budding yeast populations at the single cell level.</a> Cytometry. 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M., Emmerich, J., Muthig, M., Neubauer, P., Junne, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=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.</a> Microbial cell factories. 17 (1), 73 (2018).</li><li>Marquard, D., Schneider-Barthold, C., D&#252;sterloh, S., Scheper, T., Lindner, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+monitoring+of+cell+concentration+in+high+cell+density+Escherichia+coli+cultivations+using+in+situ+Microscopy.">Online monitoring of cell concentration in high cell density Escherichia coli cultivations using in situ Microscopy.</a> Journal of biotechnology. 259, 83-85 (2017).</li><li>Marquard, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+microscopy+for+online+monitoring+of+cell+concentration+in+Pichia+pastoris+cultivations.">In situ microscopy for online monitoring of cell concentration in Pichia pastoris cultivations.</a> Journal of biotechnology. 234, 90-98 (2016).</li><li>Camisard, V., Brienne, J., Baussart, H., Hammann, J., Suhr, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inline+characterization+of+cell+concentration+and+cell+volume+in+agitated+bioreactors+using+in+situ+microscopy:+application+to+volume+variation+induced+by+osmotic+stress.">Inline characterization of cell concentration and cell volume in agitated bioreactors using in situ microscopy: application to volume variation induced by osmotic stress.</a> Biotechnology and bioengineering. 78 (1), 73-80 (2002).</li><li>B&#246;hm, A., &#220;cker, A., J&#228;ger, T., Ronneberger, O., Falk, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ISOODL:+Instance+segmentation+of+overlapping+biological+objects+using+deep+learning.">ISOODL: Instance segmentation of overlapping biological objects using deep learning.</a> , 1225-1229 (2018).</li><li>Davey, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Life,+Death,+and+In-Between:+Meanings+and+Methods+in+Microbiology.">Life, Death, and In-Between: Meanings and Methods in Microbiology.</a> Applied and environmental microbiology. 77 (16), 5571-5576 (2011).</li><li>Lodolo, E. J., Kock, J. L., Axcell, B. C., Brooks, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+yeast+Saccharomyces+cerevisiae-the+main+character+in+beer+brewing.">The yeast Saccharomyces cerevisiae-the main character in beer brewing.</a> FEMS yeast research. 8 (7), 1018-1036 (2008).</li><li>Albertin, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+size+drives+industrial+Saccharomyces+cerevisiae.+alcoholic+fermentation+and+is+under+genetic+control.">Population size drives industrial Saccharomyces cerevisiae. alcoholic fermentation and is under genetic control.</a> Applied and environmental microbiology. 77 (8), 2772-2784 (2011).</li><li>Gomes, J., Chopda, V. R., Rathore, A. 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The authors have nothing to declare.

ISM as presented here with the same or very similar devices was used to measure morphologic dynamics of fungi, microalgae, and yeast cells, which enabled the determination of growth activity, and in case of algae, intracellular product accumulation. The sensor has no movable parts and is directly applicable in any standard stirred tank bioreactor, either through a standard port or in a sterilizable by-pass. Since yeast is much smaller than algae, the reduction in cell size required some recent hardware adaptions like a h...

Morphological features of cells are often related to the physiological state, a connection between form and function exists for many applications. The morphology of a single cell is influenced by the state of growth, the cell&#39;s age, osmotic and other potential cell stresses or product accumulation. Morphological changes of cells are often a measure of the growth vitality of a culture. Intracellular product synthesis, lipid accumulation in algae and inclusion body formation in bacteria, among others, are related with the cell size as well. Cell agglomeration can be another factor that is worth investigating as summarized recently2.
Population heterogeneities can be quantified based on morphological features of individual cells. Studies showed that heterogeneity within a culture might be significant, e.g., under large-scale production conditions3 the overall yield might be affected by a low performance of subpopulations4.
Usually, the assessment of morphological features of cells is performed by manual sampling or with a by-pass flow chamber coupled to a photo-optical device. This leads to several restrictions: the limited amount of acquired data can hardly provide statistically reliable measurements; the time delay in between sampling and the accessibility of results may be too long in comparison to the dynamics of the process; and most important, the sampling procedure (location of the sampling port, pre-treatment of the sample before the measurement, unfavorable conditions in the sampling or bypass tube) can trigger a biased error as the sample procedure itself can already affect cell morphology. Finally, there exists always a high risk of contamination during sampling or in by-pass solutions, if they are not sterilizable in place.
The application of in situ microscopy (ISM) can circumvent several of these problems. If cells are detected automatically, a correct identification of their morphological features can be surveyed5. Until now, the main limitations of this method were (i) the evaluation time of images, which was too long for in situ applications, and (ii) the poor resolution of images, especially at high cell densities. Although first solutions of ISM included mechanical sampling, dilution of the probe, or were restricted to a by-pass system6,7, further approaches allow capture of the cell suspension directly8.
Recent advances in ISM allow for the in line or on line monitoring of cells on a single-cell basis, which provides the distribution of morphological parameters in real-time directly in cell suspensions at considerably high cell concentrations. Through off line analyses of the cells&#39; key parameters, correlations with information provided by the coupled automated cell detection and ISM can be identified. Then, new soft sensor designs are achieved, in which an unmeasurable parameter is estimated with the single-cell morphology.
In this report, the ISM is conducted by coupling a photo-optical probe to an automated image analysis. The ISM consists of a single-rod sensor probe that enables the capture of images within a known focus range in an adjustable measurement gap with a high-resolution CCD camera [MM-Ho = CCD GT2750 (2750x2200) and MM 2.1 = CMOS G507c (2464x2056)]. The flash light illumination is conducted by transmission. Therefore, the light originates from the opposite side of the camera9 and its intensity can be adjusted. Cells pass continuously through this gap with the liquid flow. Hence, a representative sample population is obtained. The probe can be mounted directly to the bioreactor so that it reaches into the cell suspension, or it can be used in a sterilizable by-pass. The sensor shell is connected to the system prior to sterilization, the optical parts are afterwards mounted into the shell.
Until now, relevant industrial microorganisms, e.g., filamentous fungi (diameter of up to over 200 &#181;m), the heterotrophic microalgae Crypthecodinium cohnii (average cell diameter of 20 &#181;m), and the yeast Saccharomyces cerevisiae (average cell diameter of 5 &#181;m), were investigated with this or similar devices, which is shortly described.
Filamentous fungi tend to form pellets under certain cultivation conditions. These are of a size of up to several hundred &#181;m. The hyphae of the fungal cells develop different lengths in dependence to the hydrodynamic stress in the fluid phase. This has an influence on the metabolic and growth activity, substrate uptake and product release. ISM was applied to identify the pellet size distribution and the width of zones of lower biomass density at the edges of the pellets (own unpublished data).
The size of C. cohnii alters between 15 and 26 &#181;m when cells accumulate the polyunsaturated fatty acid docosahexaenoic acid (DHA) under nitrogen limitation. This biotechnological DHA production process consists of two parts, the growth phase, in which cells divide and become smaller, and the production phase, in which cells accumulate the product and thus become larger. Therefore, the cell size was used to determine the process state, in which either growth or DHA production was favorable. Finally, a correlation between the cell size and the DHA content was found. In this case, ISM allows to monitor the intracellular DHA accumulation in real time without the requirement of sampling, cell disruption, and the common gas chromatography analysis10.
Budding yeast is usually of a size between 3 and 8 &#181;m. The proportion of cells that are in the maturation state at a time, as described with the budding index (BI), provides information about the growth vitality11,12, and even a relation with recombinant protein secretion has been proven13. With the help of ISM, budding and non-budding yeast cells (cells with and without a bud) were distinguished14. Stress conditions can also lead to a broader variation of the cell size within a yeast population, as recently shown in scale-down cultivations, in which the conditions of large-scale nutrient-limited fed-batch cultivations were mimicked3.
Therefore, ISM has the potential to monitor growth vitality and product formation on a single-cell level during all stages of a bioprocess for the identification of optimal cultivation conditions, or for the purpose of process control. The methods described here are focused on microbial applications with single cells, but are also applicable to larger particles like human and animal cells, cell agglomerates and pellets of filamentous organisms.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sensor MM 2.1 - MFC</td>
			<td>SOPAT GmbH, Germany</td>
			<td>n.a.</td>
			<td>Inline Monocular Microscopic probe Version 2.1 with a Mirco Flow Cell</td>
		</tr>
		<tr>
			<td>Sofware version v1R.003.0092</td>
			<td>SOPAT GmbH, Germany</td>
			<td>n.a.</td>
			<td></td>
		</tr>
		<tr>
			<td>Thickness gauge</td>
			<td>n.n.</td>
			<td></td>
			<td>It can be any supplier, DIN 2275:2014-03</td>
		</tr>
		<tr>
			<td>Ethanol 70%</td>
			<td>n.n.</td>
			<td></td>
			<td>It can be any supplier</td>
		</tr>
		<tr>
			<td>SOPAT manual Version 2.0.5</td>
			<td>SOPAT GmbH, Germany</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Optical lense paper</td>
			<td>VWR</td>
			<td>470150-460</td>
			<td></td>
		</tr>
		<tr>
			<td>Fiji, ImageJ</td>
			<td>open source</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL conical centrifuge tubes</td>
			<td></td>
			<td></td>
			<td>It can be any supplier</td>
		</tr>
	</tbody>
</table>

in situ microscopy for real-time determination of single-cell morphology in bioprocesses

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved oxygen concentration). Nevertheless, the morphology of cells can be a suitable indicator for optimal conditions, since it changes with dependence on the growth state, product accumulation and cell stress. Furthermore, the single-cell size distribution provides not only information about the cultivation conditions, but also about the population heterogeneity. To gain such information, a photo-optical in situ microscopy device1 was developed to enable the monitoring of the single-cell size distribution directly in the cell suspension in bioreactors. An automated image analysis is coupled to the microscopy based on a neural network model, which is trained with user-annotated images. Several parameters, which are gained from the captures of the microscope, are correlated to process relevant features of the cells, like their metabolic activity. Until now, the presented in situ microscopy probe series was applied to measure the pellet size in filamentous fungi suspensions. It was used to distinguish the single-cell size in microalgae cultivation and relate it to lipid accumulation. The shape of cellular particles was related to budding in yeast cultures. The microscopy analysis can be generally split into three steps: (i) image acquisition, (ii) particle identification, and (iii) data analysis, respectively. All steps have to be adapted to the organism, and therefore specific annotated information is required in order to achieve reliable results. The ability to monitor changes in cell morphology directly in line or on line (in a by-pass) enables real-time values for monitoring and control, in process development as well as in production scale. If the off line data correlates with the real-time data, the current tedious off line measurements with unknown influences on the cell size become needless.

The cell size detection in yeast cultures with the ISM and automated image detection to distinguish between budding and non-budding cells was successfully conducted. Both the stroboscope intensity and the choice of the measurement gap have a range of tolerance, in which the particle identification is not affected. For example, S. cerevisiae cells were measured with various stroboscope intensities within a variation range of 11% at a dry biomass concentration of 4 g L-1...

Watch this Scientific Journal Video about In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses at JoVE.com

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

A photo-optical in situ microscopy device was developed to monitor the size of single cells directly in the cell suspension. The real-time measurement is conducted by coupling the photo-optical sterilizable probe to an automated image analysis. Morphological changes appear with dependence on the growth state and cultivation conditions.

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved ...

This method can help answer key questions of population heterogeneity through the morphological assessment of the cells. In situ microscopy allows study cultivation conditions, process yields, and product quality. The main advantage of this technique is an in situ measurement, which provides real-time data without sampling or bringing risk of contamination.In situ microscopy coupled to automated image recognition can provide additional information about cellular structures, shape, and cell agglomeration beyond size. It's also possible to measure cell concentrations. The method has proven to be a reliable tool.No alternative method provides the same information in such a short time and low effort for sample preparation. The method has been adapted to various bioprocesses and microorganisms, SEG filamentous fungi, micro-allergy, and yeast. The probe is used in conjunction with a computer.The hardware consists of a single rod probe with a high-resolution CCD camera. Cells that pass through an adjustable measurement gap are imaged by the camera. Light enters the gap opposite the camera, meaning illumination is by transmission.Set the hardware parameters using cell concentrations that span the expected range. Begin making preparations for offline measurements. Use a thickness gauge to adjust the measurement gap.Then turn the probe screw nut to set the measurement gap to five or 10 times the maximum expected cell diameter. At the computer, open the probe controller software. In the software, select the desired probe and go to section actions.Then press Connect. Next, go to the Probe Control tab and open it. Start video streaming by pressing the play button.Fix the probe to a tripod and perform the following steps for each measurement. Work with the probe and spray ethanol in the measurement gap. Carefully wipe any dust or dirt away with optical paper.At the computer, use live view to check that the glass of the sensor is free of particles. Next, place a dry optical paper in the measurement gap for focusing. Turn the binding screw to move the focus manually.Stop focusing when single fibers of the paper in the measurement gap are clearly seen. Now, get a tube filled with culture broth. Dip the microscope in the broth, so the gap is fully covered with cell suspension.Use the binding screws to fine-tune the focus on the cells. Once this focusing is done, do not change it again during the experiment. Return to the software for the experiment.Go to the Triggering menu. Under the Triggering menu, set the frame rate. One hertz is recommended for offline measurements.From there, go to the Frames Per Trigger field and set it as necessary for good statistics. Here, about 200. Also, go to the General menu.There, select the directory in which the images will be saved. Begin image acquisition with the Start Image Trigger Acquisition button. Gently move the tube to induce flow in the measurement gap.Use the images from the first run of the experiment as a training set. Have the open source software Fiji ready and drop the images from the experiment into its window. When done, select Analyze, followed by Tools, then ROI Manager.Next, choose a selection tool. In the image, decide on a particle to annotate that is in focus. Draw a circle around it with the selection tool.Press Add to add the annotation to the ROI Manager. Next, select a brush tool and choose an appropriate pixel size. Use the brush tool to refine the selection.Continue marking all objects of interest in the same way on about 15 images. Save the annotated files and use them as a training set. When the recognition algorithm is trained and ready, use it to visualize results.In the Results Analyzer, go to File, followed by Import File. There, select the desired results file. Continue by pressing the Create Chart button.Select Distribution Chart. This will display the morphological distribution of the culture. Return to Create Chart to select Sensitivity Plot.The information displayed can help determine how many cells must be analyzed to obtain the desired accuracy. Perform online measurements after completing offline measurements. For online measurements, connect the probe directly to a bioreactor.Once connected, properly sterilize the assembly. Select Monitoring in the dashboard. There is a play button to start image acquisition and a stop button to end it.Press the play button. At the bioreactor, begin inoculation of the culture. The camera will capture video of the cells and automatically identify them for analysis.The monitoring provides online data of cell sizes and shapes of different cell morphologies during the entire fermentation. The real-time results can be formatted in various ways and also be exported for further analysis. This plot of cumulative distributions of S.Cerevisiae cell diameter is created from data analyzed using this technique.It demonstrates the automatic cell recognition is able to distinguish budding and non-budding cells. The solid curve is the distribution during a cultivation at three hours. The dotted curve is the distribution of the same cultivation at seven hours.The dashed curve represents data collected at 13 hours. The annotation procedure is time-consuming, but it is the key for achieving the desired accuracy for the cell identification. With this procedure, if the measurement times are shorter than the process dynamics, the real-time measurement is able to be used for process control.This technique paves the way for using the measurement of population heterogeneity as a process parameter for microbial cultivations from the early process development steps to production scale.

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Bioengineering

7.8K visualizações.  Technische Universität Berlin. Esse método pode ajudar a responder a questões-chave da heterogeneidade populacional através da avaliação morfológica das células. A microscopia in situ permite estudar condições de cultivo, rendimentos de processos e qualidade do produto. A principal vantagem dessa técnica é uma medição in situ, que fornece dados em tempo real sem amostragem ou trazendo risco de contaminação.A microscopia in situ acoplada ao reconhecimento automatizado de imagens pode fornecer informaç...