The authors are grateful for funding from the Engineering and Physical Sciences Research Council EP/P015743/1.

1. Instrument setup
<ol>
	<li>Select the instrument or object to be imaged. In this experiment an ultrasonic scaler was imaged. Cavitation bubbles occur around the tips of ultrasonic scalers in water.</li>
	<li>Select a micro positioning stage for the instrument to be imaged with XYZ translation and rotation. Place on a laboratory jack. Attach the instrument handle to the micro positioning stage</li>
	<li>Select an optically transparent water container for imaging. The container used in these experiments was created with glass microscope slides.</li>
	<li>Select an XY stage with a rotation platform. Place on a laboratory jack. Place the water container on the stage and fill with filtered water (reverse osmosis or distilled).</li>
</ol>
2. High-speed camera setup
<ol>
	<li>Select a high-speed camera with the desired frame rate and resolution and a high intensity light source with a fibre light guide.</li>
	<li>Attach a micropositioning sliding plate to the high-speed camera body and connect it to a tripod stand.</li>
	<li>Select a lens with the desired resolution and focal length and attach this to the camera. For this experiment a zoom lens was used at a resolution of 8.4 &#181;m/pixel.</li>
	<li>Fill the imaging tank with water and position the tip of the instrument to be imaged in the water tank in the desired orientation.</li>
	<li>After connecting the camera and loading the live view in the software, use low magnification to focus on the tip of the ultrasonic scaler, repositioning the light source if necessary. Position the instrument and the light source in front of the camera and focus. Adjust to the desired frame rate and brightness. 
	NOTE: A higher light intensity is required for imaging at high frames rates, short shutter speeds and/or high magnifications. Illumination can be provided in reflection mode or transmission mode. In this protocol the illumination is provided in transmission mode (bright field) using a high intensity cold illumination device.</li>
	<li>Set an optimal frame rate and shutter speed for the high-speed camera. In this experiment the frame rate was 6400 fps with a shutter speed of 262 nanoseconds. A short shutter speed is required for fast moving bubbles such as cavitation bubbles to ensure that they are in focus.</li>
	<li>Adjust the magnification of the zoom lens and the intensity of the light source so the background is white without being overexposed.</li>
</ol>
3. Calibration
<ol>
	<li>Record the position of the tip (rotation in x-y stage, rotation angle of instrument for reproducibility).</li>
	<li>To ensure the field of view is consistent for each repeat, choose a reference point and note down the coordinates. In this case the reference point was the tip of the ultrasonic scaler. It can then be repositioned in future experiments in the same place within the field of view.</li>
	<li>If the pixel size is unknown, image a graticule with 10 &#181;m markings at the set magnification and use image analysis software such as Fiji to calculate the resolution.</li>
</ol>
4. High-speed video recording
<ol>
	<li>Image the instrument without cavitation. This will be subtracted from the cavitation images in image analysis when calculating the area of the cavitation bubbles. Save the videos in a format such as TIFF so no image quality is lost.</li>
	<li>Image the instrument operating with cavitation. Ensure there are sufficient frames for accurate analysis, for example 5 repeats with 500 frames each.</li>
</ol>
5. Image processing
<ol>
	<li>Download Fiji19 from the ImageJ website (https://imagej.net/Fiji). An ImageJ macro code has been provided which automatically does the image analysis steps described below and can also be changed to suit the application. The individual steps of the macro are described in steps 5.3-5.5.</li>
	<li>Crop the image to remove any darker areas resulting from uneven illumination, if needed. Ensure that all images are cropped to the same size and at the identical point in the image.</li>
	<li>Convert the images to binary by automatically thresholding using one of the auto thresholds. In this example the minimum auto threshold is used.</li>
	<li>Run the fill holes command to remove any black pixels from inside the bubbles which were falsely segmented.</li>
	<li>Calculate the histogram of the stack to show the number of pixels corresponding to the scaler and the cavitation in each frame.</li>
	<li>In this case the pixels corresponding to the bubbles are white and have value 255. Save these measurements.</li>
	<li>Repeat steps 5.3-5.6 for the video of the instrument operating without the bubbles.</li>
	<li>Calculate the mean area of the ultrasonic scaler tip only from the results of the histogram.</li>
	<li>Subtract the mean area of the instrument from each of the areas calculated from the videos of the bubbles around the scaler. The area of the bubbles is left to measure.</li>
	<li>Visualize by subtracting the binary image of the scaler from the binary image of the scaler with bubbles using the image calculator in Fiji.</li>
	<li>Calculate the mean and standard deviation of the area of the bubbles.</li>
	<li>Convert the values from number of pixels to area (in this case &#181;m2) by multiplying by the pixel size squared. Calculate the size of each pixel by imaging a graticule with the high-speed camera at the same magnification as was used for imaging and use ImageJ to set the scale.</li>
	<li>Plot the data. It is also possible to conduct statistical analysis to show any significant difference in the area of bubbles if comparing different conditions.</li>
</ol>
6. ImageJ macro
<ol>
	<li>In the ImageJ/Fiji menu, go to Plugins &#62; New &#62; Macro. Ensure IJ1 Macro is checked under the language menu and copy and paste the following code. Click run to execute the macro (Supplementary File).</li>
</ol>

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The authors have nothing to disclose.

The technique described in this paper enables imaging of fast-moving microbubbles with high spatial and temporal resolution. It can potentially benefit a wide range of scientific disciplines such as chemical engineering, dentistry and medicine. Engineering applications include imaging cavitation bubbles for cleaning surfaces, or for imaging bubbles in fluidized bed reactors. Biomedical applications include imaging cavitation around medical and dental instruments and imaging biofilm debridement from hard and soft tissue using cavitation bubbles. In this study we demonstrated the technique by imaging cavitation around two different dental ultrasonic scaler tips. The amount of cavitation varies between the two tips tested in this study, with more cavitation clouds observed around the free end of tip 10P. This has previously been linked to vibration amplitude20. The high-speed videos show that the FSI 1000 tip has less vibration, which is likely to be why there is less cavitation around this tip.
One limitation of the image analysis method is that the image subtraction technique to remove the area of the scaler is not completely accurate because the scaler is oscillating and therefore the subtraction may leave some areas of the scaler falsely segmented as bubbles. However, this has been accounted for by averaging the area from a large number of frames (n=2000). This would not be a problem for applications where the object to be subtracted is stationary. For studies where the moving object to be subtracted has a much higher variance, we recommend synchronizing the movements in both videos before subtracting for accurate results. In the current study, we did not synchronize the oscillations but since the vibration was low, we can assume that the oscillations correspond well to each other in these two measurements.
The image thresholding is accurate because the brightfield illumination provides a uniform background with good contrast. It is critical to ensure that the background is uniform and does not contain any other objects which could be falsely segmented. The thresholding method can be modified by using other automatic thresholds to suit the application. Manual thresholding, where the user sets the threshold value, is also possible but is not recommended as it reduces the reproducibility of the results, since different users will select different threshold values.
Image analysis has been used for many other bubble imaging studies. These also use a similar method of backlighting to get optimum contrast between the bubbles and the background, and thresholding to segment the bubbles21,22,23,24. The method shown in the current study can also be generalized to be used for many different bubble imaging applications, which are not limited to only high-speed imaging. High-speed imaging has been used for cavitation bubbles generated in water and also around instruments such as endodontic files and ultrasonic scalers12,25,26,27,28. For example Rivas et al. and Macedo et al. used a high-speed camera attached to a microscope, with illumination provided by a cold light source to image cleaning with cavitation, and to image cavitation around an endodontic file17,29. Bright field illumination provides more contrast between the background and the bubbles, making it possible to use simple segmentation techniques such as thresholding, as demonstrated by Rivas et al. for imaging and quantifying cavitation erosion and cleaning over time29. Dark field illumination makes thresholding more difficult due to the higher variation in grey scales4,30. Image analysis has been used in other studies to gather more information about bubbles1,2. Vyas et al. used a machine learning approach to segment cavitation bubbles around an ultrasonic scaler20. The method described in the current paper is quicker because it uses simple thresholding so it is less computationally intensive, and bubbles occurring above and below the scaler can be analysed. However, the thresholding method used in the current paper is only accurate if the background is uniform. If it is not possible to obtain a uniform background during imaging, other image processing techniques can be used such as the use of background subtraction using a rolling ball radius to correct for uneven illumination, filtering using median or Gaussian filters to remove noise, or also using machine learning based techniques20,31.
In conclusion, we present a high-speed imaging and analysis protocol to image and calculate the area of a microscopic moving object. We have demonstrated this method by imaging cavitation bubbles around an ultrasonic scaler. It can be used for imaging cavitation around other dental instruments such as endodontic files and it can be easily adapted for other non-dental bubble imaging applications.

Imaging the motion of bubbles is important for various applications because it controls the hydrodynamics of a system. There are many applications where this can be useful: in fluidized bed reactors1,2, or for cleaning with cavitation bubbles3,4. The purpose of imaging bubbles is to understand more about the bubble dynamics or about the direction and motion of a cloud of bubbles. This can be done through observing structures imaged and also by using image analysis to obtain quantitative information, such as the size of the bubbles.
Cavitation bubbles are gas or vapor entities that occur in a fluid when the pressure drops below the saturated pressure value5. They can occur when an acoustic field is applied to a fluid at ultrasonic frequencies. They repeatedly grow and collapse, and upon collapse can release energy in the form of high-speed micro-jets and shockwaves6,7. These can dislodge particles on a surface through shear forces and cause surface cleaning8. Cavitation bubbles are being investigated for surface cleaning in different industries, such as for semiconductors, food, and wound cleaning9,10,11,12. They could also be used to clean dental plaque from teeth and biomaterials such as dental implants12,13. Cavitation occurs around currently used dental instruments such as ultrasonic scalers and endodontic files and shows potential as an additional cleaning process with these instruments14.
The oscillation of cavitation bubbles occurs over a few microseconds and therefore a high-speed camera is required to capture their motion by imaging at thousands of frames per second8. We demonstrate a method of imaging microbubble cavitation around dental ultrasonic scalers. The aim is to understand how cavitation varies around different ultrasonic scalers, so it can be optimised as a novel way to clean dental plaque.
Previous methods used to investigate the cavitation include sonochemiluminesence, which uses luminol to detect where cavitation has occurred15,16. However, this is an indirect technique and it is not able to visualize the cavitation bubbles in real time. Therefore, it is not able to accurately determine exactly where it happens on the instrument, and no information can be gained on the bubble dynamics, unless it is combined with other imaging techniques17. High-speed imaging can image not only the cavitation bubbles growing and collapsing but also the type of cavitation occurring: cavitation clouds, microstreamers and micro-jets6,7,18. These give more information about how the cavitation can clean surfaces.
We present a method of imaging cavitation microbubbles using a high-speed camera and calculating the mean area of cavitation occurring. This method is demonstrated using an example of cavitation occurring around different dental ultrasonic scaler tips, although the experimental and image analysis steps can be used for other applications, such as for imaging other macro and microbubbles.

<table border="0" cellpadding="0" cellspacing="0" style="width:733px;" width="733">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">0.25x attachment</td>
			<td style="width:176px;">Navitar</td>
			<td style="width:123px;">1-50011</td>
			<td style="width:172px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:263px;">12x with 12mm fine focus 
			Long distance microscope zoom lens</td>
			<td style="width:176px;">Navitar</td>
			<td style="width:123px;">1-50486</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">2x adaptor with f mount</td>
			<td style="width:176px;">Navitar</td>
			<td style="width:123px;">1-62922</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Cavitron Plus Ultrasonic Scaler</td>
			<td style="width:176px;">Dentsply Sirona</td>
			<td>8184003</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:263px;">Cavitron Ultrasonic Insert FSI 1000FSI 1000</td>
			<td style="width:176px;">Dentsply Sirona</td>
			<td>UCAFTHD</td>
			<td></td>
		</tr>
		<tr height="104">
			<td height="104" style="height:104px;width:263px;">Fibre light guide. 8mm fibre bundle 1500mm length. Focussing lens assembly for Hayashi light, 1/4&#34;-20 tripod 
			thread for mounting.</td>
			<td style="width:176px;">Hayashi</td>
			<td style="width:123px;">LGC1- 
			8L1500</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Geared head</td>
			<td style="width:176px;">Manfrotto</td>
			<td style="width:123px;">MN405</td>
			<td>7.5kg load capacity</td>
		</tr>
		<tr height="104">
			<td height="104" style="height:104px;width:263px;">HDF7010 High-Power LED Endoscope light 
			source. 150W LED provides cold output equivalent to 250W 
			Xenon.</td>
			<td style="width:176px;">Hayashi</td>
			<td style="width:123px;">LA-HDF710</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Heavy weight Tripod</td>
			<td style="width:176px;">Manfrotto</td>
			<td style="width:123px;">MN475B</td>
			<td>Geared centre column, 12kg load capacity</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">High Speed Camera</td>
			<td style="width:176px;">Photron</td>
			<td style="width:123px;">103526</td>
			<td>FASTCAM Mini AX200 900K M3 (16GB memory)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">High-Precision Rotation&#160;Stage</td>
			<td style="width:176px;">Thorlabs</td>
			<td style="width:123px;">PR01/M</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Laboratory jacks</td>
			<td style="width:176px;">Camlab</td>
			<td style="width:123px;">1194083</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Micropositioning sliding plate</td>
			<td style="width:176px;">Manfrotto</td>
			<td>SKU 454</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Micropositioning stage 3D</td>
			<td style="width:176px;">Thorlabs</td>
			<td style="width:123px;">PT3/M</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Micropositioning stage rotation</td>
			<td style="width:176px;">Thorlabs</td>
			<td style="width:123px;">OCT-XYR1/M</td>
			<td>OCT-XYR1/M - XY Stage with Solid Top Plate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">NEWTRON P5 XS Ultrasonic Scaler&#160;</td>
			<td style="width:176px;">Acteon</td>
			<td>F62118</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Ultrasonic Insert 10P</td>
			<td style="width:176px;">Acteon</td>
			<td style="width:123px;">F00253</td>
			<td></td>
		</tr>
	</tbody>
</table>

imaging and quantification of the area of fast-moving microbubbles using a high-speed camera and image analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

The image analysis steps can be seen in Figure 1 for one of the ultrasonic scaler tips tested. A FSI 1000 tip and a 10P tip were imaged inside a water tank with the cooling water turned off (Figure 2). Cavitation occurred near the bend of tip FSI 1000 at maximum power, and near the free end in tip 10P (Figure 3 and Figure 4). The mean area of cavitation was 0.1 &#177; 0.07 mm2 for the FSI 1000 tip and 0.50 &#177; 0.25 mm2 for the 10P tip (Figure 5).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61509/61509fig1.jpg" /> 
Figure 1:&#160;High-speed imaging setup and image analysis steps (a) Schematic of the high-speed imaging setup used in the study. (b) Schematic of the image analysis steps used in the study, showing the raw images on the left of the scaler tip only and with cavitation, which were then binarized and subtracted from each other to calculate the area of the cavitation clouds. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/61509fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61509/61509fig2.jpg" /> 
Figure 2: Comparison between different tips High-speed image stills showing cavitation occurring around the two ultrasonic scaler tips tested (a) FSI 1000 (b) 10P. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/61509fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61509/61509fig3.jpg" /> 
Figure 3: Tip 10P high-speed images: High-speed image stills of tip 10P, from a video taken at 6400 frames per second. Cavitation can be seen around the free end of the tip. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/61509fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61509/61509fig4.jpg" /> 
Figure 4: Tip FSI1000 high-speed images: High-speed image stills of tip FSI 1000, from a video taken at 6400 frames per second. Cavitation can be seen around the middle of the tip. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/61509fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61509/61509fig5.jpg" /> 
Figure 5: Cavitation area image analysis results. The mean area of cavitation occurring around the FSI 1000 and 10P ultrasonic scaler tips calculated using the image analysis technique described. The error bars represent the standard deviation. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/61509fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary&#160;File. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/61509/Jove_macro.ijm" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis at JoVE.com

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...

imaging-quantification-area-fast-moving-microbubbles-using-high-speed

Universidad de Cartagena UDEC

Research

JoVE Journal

Bioengineering

5.8K Views.  University of Birmingham. Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

Video: Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. 
In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at ...

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

The goal of this protocol is to build a parallel high-extinction and high-resolution optical Brillouin spectrometer. Brillouin spectroscopy is a non-contact measurement method that can be used to obtain direct readouts of viscoelastic material properties. It has been a useful tool in material characterization, structural monitoring and environmental sensing. In the past, Brillouin spectroscopy has usually employed scanning Fabry-Perot etalons to perform spectral analysis. This process requires high illumination power and long acquisition times, making the technique unsuitable for biomedical applications. A recently introduced novel spectrometer overcomes this challenge by employing two VIPAs in a cross-axis configuration. This innovation enables sub-Gigahertz (GHz) resolution spectral analysis with sub-second acquisition time and illumination power within the safety limits of biological tissue. The multiple new applications facilitated by this improvement are currently being explored in biological research and clinical application.

Here we present a protocol to build a rapid Brillouin spectrometer. Cascading virtually imaged phase array (VIPA) etalons achieve a measurement speed more than 1,000 times faster than traditional scanning Fabry-Perot spectrometers. This improvement provides the means for Brillouin analysis of tissue and biomaterials at low power levels in vivo.

The goal of this protocol is to build a parallel high-extinction and high-resolution optical Brillouin spectrometer. Brillouin spectroscopy is a ...

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Optical super-resolution imaging with structured illumination microscopy (SIM) is a key technology for the visualization of processes at the molecular level in the chemical and biomedical sciences. Although commercial SIM systems are available, systems that are custom designed in the laboratory can outperform commercial systems, the latter typically designed for ease of use and general purpose applications, both in terms of imaging fidelity and speed. This article presents an in-depth guide to building a SIM system that uses total internal reflection (TIR) illumination and is capable of imaging at up to 10 Hz in three colors at a resolution reaching 100 nm. Due to the combination of SIM and TIRF, the system provides better image contrast than rival technologies. To achieve these specifications, several optical elements are used to enable automated control over the polarization state and spatial structure of the illumination light for all available excitation wavelengths. Full details on hardware implementation and control are given to achieve synchronization between excitation light pattern generation, wavelength, polarization state, and camera control with an emphasis on achieving maximum acquisition frame rate. A step-by-step protocol for system alignment and calibration is presented and the achievable resolution improvement is validated on ideal test samples. The capability for video-rate super-resolution imaging is demonstrated with living cells.

This article provides an in depth guide for the assembly and operation of a structured illumination microscope operating with total internal reflection fluorescence illumination (TIRF-SIM) to image dynamic biological processes with optical super-resolution in multiple colors.

Optical super-resolution imaging with structured illumination microscopy (SIM) is a key technology for the visualization of processes at the molecular ...

A Rapid Image-based Bacterial Virulence Assay Using Amoeba

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria can undergo changes in the physiology due to the exposure to host growth environment and the presence of the host cells. We developed an assay to rapidly measure the virulence state of bacteria that minimize the extent to which bacteria grow in the presence of host cells. Bacteria and amoebae are mixed together and immobilized on a single imaging plane using an agar pad. The procedure uses single-cell fluorescence imaging with calcein-acetoxymethyl ester (calcein-AM) as an indicator of host cell health. The fluorescence of host cells is analyzed after 1 h of exposure of host cells to bacteria using epifluorescence microscopy. Image analysis software is used to compute a host killing index. This method has been used to measure virulence within planktonic and surface-attached Pseudomonas aeruginosa sub-populations during the initial stage of biofilm formation and may be adapted to other bacteria and other stages of biofilm growth. This protocol provides a rapid and robust method of measuring virulence and avoids many of the complexities associated with the growth and maintenance of mammalian cell lines. Virulence phenotypes measured here using amoebae have also been validated using mouse macrophages. In particular, this assay was used to establish that surface attachment upregulates virulence in P. aeruginosa.

Here, we present a protocol to measure the virulence of planktonic or surface-attached bacteria using D. discoideum (amoeba) as a host. Virulence is measured over a period of 1 h and host killing is quantified using fluorescence microscopy and image analysis. We demonstrate this protocol using the bacterium P. aeruginosa.

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria ...

Implementation of Interference Reflection Microscopy for Label-free, High-speed Imaging of Microtubules

There are several methods for visualizing purified biomolecules near surfaces. Total-internal reflection fluorescence (TIRF) microscopy is a commonly used method, but has the drawback that it requires fluorescent labeling, which can interfere with the activity of the molecules. Also, photobleaching and photodamage are concerns. In the case of microtubules, we have found that images of similar quality to TIRF can be obtained using interference reflection microscopy (IRM). This suggests that IRM might be a general technique for visualizing the dynamics of large biomolecules and oligomers in vitro. In this paper, we show how a fluorescence microscope can be modified simply to obtain IRM images. IRM is easier and considerably cheaper to implement than other contrast techniques such as differential interference contrast microcopy or interferometric scattering microscopy. It is also less susceptible to surface defects and solution impurities than darkfield microscopy. Using IRM, together with the image analysis software described in this paper, the field of view and the frame rate is limited only by the camera; with a sCMOS camera and&#160;wide-field&#160;illumination&#160;microtubule length can be measured with precision up to 20 nm with a bandwidth of 10 Hz.&#160;

This protocol is a guide for implementing interference reflection microscopy on a standard fluorescence microscope for label-free, high-contrast, high-speed imaging of microtubules using in vitro surfaces assays.

There are several methods for visualizing purified biomolecules near surfaces. Total-internal reflection fluorescence (TIRF) microscopy is a commonly used ...

The Quantification of Injectability by Mechanical Testing

Injectable biomaterials are becoming increasingly popular for the minimally invasive delivery of drugs and cells. These materials are typically more viscous than traditional aqueous injections and may be semi-solid, therefore, their injectability cannot be assumed. This protocol describes a method to objectively assess the injectability of these materials using a standard mechanical tester. The syringe plunger is compressed by the crosshead at a set rate, and the force is measured. The maximum or plateau force value can then be used for comparison between samples, or to an absolute force limit. This protocol can be used with any material, and any syringe and needle size or geometry. The results obtained may be used to make decisions about formulations, syringe and needle sizes early in the translational process. Further, the effects of altering formulations on injectability may be quantified, and the optimum time to inject temporally changing materials determined. This method is also suitable as a reproducible way to examine the effects of injection on a material, to study phenomena such as self-healing and filter pressing or study the effects of injection on cells. This protocol is faster and more directly applicable to injectability than rotational rheology, and requires minimal post processing to obtain key values for direct comparisons.

Presented here is a protocol for quantitatively evaluating the injectability of a material through a syringe-needle system using a standard mechanical testing rig.

Injectable biomaterials are becoming increasingly popular for the minimally invasive delivery of drugs and cells. These materials are typically more ...

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles

There are many methods that can be used for the production of vaporizable phase-shift droplets for imaging and therapy. Each method utilizes different techniques and varies in price, materials, and purpose. Many of these fabrication methods result in polydisperse populations with non-uniform activation thresholds. Additionally, controlling the droplet sizes typically requires stable perfluorocarbon liquids with high activation thresholds that are not practical in vivo. Producing uniform droplet sizes using low-boiling point gases would be beneficial for in vivo imaging and therapy experiments. This article describes a simple and economical method for the formation of size-filtered lipid-stabilized phase-shift nanodroplets with low-boiling point decafluorobutane (DFB). A common method of generating lipid microbubbles is described, in addition to a novel method of condensing them with high-pressure extrusion in a single step. This method is designed to save time, maximize efficiency, and generate larger volumes of microbubble and nanodroplet solutions for a wide variety of applications using common laboratory equipment found in many biological laboratories.

This protocol describes a method of generating large volumes of lipid encapsulated decafluorobutane microbubbles using probe-tip sonication and subsequently condensing them into phase-shift nanodroplets using high-pressure extrusion and mechanical filtration.

There are many methods that can be used for the production of vaporizable phase-shift droplets for imaging and therapy. Each method utilizes different ...

Multi-timescale Microscopy Methods for the Characterization of Fluorescently-labeled Microbubbles for Ultrasound-Triggered Drug Release

Microbubble contrast agents hold great promise for drug delivery applications with ultrasound. Encapsulating drugs in nanoparticles reduces systemic toxicity and increases circulation time of the drugs. In a novel approach to microbubble-assisted drug delivery, nanoparticles are incorporated in or on microbubble shells, enabling local and triggered release of the nanoparticle payload with ultrasound. A thorough understanding of the release mechanisms within the vast ultrasound parameter space is crucial for efficient and controlled release. This set of presented protocols is applicable to microbubbles with a shell containing a fluorescent label. Here, the focus is on microbubbles loaded with poly(2-ethyl-butyl cyanoacrylate) polymeric nanoparticles, doped with a modified Nile Red dye. The particles are fixed within a denatured casein shell. The microbubbles are produced by vigorous stirring, forming a dispersion of perfluoropropane gas in the liquid phase containing casein and nanoparticles, after which the microbubble shell self-assembles. A variety of microscopy techniques are needed to characterize the nanoparticle-stabilized microbubbles at all relevant timescales of the nanoparticle release process. Fluorescence of the nanoparticles enables confocal imaging of single microbubbles, revealing the particle distribution within the shell. In vitro ultra-high-speed imaging using bright-field microscopy at 10 million frames per second provides insight into the bubble dynamics in response to ultrasound insonation. Finally, nanoparticle release from the bubble shell is best visualized by means of fluorescence microscopy, performed at 500,000 frames per second. To characterize drug delivery in vivo, the triggered release of nanoparticles within the vasculature and their extravasation beyond the endothelial layer is studied using intravital microscopy in tumors implanted in dorsal skinfold window chambers, over a timescale of several minutes. The combination of these complementary characterization techniques provides unique insight into the behavior of microbubbles and their payload release at a range of time and length scales, both in vitro and in vivo.

The presented protocols can be used to characterize the response of fluorescently-labeled microbubbles designed for ultrasound-triggered drug delivery applications, including their activation mechanisms as well as their bioeffects. This paper covers a range of in vitro and in vivo microscopy techniques performed to capture the relevant length and timescales.

Microbubble contrast agents hold great promise for drug delivery applications with ultrasound. Encapsulating drugs in nanoparticles reduces systemic ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...