Name: in situ microscopy for real-time determination of single-cell morphology in bioprocesses
Uploaded: 2019-12-05T12:00:00.000+00:00
Duration: 7 min 26 s
Description: Watch this Scientific Journal Video about In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses at JoVE.com

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) SOPAT support for artificial neural network (ANN) training and workflow creation; (iv) Batcher for data batch processing using already acquired images with a workflow; (v) Result Analyzer for result visualization and evaluation on batch processed images; and (vi) Monitor for automated real-time measurement and result visualization.
1. Setting Hardware Parameters
<ol>
	<li>Prepare a culture with the highest cell concentration that might be achieved during the experiment or centrifuge and resuspend the pellet in order to achieve this concentration. In this case, 65 g L-1 of dry biomass concentration was chosen for S. cerevisiae cultivations.</li>
	<li>Prepare different dilutions, which range from the highest to the lowest concentration so that the expected range is fully covered. A minimum of 4 different concentrations are recommended.</li>
	<li>Identify the cell size range of the microorganism with conventional microscopy. Define the expected maximum diameter (dmax) of respective cells. This value is set to 8 &#181;m in case of S. cerevisiae.</li>
	<li>Choose two measurement gaps of 5x and 10x of the expected dmax of the cells.</li>
	<li>Choose the maximum stroboscope intensity. Choose stroboscope intensities for both gaps with the highest cell concentration so that cells are still visible on the images with the lowest light intensity (darkest images).</li>
	<li>Choose the minimum stroboscope intensity, and then choose stroboscope intensities for both gaps so that cells are still visible on the images with the highest light intensity (brightest images). Use the lowest cell concentration, which likely appears during the measurement period.</li>
	<li>Choose one focus position, which yields the sharpest images for each measurement gap, for both stroboscope intensities and for the concentration range that need to be tested (see step 2 for details about focusing). Focus cells appropriately so that the image data can be annotated afterwards (see step 4).</li>
	<li>Measure the previously prepared dilution series of the cell concentration (see step 2) with both gap widths and stroboscope intensities.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57823/57823fig1.jpg" /> 
Figure 1: Concentration calibration tool. Left GUI: set image directories (minimum of 3) with known concentrations; central GUI: choose features to be calculated on the image directory; right GUI: choose the weighted root mean square error (WRMSE) to identify the minimum. WRMSE and the best correlation between any image feature and the cell concentration. <a href="https://www.jove.com/files/ftp_upload/57823/57823fig1alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="9">
	<li>Evaluate the dilution series experiment.
	<ol>
		<li>Use the Concentration Calibration (CoCa) Tool (see Figure 1) to identify the optimal correlation between the extracted image features (brightness or sharpness) and the previously measured concentrations provided by the user, e.g., dry biomass or cell counts. Follow the instructions in the software manual for further details.</li>
		<li>Identify the optimal correlation between the extracted information from the image features at various concentrations compared to any off line measurements. See the legend of Figure 1.</li>
		<li>Choose the most reasonable measurement gap and stroboscope intensities under consideration of the concentration correlation curve with the features that result in the smallest weighted root mean square error (WRMSE). 
		NOTE: The measurement gap is fixed during the experiment, whereas the stroboscope intensity can be adapted according to the cell concentration.</li>
	</ol>
	</li>
</ol>
2. Off Line Measurement
<ol>
	<li>Adjust the desired measurement gap according to step 1 with the help of a thickness gauge.</li>
	<li>Open the graphical user interface Probe Control in the SOPAT dashboard.</li>
	<li>Connect the desired probe to the amplifier in the software subsection Actions and press Connect.</li>
	
	<li>Press the Play button to start streaming (Live View).</li>
	<li>Clean the measurement gap by spraying ethanol into the gap and carefully wipe any dust or dirt with an optical paper. Check that the glass of the sensor is free of particles with the Live View in the CamControl. 
	NOTE: Particles and dust disturb measurements and the automatic cell identification.</li>
	<li>Place a dry optical paper in the measurement gap. Open the tab Probe control and adjust the stroboscope intensity in order to visualize the paper. Turn the binding screw until the single fibers of the paper are clearly seen.</li>
	<li>Fill a tube with culture broth. Dip the microscope in the culture broth so that the gap is fully covered with cell suspension. Open the tab Probe control and adjust the desired stroboscope intensity according to section 1. Focus on the cells by fine-tuning the focus binding screw. The focus must not be changed anymore during the experiment 
	NOTE: 5-6 mL of culture broth are added to a 50 mL conical centrifugal tube to float the measuring gap sufficiently.</li>
	<li>Define the number of frames per time point [-] in the user interface in the menu Triggering in the GUI Frames per trigger. Set the number of frames to 200 frames per trigger. 
	NOTE: The number of frames can be reduced to the lowest value, which is necessary for a statistical reliable result. This depends on the sample size required to obtain a representative morphological cell size distribution (see also step 5).</li>
	<li>Define the frame rate [Hz] in the menu Triggering in the GUI Frame rate. Choose a frame rate that guarantees that moving particles from a previous frame will not appear in the following frame. 
	NOTE: This can be proven with a test trigger with 200 frames. Inspect the images for particles, which are captured repeatedly. If this is the case, decrease the frame rate. For off line measurements, 1 Hz is recommended.</li>
	<li>Set the directory, in which the acquired images will be saved, in the menu General.</li>
	<li>Perform an image acquisition by activating the Start image trigger acquisition button. Move the tube with culture suspension gently up and down to induce a flow through the measuring gap.</li>
	<li>Repeat step 2.5 after each measurement.</li>
	<li>Check the acquired images. Cells have to be sharp enough for annotation. Inspect the images for particles, which are captured repeatedly. If this is the case, decrease the frame rate.</li>
	<li>Save the settings by selecting the following pathway: C:\Program Files\SOPAT GmbH\monitoringPrograms\camcontrol, and press Save.</li>
</ol>
3. Particle Identification
<ol>
	<li>Annotate particles for the training of the artificial neural network (ANN) (training set).
	<ol>
		<li>Load the acquired images into the annotation tool &#34;Fiji, ImageJ&#34; by dragging and dropping the file into the Main &#34;ImageJ&#34; window (see GUI &#34;Fiji&#34; tool in Figure 2)</li>
		<li>Open the ROI Manager by selecting: Analyze | Tools | ROI Manager.</li>
		<li>Choose a selection tool. Wand (tracing) tool, freehand, oval, or elliptical selections are recommended.</li>
		<li>Draw a circle around the particle that shall be annotated with the selection tools mentioned before and then refine it with the brush tool.</li>
		<li>Add the annotation to the ROI Manager by pressing Add [t].</li>
		<li>Mark all objects of interest (cells to be identified) on about 15 images. 
		NOTE: In order to cover all necessary information, i.e., different shapes, sizes, concentration of cells, brightness, etc., use five images from the start, five images from in between, and five images from the end of the experiment.</li>
		<li>Decide, if cells need to be classified in different subclasses due to their shape (e.g., different phases of a cell cycle), or if all cells are of the same class.</li>
		<li>Change the name of each selected particles accordingly. Set one name or abbreviation for each class and a counter for each particle of the class (e.g., cell_1, cell_2, etc.). Annotate at least 50 particles per class.</li>
		<li>Do not annotate objects, which should not be detected, because they are not relevant for the process, such as gas bubbles or other particles like undissolved media components. 
		NOTE: Those events will not be included in the training procedure for the ANN and be regarded as background.</li>
		<li>Do not annotate cells that are out of focus.</li>
		<li>Annotate the images as consistent as possible. If there are doubts, the label Ignore can be applied. It is highly recommended not to abuse its use, since the ANN will only recognize structures that are labeled.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57823/57823fig2a.jpg" /> 
Figure 2: Fiji tool user interface. A training set is created with the annotated images. A manual annotation consisting of two classes is depicted, the list of annotated particles is shown in the ROI Manager. Different names and colors can be set for different classes. <a href="https://www.jove.com/files/ftp_upload/57823/57823fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Save the annotated objects and the image to a ZIP format and send the file to the training network, either via upload to the platform or by sending the ZIP file via e-mail. 
	NOTE: Usually, it takes a number of iterative training rounds to identify adequate predictions of the classified objects on the images. Each training round leads to a workflow that is returned by the program.</li>
	<li>Use the workflow (*.wf) with the trained object recognition algorithm to analyze test images with the data batch progressing program Batcher that can be started in the dashboard.</li>
	<li>Check object detection on the test images through the quantification of false positive and negative events.
	<ol>
		<li>Quantify the detection of false positive events: particles erroneously detected as cells, cells that are not correctly classified, and cells of which the contour has not been well identified.</li>
		<li>Quantify the false negative events (cells that are not recognized as such).</li>
	</ol>
	</li>
	<li>Visualize the results in the tool Result analyzer by starting the program in the dashboard.
	<ol>
		<li>Import the desired results files with File | Import file or File | Import folders.</li>
		<li>Visualize the results by Chart | Create chart on the Charts GUI.</li>
		<li>Select one of the following options: Distribution chart, Sensitivity plot, Characteristic over time, Characteristic over survey points, and Feature vs. feature. 
		NOTE: A manual regarding the utilization of the Result Analyzer comes with the system and is also available from the support.</li>
		<li>If the results are acceptable, run the workflow in the Batcher on all acquired images of the experiment. At the same time then the monitoring program can be created by combining the saved settings from the Probe Controller (*.pcfg) with the workflow (*.wf), see also the manual. 
		NOTE: The workflow can also be used for monitoring future experiments for this culture media.</li>
		<li>If the results are not acceptable, check the annotation on the training set and/or continue with another iterative training round (see step 4.2).</li>
	</ol>
	</li>
</ol>
4. Sample Size Quantification
<ol>
	<li>Set the standard deviation (&#963;), which is acceptable among the detected particles. 
	NOTE: The standard deviation changes in parallel to the cell size homogeneity. The maximum standard deviation indicates the sample with the highest degree of size heterogeneity.</li>
	<li>Set the amplitude of the confidence interval or the desired accuracy in relation to the expected variance of measurements (e).</li>
	<li>Set the admitted error (&#945;) between 5% (z1-&#945;/2 = 1.96) and 10% (z1-&#945;/2 = 1.64).</li>
	<li>Calculate the number of cells to be identified from each class from equation 1. 
	<img alt="Equation 1" src="/files/ftp_upload/57823/57823eq1.jpg" /> [Equation 1] 
	NOTE: Based on the number of cells, the number of images that needs to be acquired can be defined for each data point.</li>
	<li>Perform a sensitivity analysis on random time points of the experiment to check that the analysis of n particles leads to a variability of the mean Feret diameter and the Dv90 of less than 5%. It can be calculated automatically in the Result Analyzer.</li>
</ol>
5. On Line (By-Pass) or In Line Measurement
<ol>
	<li>Perform the off line measurement procedure first (see step 2) in order to set the hardware and software settings as a function of the organism and process (concentration or media).</li>
	<li>Upload the saved settings from the previous section by selecting the button Load and select the following pathway: C:\Program Files\SOPAT GmbH\monitoringPrograms\camcontrol.</li>
	<li>Connect the probe to the flow cell or to the bioreactor. 
	NOTE: In situ measurements can be performed with a pinch flange.</li>
	<li>Perform sterilization. 
	NOTE: Only the probe-wetted material of the instrument is sterilizable through steam sterilization. The probe wetted length can go from 6 to 222 mm (Figure 3).</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57823/57823fig3a.jpg" /> 
Figure 3: Sketch of the ISM devices. The probe MM-Ho (A) is installable directly in the bioreactor, whereas the probe MM 2.1 (B) can be used as a by-pass. The culture broth circulation is marked with arrows in each picture. The conversion factors are 0.166 &#181;m pix-1 for MM-Ho and 0.087 &#181;m pix-1 for MM 2.1. <a href="https://www.jove.com/files/ftp_upload/57823/57823fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="5">
	<li>Define the image acquisition rate in the GUI Triggering in the field Trigger interval [s]. 
	NOTE: Depending on the process dynamics, the image acquisition rate can be adapted. For example, if a lag-phase of 3 hours is expected, the rate of acquisition can be lower, than if a metabolism shift or the accumulation of a product shall be monitored. Usually, this requires a much shorter acquisition time in the minute range. For example, an image acquisition sequence between 5 and 10 min during a batch yeast cultivation provides sufficient information to capture process dynamics.</li>
	<li>Define the frame rate as explained in step 2.10. 
	NOTE: In on line and in line measurements, the frame rate can be increased, since the mechanical stirring can increase the flow rate through the gap.</li>
	<li>Start image acquisition by activating the button Start streaming of selected probe.</li>
	<li>Stop acquisition when the experiment is finished with the button Stop streaming of selected probe. 
	NOTE: For the 1st run, start the acquisition just before the inoculation of the culture and proceed to step 4. For the following runs, open the program Monitoring in the dashboard and select the created workflow (see step 4). Start the monitoring just before the inoculation of the culture by pressing the Play button.</li>
</ol>

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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><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...

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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 ...

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

Research

JoVE Journal

Bioengineering

7.9K Views.  Technische Universität Berlin. 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.

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

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

Many biological and clinical studies require the longitudinal study and analysis of morphology and function with cellular level resolution. Traditionally, multiple experiments are run in parallel, with individual samples removed from the study at sequential time points for evaluation by light microscopy. Several intravital techniques have been developed, with confocal, multiphoton, and second harmonic microscopy all demonstrating their ability to be used for imaging in situ 1. With these systems, however, the required infrastructure is complex and expensive, involving scanning laser systems and complex light sources. Here we present a protocol for the design and assembly of a high-resolution microendoscope which can be built in a day using off-the-shelf components for under US$5,000. The platform offers flexibility in terms of image resolution, field-of-view, and operating wavelength, and we describe how these parameters can be easily modified to meet the specific needs of the end user.
We and others have explored the use of the high-resolution microendoscope (HRME) in in vitro cell culture 2-5, in excised 6 and living animal tissues 2,5, and in human tissues in vivo 2,7. Users have reported the use of several different fluorescent contrast agents, including proflavine 2-4, benzoporphyrin-derivative monoacid ring A (BPD-MA) 5, and fluoroscein 6,7, all of which have received full, or investigational approval from the FDA for use in human subjects. High-resolution microendoscopy, in the form described here, may appeal to a wide range of researchers working in the basic and clinical sciences. The technique offers an effective and economical approach which complements traditional benchtop microscopy, by enabling the user to perform high-resolution, longitudinal imaging in situ.

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

In many biological and clinical situations it is advantageous to study cellular processes as they evolve in their native microenvironment. Here we describe the assembly and use of a low-cost fiber-optic microscope which can provide real time imaging in cell culture, animal studies, and clinical patient studies.

Many biological and clinical studies require the longitudinal study and analysis of morphology and function with cellular level resolution.  ...

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described in detail. The PLBR can be utilized for the analysis of single bacteria and microcolonies to investigate biotechnological and microbiological related questions concerning, e.g.&#160;cell growth, morphology, stress response, and metabolite or protein production on single-cell level. The device features continuous media flow enabling constant environmental conditions for perturbation studies, but in addition allows fast medium changes as well as oscillating conditions to mimic any desired environmental situation. To fabricate the single use devices, a silicon wafer containing sub micrometer sized SU-8 structures served as the replication mold for rapid polydimethylsiloxane casting. Chips were cut, assembled, connected, and set up onto a high resolution and fully automated microscope suited for time-lapse imaging, a powerful tool for spatio-temporal cell analysis. Here, the biotechnological platform organism Corynebacterium glutamicum&#160;was seeded into the PLBR and cell growth and intracellular fluorescence were followed over several hours unraveling time dependent population heterogeneity on single-cell level, not possible with conventional analysis methods such as flow cytometry. Besides insights into device fabrication, furthermore, the preparation of the preculture, loading, trapping of bacteria, and the PLBR cultivation of single cells and colonies is demonstrated. These devices will add a new dimension in microbiological research to analyze time dependent phenomena of single bacteria under tight environmental control. Due to the simple and relatively short fabrication process the technology can be easily adapted at any microfluidics lab and simply tailored towards specific needs.

In this protocol the fabrication, setup and basic operation of a microfluidic picoliter bioreactor (PLBR) for single-cell analysis of prokaryotic microorganisms is introduced. Industrially relevant microorganisms were analyzed as proof of principle allowing insights into growth rate, morphology, and phenotypic heterogeneity over certain time periods, hardly possible with conventional methods.

In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described ...

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Bacterial cells are able to form surface-attached biofilm communities known as biofilms by encasing themselves in extracellular polymeric substances (EPS). The EPS serves as a physical and protective scaffold that houses the bacterial cells and consists of a variety of materials that includes proteins, exopolysaccharides and DNA. The composition of the EPS may change, which remodels the mechanic properties of the biofilm to further develop or support alternative biofilm structures, such as streamers, as a response to environmental cues. Despite this, there are little quantitative descriptions on how EPS components contribute to the mechanical properties and function of biofilms. Rheology, the study of the flow of matter, is of particular relevance to biofilms as many biofilms grow in flow conditions and are constantly exposed to shear stress. It also provides measurement and insight on the spreading of the biofilm on a surface. Here, particle-tracking microrheology is used to examine the viscoelasticity and effective crosslinking roles of different matrix components in various parts of the biofilm during development. This approach allows researchers to measure mechanic properties of biofilms at the micro-scale, which might provide useful information for controlling and engineering biofilms.

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Particle-tracking microrheology investigates the viscoelasticity of materials. Here, the technique is used to determine the viscoelasticity, creep compliance and effective crosslinking roles of different matrix components of a bacterial biofilm. The matrix consists of polymeric substances secreted by the bacteria and its components determine biofilm structure and mechanical properties.

Bacterial cells are able to form surface-attached biofilm communities known as biofilms by encasing themselves in extracellular polymeric substances ...

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Optogenetic systems utilize genetically-encoded proteins that change conformation in response to specific wavelengths of light to alter cellular processes. There is a need for culturing and measuring systems that incorporate programmed illumination and stimulation of optogenetic systems. We present a protocol for building and using a continuous culturing apparatus to illuminate microbial cells with programmed doses of light, and automatically acquire and analyze images of cells in the effluent. The operation of this apparatus as a chemostat allows the growth rate and the cellular environment to be tightly controlled. The effluent of the continuous cell culture is regularly sampled and the cells are imaged by multi-channel microscopy. The culturing, sampling, imaging, and image analysis are fully automated so that dynamic responses in the fluorescence intensity and cellular morphology of cells sampled from the culture effluent are measured over multiple days without user input. We demonstrate the utility of this culturing apparatus by dynamically inducing protein production in a strain of Saccharomyces cerevisiae engineered with an optogenetic system that activates transcription.

We designed a continuous culturing apparatus for use with optogenetic systems to illuminate cultures of microbes and regularly image cells in the effluent with an inverted microscope. The culturing, sampling, imaging, and image analysis are fully automated so that dynamic responses to illumination can be measured over several days.

Optogenetic systems utilize genetically-encoded proteins that change conformation in response to specific wavelengths of light to alter cellular ...

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate ...

Neuroscience

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology1 even in complex tissue sections2. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. 
Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells3, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgen&ouml;ssische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). 
We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

We developed a software platform that utilizes Imaris Neuroscience, ImarisXT and MATLAB to measure the changes in morphology of an undefined shape taken from three-dimensional confocal fluorescence of single cells. This novel approach can be used to quantify changes in cell shape following receptor activation and therefore represents a possible additional tool for drug discovery.

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for ...

Biology

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be affected by cell shape. Bacterial cells are three-dimensional (3D) objects, although they are rarely treated as such. Most microscopy techniques result in two-dimensional (2D) images leading to the loss of data pertaining to the actual 3D cell shape and localization of proteins. Certain shape parameters, such as Gaussian curvature (the product of the two principal curvatures), can only be measured in 3D because 2D images do not measure both principal curvatures. Additionally, not all cells lie flat when mounting and 2D imaging of curved cells may not accurately represent the shapes of these cells. Accurately measuring protein localization in 3D can help determine the spatial regulation and function of proteins. A forward convolution technique has been developed that uses the blurring function of the microscope to reconstruct 3D cell shapes and to accurately localize proteins. Here, a protocol for preparing and mounting samples for live cell imaging of bacteria in 3D both to reconstruct an accurate cell shape and to localize proteins is described. The method is based on simple sample preparation, fluorescent image acquisition, and MATLAB-based image processing. Many high-quality fluorescent microscopes can be simply modified to take these measurements. These cell reconstructions are computationally intensive and access to high-throughput computational resources is recommended, although not necessary. This method has been successfully applied to multiple bacterial species and mutants, fluorescent imaging modalities, and microscope manufacturers.

This protocol explains how to prepare and mount bacterial samples for live three-dimensional imaging and how to reconstruct the three-dimensional shape of E. coli from those images.

The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be ...

Immunology and Infection

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Polymicrobial communities are ubiquitous in nature, yet studying their interactions at the single-cell level is difficult. Thus, a microscopy-based method has been developed for observing interspecies interactions between two bacterial pathogens. The use of this method to interrogate interactions between a motile Gram-negative pathogen, Pseudomonas aeruginosa and a non-motile Gram-positive pathogen, Staphylococcus aureus is demonstrated here. This protocol consists of co-inoculating each species between a coverslip and an agarose pad, which maintains the cells in a single plane and allows for visualization of bacterial behaviors in both space and time.
Furthermore, the time-lapse microscopy demonstrated here is ideal for visualizing the early interactions that take place between two or more bacterial species, including changes in bacterial species motility in monoculture and in coculture with other species. Due to the nature of the limited sample space in the microscopy setup, this protocol is less applicable for studying later interactions between bacterial species once cell populations are too high. However, there are several different applications of the protocol which include the use of staining for imaging live and dead bacterial cells, quantification of gene or protein expression through fluorescent reporters, and tracking bacterial cell movement in both single species and multispecies experiments.

This live-bacterial cell imaging protocol allows for visualization of interactions between multiple bacterial species at the single-cell level over time. Time-lapse imaging allows for observation of each bacterial species in monoculture or coculture to interrogate interspecies interactions in multispecies bacterial communities, including individual cell motility and viability.

Polymicrobial communities are ubiquitous in nature, yet studying their interactions at the single-cell level is difficult. Thus, a microscopy-based method ...

Use of In Vivo Imaging to Screen for Morphogenesis Phenotypes in Candida albicans Mutant Strains During Active Infection in a Mammalian Host

Candida albicans is an important human pathogen. Its ability to switch between morphologic forms is central to&#160;its pathogenesis; these morphologic changes are regulated by a complex signaling network controlled in response to environmental stimuli. These regulatory components have been highly studied, but almost all studies use a variety of in vitro stimuli to trigger filamentation. To determine how morphogenesis is regulated during the pathogenesis process, we developed an in vivo microscopy system to obtain high spatial resolution images of organisms undergoing hyphal formation within the mammalian host. The protocol presented here describes the use of this system to screen small collections of C. albicans mutant strains, allowing us to identify key regulators of morphogenesis as it occurs at the site of infection. Representative results are presented, demonstrating that some regulators of morphogenesis, such as the transcriptional regulator Efg1, have consistent phenotypes in vitro and in vivo, whereas other regulators, such as adenyl cyclase (Cyr1), have significantly different phenotypes in vivo compared to in vitro.

Use of In Vivo Imaging to Screen for Morphogenesis Phenotypes in Candida albicans Mutant Strains During Active Infection in a Mammalian Host

This manuscript describes a method for screening moderately sized Candida albicans mutant libraries for morphogenesis phenotypes during active infection in a mammalian host using non-invasive confocal microscopy.

Candida albicans is an important human pathogen. Its ability to switch between morphologic forms is central to&#160;its pathogenesis; these morphologic ...

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator and that it is possible to monitor and quantify single cells along several cell cycles. We describe the full protocol used to monitor and quantify a HeLa cell culture for 2.7 days. First, cell culture acquisition is performed with a lens-free video microscope, and then the data is analyzed following a four-step process: multi-wavelength holographic reconstruction, cell-tracking, cell segmentation and cell division detection algorithms. As a result, we show that it is possible to gather a dataset featuring more than 10,000 cell cycle tracks and more than 2 x 106 cell morphological measurements.

Lens-free video microscopy enables us to monitor cell cultures directly inside the incubator. Here we describe the full protocol used to acquire and analyze a 2.7 day long acquisition of cultured HeLa cells, leading to a dataset of 2.2 x 106 measurements of individual cell morphology and 10584 cell cycle tracks.

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator ...

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

Summary

Explore More Videos

Chapters in this video

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Use of In Vivo Imaging to Screen for Morphogenesis Phenotypes in Candida albicans Mutant Strains During Active Infection in a Mammalian Host

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture