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 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 ...

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

Research

JoVE Journal

Bioengineering

7.8K 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

Three-dimensional Optical-resolution Photoacoustic Microscopy

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As the mainstays of in vivo three-dimensional (3-D) optical microscopy, single-/multi-photon fluorescence microscopy and optical coherence tomography (OCT) have demonstrated their extraordinary sensitivities to fluorescence and optical scattering contrasts, respectively. However, the optical absorption contrast of biological tissues, which encodes essential physiological/pathological information, has not yet been assessable. 
The emergence of biomedical photoacoustics has led to a new branch of optical microscopy optical-resolution photoacoustic microscopy (OR-PAM)1, where the optical irradiation is focused to the diffraction limit to achieve cellular1 or even subcellular2 level lateral resolution. As a valuable complement to existing optical microscopy technologies, OR-PAM brings in at least two novelties. First and most importantly, OR-PAM detects optical absorption contrasts with extraordinary sensitivity (i.e., 100%). Combining OR-PAM with fluorescence microscopy3 or with optical-scattering-based OCT4 (or with both) provides comprehensive optical properties of biological tissues. Second, OR-PAM encodes optical absorption into acoustic waves, in contrast to the pure optical processes in fluorescence microscopy and OCT, and provides background-free detection. The acoustic detection in OR-PAM mitigates the impacts of optical scattering on signal degradation and naturally eliminates possible interferences (i.e., crosstalks) between excitation and detection, which is a common problem in fluorescence microscopy due to the overlap between the excitation and fluorescence spectra. 
Unique for optical absorption imaging, OR-PAM has demonstrated broad biomedical applications since its invention, including, but not limited to, neurology5, 6, ophthalmology7, 8, vascular biology9, and dermatology10. In this video, we teach the system configuration and alignment of OR-PAM as well as the experimental procedures for in vivo functional microvascular imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology capable of imaging optical absorption contrasts in vivo with cellular resolution and sensitivity. Here, we provide a visualized instruction on the experimental protocols of OR-PAM, including system configuration, system alignment, typical in vivo experimental procedures, and functional imaging schemes.

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As ...

Video-rate Scanning Confocal Microscopy and Microendoscopy

Confocal microscopy has become an invaluable tool in biology and the biomedical sciences, enabling rapid, high-sensitivity, and high-resolution optical sectioning of complex systems. Confocal microscopy is routinely used, for example, to study specific cellular targets1, monitor dynamics in living cells2-4, and visualize the three dimensional evolution of entire organisms5,6. Extensions of confocal imaging systems, such as confocal microendoscopes, allow for high-resolution imaging in vivo7 and are currently being applied to disease imaging and diagnosis in clinical settings8,9.
Confocal microscopy provides three-dimensional resolution by creating so-called "optical sections" using straightforward geometrical optics. In a standard wide-field microscope, fluorescence generated from a sample is collected by an objective lens and relayed directly to a detector. While acceptable for imaging thin samples, thick samples become blurred by fluorescence generated above and below the objective focal plane. In contrast, confocal microscopy enables virtual, optical sectioning of samples, rejecting out-of-focus light to build high resolution three-dimensional representations of samples.
Confocal microscopes achieve this feat by using a confocal aperture in the detection beam path. The fluorescence collected from a sample by the objective is relayed back through the scanning mirrors and through the primary dichroic mirror, a mirror carefully selected to reflect shorter wavelengths such as the laser excitation beam while passing the longer, Stokes-shifted fluorescence emission. This long-wavelength fluorescence signal is then passed to a pair of lenses on either side of a pinhole that is positioned at a plane exactly conjugate with the focal plane of the objective lens. Photons collected from the focal volume of the object are collimated by the objective lens and are focused by the confocal lenses through the pinhole. Fluorescence generated above or below the focal plane will therefore not be collimated properly, and will not pass through the confocal pinhole1, creating an optical section in which only light from the microscope focus is visible. (Fig 1). Thus the pinhole effectively acts as a virtual aperture in the focal plane, confining the detected emission to only one limited spatial location.
Modern commercial confocal microscopes offer users fully automated operation, making formerly complex imaging procedures relatively straightforward and accessible. Despite the flexibility and power of these systems, commercial confocal microscopes are not well suited for all confocal imaging tasks, such as many in vivo imaging applications. Without the ability to create customized imaging systems to meet their needs, important experiments can remain out of reach to many scientists.
In this article, we provide a step-by-step method for the complete construction of a custom, video-rate confocal imaging system from basic components. The upright microscope will be constructed using a resonant galvanometric mirror to provide the fast scanning axis, while a standard speed resonant galvanometric mirror will scan the slow axis. To create a precise scanned beam in the objective lens focus, these mirrors will be positioned at the so-called telecentric planes using four relay lenses. Confocal detection will be accomplished using a standard, off-the-shelf photomultiplier tube (PMT), and the images will be captured and displayed using a Matrox framegrabber card and the included software.

The complete construction of a custom, real-time confocal scanning imaging system is described. This system, which can be readily used for video-rate microscopy and microendoscopy, allows for an array of imaging geometries and applications not accessible using standard commercial confocal systems, at a fraction of the cost.

Confocal microscopy has become an invaluable tool in biology and the biomedical sciences, enabling rapid, high-sensitivity, and high-resolution optical ...

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians.&#160;Although historically invasive methods have comprised the only means available, the development of noninvasive imaging modalities (echocardiography, MRI, CT) having high temporal and spatial resolution provide a new window for quantitative diastolic function assessment. Echocardiography is the agreed upon standard for diastolic function assessment, but indexes in current clinical use merely utilize selected features of chamber dimension (M-mode) or blood/tissue motion (Doppler) waveforms without incorporating the physiologic causal determinants of the motion itself.&#160;The recognition that all left ventricles (LV) initiate filling by serving as mechanical suction pumps allows global diastolic function to be assessed based on laws of motion that apply to all chambers. What differentiates one heart from another are the parameters of the equation of motion that governs filling. Accordingly, development of the Parametrized Diastolic Filling (PDF) formalism&#160;has shown that the entire range of clinically observed early transmitral flow (Doppler E-wave) patterns are extremely well fit by the laws of damped oscillatory motion.&#160;This permits analysis of individual E-waves in accordance with a causal mechanism (recoil-initiated suction) that yields three (numerically) unique lumped parameters whose physiologic analogues are chamber stiffness (k), viscoelasticity/relaxation (c), and load (xo).&#160;The recording of transmitral flow (Doppler E-waves) is standard practice in clinical cardiology and, therefore, the echocardiographic recording method is only briefly reviewed. Our focus is on determination of the PDF parameters from routinely recorded E-wave data.&#160;As the highlighted results indicate, once the PDF parameters have been obtained from a suitable number of load varying E-waves, the investigator is free to use the parameters or construct indexes from the parameters (such as stored energy 1/2kxo2, maximum A-V pressure gradient kxo, load independent index of diastolic function, etc.) and select the aspect of physiology or pathophysiology to be quantified.

Accurate, causality-based quantification of global diastolic function has been achieved by kinematic modeling-based analysis of transmitral flow via the Parametrized Diastolic Filling (PDF) formalism. PDF generates unique stiffness, relaxation,&#160;and load parameters and elucidates &#39;new&#39; physiology while providing sensitive and specific indexes of dysfunction.

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians.&#160;Although historically invasive methods have comprised ...

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis

Because protein variants play critical roles in many diseases including TDP-43 in Amyotrophic Lateral Sclerosis (ALS), alpha-synuclein in Parkinson&#8217;s disease and beta-amyloid and tau in Alzheimer&#8217;s disease, it is critically important to develop morphology specific reagents that can selectively target these disease-specific protein variants to study the role of these variants in disease pathology and for potential diagnostic and therapeutic applications. We have developed novel atomic force microscopy (AFM) based biopanning techniques that enable isolation of reagents that selectively recognize disease-specific protein variants. There are two key phases involved in the process, the negative and positive panning phases. During the negative panning phase, phages that are reactive to off-target antigens are eliminated through multiple rounds of subtractive panning utilizing a series of carefully selected off-target antigens. A key feature in the negative panning phase is utilizing AFM imaging to monitor the process and confirm that all undesired phage particles are removed. For the positive panning phase, the target antigen of interest is fixed on a mica surface and bound phages are eluted and screened to identify phages that selectively bind the target antigen. The target protein variant does not need to be purified providing the appropriate negative panning controls have been used. Even target protein variants that are only present at very low concentrations in complex biological material can be utilized in the positive panning step. Through application of this technology, we acquired antibodies to protein variants of TDP-43 that are selectively found in human ALS brain tissue. We expect that this protocol should be applicable to generating reagents that selectively bind protein variants present in a wide variety of different biological processes and diseases.

Using atomic force microscopy in combination with biopanning technology we created a negative and positive biopanning system to acquire antibodies against disease-specific protein variants present in any biological material, even at low concentrations. We were successful in obtaining antibodies to TDP-43 protein variants involved in Amyotrophic Lateral Sclerosis.

Because protein variants play critical roles in many diseases including TDP-43 in Amyotrophic Lateral Sclerosis (ALS), alpha-synuclein in ...

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

T-cells are remarkably specific and effective when recognizing antigens in the form of peptides embedded in MHC molecules (pMHC) on the surface of Antigen Presenting Cells (APCs). This is despite T-cell antigen receptors (TCRs) exerting usually a moderate affinity (&#181;M range) to antigen when binding is measured in vitro1. In view of the molecular and cellular parameters contributing to T-cell antigen sensitivity, a microscopy-based methodology has been developed as a means to monitor TCR-pMHC binding in situ, as it occurs within the synapse of a live T-cell and an artificial and functionalized glass-supported planar lipid bilayer (SLB), which mimics the cell membrane of an Antigen presenting Cell (APC) 2. Measurements are based on F&#246;rster Resonance Energy Transfer (FRET) between a blue- and red-shifted fluorescent dye attached to the TCR and the pMHC. Because the efficiency of FRET is inversely proportional to the sixth power of the inter-dye distance, one can employ FRET signals to visualize synaptic TCR-pMHC binding. The sensitive of the microscopy approach supports detection of single molecule FRET events. This allows to determine the affinity and off-rate of synaptic TCR-pMHC interactions and in turn to interpolate the on-rate of binding. Analogous assays could be applied to measure other receptor-ligand interactions in their native environment.

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

This manuscript describes how to conduct (single molecule) F&#246;rster Resonance Energy Transfer (FRET)- based assays to measure the binding dynamics between T-cell antigen receptor (TCR) and antigenic peptide-loaded MHC molecules as they occur within the immunological synapse of a T-cell in contact with a functionalized planar supported lipid bilayer.

T-cells are remarkably specific and effective when recognizing antigens in the form of peptides embedded in MHC molecules (pMHC) on the surface of Antigen ...

An In Vitro Organ Culture Model of the Murine Intervertebral Disc

Intervertebral disc (IVD) degeneration is a significant contributor to low back pain. The IVD is a fibrocartilaginous joint that serves to transmit and dampen loads in the spine. The IVD consists of a proteoglycan-rich nucleus pulposus (NP) and a collagen-rich annulus fibrosis (AF) sandwiched by cartilaginous end-plates. Together with the adjacent vertebrae, the vertebrae-IVD structure forms a functional spine unit (FSU). These microstructures contain unique cell types as well as unique extracellular matrices. Whole organ culture of the FSU preserves the native extracellular matrix, cell differentiation phenotypes, and cellular-matrix interactions. Thus, organ culture techniques are particularly useful for investigating the complex biological mechanisms of the IVD. Here, we describe a high-throughput approach for culturing whole lumbar mouse FSUs that provides an ideal platform for studying disease mechanisms and therapies for the IVD. Furthermore, we describe several applications that utilize this organ culture method to conduct further studies including contrast-enhanced microCT imaging and three-dimensional high-resolution finite element modeling of the IVD.

An In Vitro Organ Culture Model of the Murine Intervertebral Disc

Whole organ culture of the intervertebral disc (IVD) preserves the native extracellular matrix, cell phenotypes, and cellular-matrix interactions. Here we describe an IVD culture system using mouse lumbar and caudal IVDs in their functional spinal units and several applications utilizing this system.

Intervertebral disc (IVD) degeneration is a significant contributor to low back pain. The IVD is a fibrocartilaginous joint that serves to transmit and ...

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

For over a hundred years, the histological study of tissues has been the gold standard for medical diagnosis because histology allows all cell types in every tissue to be identified and characterized. Our laboratory is actively working to make technological advances in X-ray micro-computed tomography (micro-CT) that will bring the diagnostic power of histology to the study of full tissue volumes at cellular resolution (i.e., an X-ray Histo-tomography modality). Toward this end, we have made targeted improvements to the sample preparation pipeline. One key optimization, and the focus of the present work, is a straightforward method for rigid embedding of fixed and stained millimeter-scale samples. Many of the published methods for sample immobilization and correlative micro-CT imaging rely on placing the samples in paraffin wax, agarose, or liquids such as alcohol. Our approach extends this work with custom procedures and the design of a 3-dimensional printable apparatus to embed the samples in an acrylic resin directly into polyimide tubing, which is relatively transparent to X-rays. Herein, sample preparation procedures are described for the samples from 0.5 to 10 mm in diameter, which would be suitable for whole zebrafish larvae and juveniles, or other animals and tissue samples of similar dimensions. As proof of concept, we have embedded the specimens from Danio, Drosophila, Daphnia, and a mouse embryo; representative images from 3-dimensional scans for three of these samples are shown. Importantly, our methodology leads to multiple benefits including rigid immobilization, long-term preservation of laboriously-created resources, and the ability to re-interrogate samples.

We developed protocols and designed a custom apparatus to enable embedding of millimeter-scale specimens. We present sample preparation procedures with an emphasis on embedding in acrylic resin and polyimide tubing to achieve rigid immobilization and long-term storage of specimens for the interrogation of tissue architecture and cell morphology by micro-CT.

For over a hundred years, the histological study of tissues has been the gold standard for medical diagnosis because histology allows all cell types in ...

Manufacturing of a Nafion-coated, Reduced Graphene Oxide/Polyaniline Chemiresistive Sensor to Monitor pH in Real-time During Microbial Fermentation

Here, we report the engineering of a solid-state micro pH sensor based on polyaniline-functionalized, electrochemically reduced graphene oxide (ERGO-PA). Electrochemically reduced graphene oxide acts as the conducting layer and polyaniline acts as a pH-sensitive layer. The pH-dependent conductivity of polyaniline occurs by doping of holes during protonation and by the dedoping of holes during deprotonation. We found that an ERGO-PA solid-state electrode was not functional as such in fermentation processes. The electrochemically active species that the bacteria produce during the fermentation process interfere with the electrode response. We successfully applied Nafion as a proton-conducting layer over ERGO-PA. The Nafion-coated electrodes (ERGO-PA-NA) show a good sensitivity of 1.71 &#937;/pH (pH 4 - 9) for chemiresistive sensor measurements. We tested the ERGO-PA-NA electrode in real-time in the fermentation of Lactococcus lactis. During the growth of L. lactis, the pH of the medium changed from pH 7.2 to pH 4.8 and the resistance of the ERGO-PA-NA solid-state electrode changed from 294.5 &#937; to 288.6 &#937; (5.9 &#937; per 2.4 pH unit). The pH response of the ERGO-PA-NA electrode compared with the response of a conventional glass-based pH electrode shows that reference-less solid-state microsensor arrays operate successfully in a microbiological fermentation.

Here, we report the protocol for the fabrication of a Nafion-coated, polyaniline-functionalized, electrochemically reduced graphene oxide chemiresistive micro pH sensor. This chemiresistor-based, solid-state micro pH sensor can detect pH changes in real-time during a Lactococcus lactis fermentation process.

Here, we report the engineering of a solid-state micro pH sensor based on polyaniline-functionalized, electrochemically reduced graphene oxide (ERGO-PA). ...