We would like to acknowledge Anthony Bowen whose slides were used as a second human side-by-side comparison as well as Sabrina Nolan whose slides were used as a third human side-by-side and second microscope comparison.

1. Preparation of India Ink Slide
<ol>
	<li>Pipette 10 &#181;L of cryptococcal sample onto a slide. Any circular yeast strain will work but for this experiment H99 was the only strain used. 
	NOTE: If the sample is directly from culture media, diluting 1:2 with PBS or water can help prevent the India Ink from clumping.</li>
	<li>Pipette 2 &#181;L of India Ink stain onto the sample and mix by physically pushing the pipette tip to the sample and moving in a circular motion until the India Ink appears evenly distributed.</li>
	<li>Place a coverslip over the sample by holding down the left edge of the coverslip against the surface of the slide, then gently and evenly lowering the opposite side of the coverslip over the sample.</li>
	<li>Allow the slide to air dry for 5 min.</li>
	<li>Gently apply a light layer of nail polish to coverslip border to form a seal and preserve the India Ink stain.</li>
</ol>
2. Imaging Slide
<ol>
	<li>Place slides in a bright field microscope with a camera attachment and known pixel-to-micron conversion. Adjust filters, objectives, and contrast so that cell capsules are clear and cell bodies are focused, dark bands. 
	NOTE: Various filters, objectives, and contrast settings will work but a 20x objective, Ph1 filter, and 2x2 binning is recommended.</li>
	<li>Ensure the field of view is dense but not overpopulated with cryptococcal cells, with clear contrast between cell capsule and background, and properly focused with the cell body visualized as a dark band. 
	NOTE: The exact number of cells for an optimal image will vary depending on the sample and objective being used. The important aspects are to ensure cells are not clustered or overlapping, that the cell focal planes do not vary significantly, and that there is significant India Ink stain clearly visible in the background (at least 25% of the field).</li>
	<li>Save images to a single directory with clear titles, as the measurement algorithm will be run on images in a single directory and the output data will be organized according to the names of the image files.</li>
</ol>
3. Algorithm Setup
<ol>
	<li>Install Python version 2.7 from</li>
	<li>Install additional python libraries by running the commands &#34;pip install pillow&#34; and &#34;pip install openpyxl&#34;.</li>
	<li>Install MATLAB by following the instructions at</li>
	<li>Build the MATLAB python library by following the instructions provided at https://www.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html.</li>
	<li>Download the three required files included in this manuscript&#39;s Supplementary Materials (&#34;QCA.py&#34;, &#34;Analysis2.m&#34;, and &#34;TestRun.m&#34;). 
	Note: These files can be extracted to any location, but all three must be in the same directory.</li>
</ol>
4. Use of Algorithm
<ol>
	<li>Run the application by double clicking on QCA.py. 
	&#8203;NOTE: The application may take several minutes to start. The &#34;QCA.py&#34; file contains the program structure that calls the &#34;.m&#34; files to run the actual algorithm.</li>
	<li>Follow the steps outlined in the program.
	<ol>
		<li>Input the extension type of the image files preceded by a period and separated by semicolons (ex. &#34;.TIF;.jpeg&#34;) then click the Enter button.</li>
		<li>Choose the directory in which the image files are located by clicking the Select Directory button and selecting the folder which contains the images.</li>
		<li>Generate the list of image files in the directory by clicking the Generate Image List button. The images will be listed in the text box to the right. Review and ensure the list is accurate and complete.</li>
		<li>Select a random image from the list to use as a preview by clicking the Select Random Image button. 
		NOTE: If the image is unable to open due to an &#34;incorrect image mode error,&#34; the algorithm will still function properly despite not displaying the image. After step 4.2.7, the test image will still display.</li>
		<li>Input microscope objective and binning settings. If the default settings do not match the microscope used, select &#34;Custom Pixel Conversion&#34; and input the pixel-to-um conversion for the image files. Once selected, click the Calculate Conversion button and ensure the conversion is correct according to the text box on the right. 
		&#8203;NOTE: Representative images shown in Figure 1 were calculated with 40x magnification and 2x2 binning selected.</li>
		<li>Input algorithm parameters for circle detection.
		<ol>
			<li>Input the minimum and maximum radius detectable for the outer capsule detection as the Min and Max Capsule Radius entries. A smaller range will allow more accurate results.</li>
			<li>Input the minimum and maximum radius detectable for cell body detection as the Min and Max Cell Body Radius entries. 
			NOTE: All four of these entries should be numbers representing their respective values in pixels, according to the source images.</li>
			<li>Move the Capsule and Cell Body Sensitivity sliders to adjust the sensitivity threshold of the algorithm. A low sensitivity will be strict and reduce false positive circle detection, but may also detect fewer real circles. Conversely, a high sensitivity will increase the detection rate but may also result in false positive circles. 
			&#8203;NOTE: Representative results were obtained with a minimum capsule radius of 7, maximum capsule radius of 45, minimum body radius of 4, maximum body radius of 30, capsule sensitivity of 87, and body sensitivity of 87.</li>
		</ol>
		</li>
		<li>Test the parameters on the randomly selected image by clicking the Run Test button. The results will be displayed in the upper center of the program, replacing the original image. If the results look accurate, it is recommended to select an additional random image to try as well to ensure the selected parameters will fit all selected images.
		<ol>
			<li>Maximize the number of bodies and capsules detected and visually inspect whether the circles appear to fit correctly. The number of bodies within capsules detected will be displayed in the text box to the right. Otherwise, manipulate the algorithm parameters until the results are accurate. 
			NOTE: The thick colored circles are only to help with visualization. The actual circles generated for measurement is a mathematical curve calculated by the HCT algorithm.</li>
		</ol>
		</li>
		<li>Run the detection algorithm on the entire directory of image files by clicking the Begin Analysis button. Each image will be analyzed and the program will display &#34;finished&#34; in the text box to the right when all images have been analyzed.</li>
		<li>Click the Match and Cleanup button. This will match detected cell bodies to the detected capsules they reside in, and calculate the true capsule radius by subtracting body from capsule. 
		NOTE: If the algorithm is being used to detect fluorescence or another situation in which only one circle is detected this step is unnecessary. Instead click the Only Pull Bodies or the Only Pull Capsules button to collect the data for only the blue or only the green circles, respectively. It is important to note that if only the capsule data is retrieved the radius will refer to the total radius of the cell as the radius of the cell body could not be subtracted.</li>
		<li>Locate the completed data in the &#34;CleanedOuput.csv&#34; file in the image directory. If only one piece of data was selected, the file will be labeled &#34;CleanedBodies.csv&#34; or &#34;CleanedCapsules.csv&#34;.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Park, B. J., Wannemuehler, K. A., Marston, B. J., Govender, N., Pappas, P. G., Chiller, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+the+current+global+burden+of+cryptococcal+meningitis+among+persons+living+with+HIV/AIDS.">Estimation of the current global burden of cryptococcal meningitis among persons living with HIV/AIDS.</a> AIDS. 23 (4), 525-530 (2009).</li><li>Kwon-Chung, K. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryptococcus+neoformans+and+Cryptococcus+gattii,+the+etiologic+agents+of+cryptococcosis.">Cryptococcus neoformans and Cryptococcus gattii, the etiologic agents of cryptococcosis.</a> Cold Spring Harb Perspect Med. 4 (7), 019760 (2014).</li><li>Granger, D. L., Perfect, J. R., Durack, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virulence+of+Cryptococcus+neoformans.+Regulation+of+capsule+synthesis+by+carbon+dioxide.">Virulence of Cryptococcus neoformans. Regulation of capsule synthesis by carbon dioxide.</a> J Clin Invest. 76 (2), 508 (1985).</li><li>Rumbaugh, J., Pool, A., Gainey, L., Forrester, K., Wu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Role+of+Cryptococcal+Capsule+in+Pathogenesis+of+Cryptococcal+Meningitis.">The Role of Cryptococcal Capsule in Pathogenesis of Cryptococcal Meningitis.</a> Neurology. 80 (7), 007 (2013).</li><li>Bojarczuk, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryptococcus+neoformans+Intracellular+Proliferation+and+Capsule+Size+Determines+Early+Macrophage+Control+of+Infection.">Cryptococcus neoformans Intracellular Proliferation and Capsule Size Determines Early Macrophage Control of Infection.</a> Sci Rep. 6, (2016).</li><li>Robertson, E. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryptococcus+neoformans+Ex+Vivo+Capsule+Size+Is+Associated+With+Intracranial+Pressure+and+Host+Immune+Response+in+HIV-associated+Cryptococcal+Meningitis.">Cryptococcus neoformans Ex Vivo Capsule Size Is Associated With Intracranial Pressure and Host Immune Response in HIV-associated Cryptococcal Meningitis.</a> J Infect Dis. 209 (1), 74-82 (2014).</li><li>Araujo, G. d. e. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Capsules+from+Pathogenic+and+Non-Pathogenic+Cryptococcus+spp.+Manifest+Significant+Differences+in+Structure+and+Ability+to+Protect+against+Phagocytic+Cells.">Capsules from Pathogenic and Non-Pathogenic Cryptococcus spp. Manifest Significant Differences in Structure and Ability to Protect against Phagocytic Cells.</a> PLoS One. 7 (1), 29561 (2012).</li><li>Garc&#237;a-Rivera, J., Chang, Y. C., Kwon-Chung, K. J., Casadevall, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryptococcus+neoformans+CAP59+(or+Cap59p)+Is+Involved+in+the+Extracellular+Trafficking+of+Capsular+Glucuronoxylomannan.">Cryptococcus neoformans CAP59 (or Cap59p) Is Involved in the Extracellular Trafficking of Capsular Glucuronoxylomannan.</a> Eukaryot Cell. 3 (2), 385-392 (2004).</li><li>Guimar&#227;es, A. J., Frases, S., Cordero, R. J. B., Nimrichter, L., Casadevall, A., Nosanchuk, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryptococcus+neoformans+responds+to+mannitol+by+increasing+capsule+size+in+vitro+and+in+vivo:+Mannitol+impacts+the+structure+of+C.+neoformans+capsule.">Cryptococcus neoformans responds to mannitol by increasing capsule size in vitro and in vivo: Mannitol impacts the structure of C. neoformans capsule.</a> Cell Microbiol. 12 (6), 740-753 (2010).</li><li>Zaragoza, O., Fries, B. C., Casadevall, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Induction+of+Capsule+Growth+in+Cryptococcus+neoformans+by+Mammalian+Serum+and+CO2.">Induction of Capsule Growth in Cryptococcus neoformans by Mammalian Serum and CO2.</a> Infect and Immun. 71 (11), 6155-6164 (2003).</li><li>Rossi, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+Resistance+to+Fluconazole+on+Virulence+and+Morphological+Aspects+of+Cryptococcus+neoformans+and+Cryptococcus+gattii+Isolates.">Impact of Resistance to Fluconazole on Virulence and Morphological Aspects of Cryptococcus neoformans and Cryptococcus gattii Isolates.</a> Front Microbiol. 7, (2016).</li></ol>

The authors have no conflicts of interest to disclose.

The critical steps of this technique are preparing the India Ink slide and acquiring the microscope images. While the algorithm has been successfully tested with a variety of slide and image techniques the recommended protocol is described in this manuscript. The polysaccharide capsule is detected based on the exclusion of India Ink particles from the domain of the capsule as these particles are too large to penetrate the polysaccharide fibril network. India Ink exclusion results in a bright circle on top of a dark backg...

Cryptococcus neoformans is a pathogenic yeast found ubiquitously around the globe that is associated with human disease primarily in immunosuppressed populations. C. neoformans most notably accounts for a significant cause of total annual deaths in sub-Saharan Africa due to infectious disease1. The major clinical manifestation of cryptococcal infection is meningoencephalitis, which follows invasion of the central nervous system by transport in infected macrophages (Trojan horse manner) or direct crossing of the blood-brain barrier. C. neoformans expresses several virulence factors including the ability to replicate at human body temperature, urease activity, melanization, and formation of a polysaccharide capsule2. The polysaccharide capsule is composed of repeating glucuronoxylomannan and glucoronoxylomannangalactan polymers and functions as a protective barrier against factors such as environmental stress and host immune responses2.
Although the size of the cryptococcal polysaccharide capsule size has not consistently been associated with virulence, there is evidence that it is a factor in pathogenesis2,3,4,5,6,7. Capsule size is associated with meningitis pathology6, can affect macrophage ability to control Cryptococcus infection5, and can result in loss of virulence if absent8. Hence, capsule size measurements are common in cryptococcal research, but there is no fieldwide standard for a method of capsule measurement.
Currently, C. neoformans polysaccharide capsule measurement is based on manual measurements of microscopy images, and the exact methods of both image and measurement acquisitions vary across laboratories9,10,11. An immediate concern to this method is that some studies require the acquisition of thousands of individual measurements, which makes maintaining accuracy and reliability difficult. Furthermore, even when the results are published, there is often inadequate description of the measurement method. Many publications do not explain how their measurements were obtained, what focal plane was used, how they determined the threshold for capsule identification, whether they used radius or diameter, whether they used one measurement or averaged several, or other details. Some publications only state their method as which program was used, e.g., &#34;Adobe Photoshop CS3 was used to measure the cells&#34;11. This lack of standardization and reporting detail can make reproducibility difficult if not impossible. Differences in human eyesight, computer brightness, microscope settings, slide lighting, and other factors can vary not only between individuals but between samples, whereas calculations based on ratios of pixel intensity values will remain constant and applicable between samples. This technique was generated in the context of providing a standardized, accurate, rapid, and simple technique to measure capsules sizes for a field in which there was none before.
As previously mentioned, the CHT algorithm is long-established, and scripts to automatically detect circles have been written before. This method improves in two areas where other scripts would fall short. First, simply detecting circles is not enough, because with cryptococcal cells two distinct circles must be detected in relation to each other. This method specifically detects cell bodies within capsules, discriminates between the two, and performs calculations only on the relevant body-capsule pairs. Second, even when following the same protocol, different investigators will end up with different acquired images. By allowing the investigator control over every algorithm parameter, this tool can be adjusted to match a broad range of acquisition methods. There is no need for a standardized scope, objective, filter, and so on.
This technique can be readily applied to any situation in which the investigator needs to detect circles within an image that contrast with their background. Both circles lighter and darker than their background can be detected, counted, and measured using this technique.

<table border="0" cellpadding="0" cellspacing="0" style="width:875px;" width="875">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="63">
			<td height="63" style="height:63px;width:213px;">India Ink</td>
			<td style="width:99px;">Becton, Dickinson and Co.</td>
			<td style="width:119px;">261194</td>
			<td style="width:444px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:213px;">Fisherbrand Superfrost Microscope Slides</td>
			<td style="width:99px;">Fisher Scientific</td>
			<td style="width:119px;">12-550-143</td>
			<td>25x75x1</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:213px;">Fisherfinest Premium Cover Glass</td>
			<td style="width:99px;">Fisher Scientific</td>
			<td style="width:119px;">12-548-B</td>
			<td>22x22-1</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:213px;">Sally Hansen HardasNails Xtreme Wear Nail Polish</td>
			<td style="width:99px;">Sally Hansen</td>
			<td style="width:119px;">N/A</td>
			<td>109 invisible</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">SAB Media</td>
			<td style="width:99px;">Sigma</td>
			<td style="width:119px;">S3306</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">Cryptotoccus neoformans</td>
			<td style="width:99px;">ATCC</td>
			<td style="width:119px;">208821</td>
			<td>H99 strain</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">Olympus AX70 Microscope</td>
			<td style="width:99px;">Olympus</td>
			<td style="width:119px;">AX70TRF</td>
			<td>Discontinued ; Bright Field Microscope</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">Qimaging Retiga 1300</td>
			<td style="width:99px;">Qimaging</td>
			<td style="width:119px;">N/A</td>
			<td>Discontinued ; Camera Microscope Attachment</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">MATLAB</td>
			<td style="width:99px;">MathWorks</td>
			<td style="width:119px;">N/A</td>
			<td>Most recent version recommended</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">Python Programming Language</td>
			<td style="width:99px;">Python</td>
			<td style="width:119px;">N/A</td>
			<td>Version 2 necessary ; 2.7 recommended</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:213px;">Microsoft Excel</td>
			<td style="width:99px;">Microsoft</td>
			<td style="width:119px;">N/A</td>
			<td>Most recent version recommended</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:213px;">Phosphate Buffered Saline (PBS)</td>
			<td style="width:99px;">Sigma</td>
			<td style="width:119px;">P3813</td>
			<td></td>
		</tr>
	</tbody>
</table>

automated measurement of cryptococcal species polysaccharide capsule and cell body

The purpose of this technique is to provide a consistent, accurate, and manageable process for large numbers of polysaccharide capsule measurements.
First, a threshold image is generated based on intensity values uniquely calculated for each image. Then, circles are detected based on contrast between the object and background using the well-established Circle Hough Transformation (CHT) algorithm. Finally, the detected cell capsules and bodies are matched according to center coordinates and radius size, and data is exported to the user in a manageable spreadsheet.
The advantages of this technique are simple but significant. First, because these calculations are performed by an algorithm rather than a human both accuracy and reliability are increased. There is no decline in accuracy or reliability regardless of how many samples are analyzed. Second, this approach establishes a potential standard operating procedure for the Cryptococcus field instead of the current situation where capsule measurement varies by lab. Third, given that manual capsule measurements are slow and monotonous, automation allows rapid measurements on large numbers of yeast cells that in turn facilitates high throughput data analysis and increasingly powerful statistics.
The major limitations of this technique come from how the algorithm functions. First, the algorithm will only generate circles. While Cryptococcus cells and their capsules take on a circular morphology, it would be difficult to apply this technique to non-circular object detection. Second, due to how circles are detected the CHT algorithm can detect enormous pseudo-circles based on the outer edges of several clustered circles. However, any misrepresented cell bodies caught within the pseudo-circle can be easily detected and removed from the resulting data sets.
This technique is meant for measuring the circular polysaccharide capsules of Cryptococcus species based on India Ink bright field microscopy; though it could be applied to other contrast based circular object measurements.

Images are first obtained by microscopy of India Ink slides using a bright field microscope coupled with a camera (Figure 1A). It is important to have cells separated and in sufficiently low density not to overwhelm the field of view, as well as to use enough stain to create contrast between cells and background. As stated in the protocol, the exact number of cells for an optimal image will vary depending on the sample, microscope, and object...

Watch this Scientific Journal Video about Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body at JoVE.com

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

This technique describes an automated batch image processor designed to measure polysaccharide capsule and body radii. While initially designed for Cryptococcus neoformans capsule measurements the automated image processor can also be applied to other contrast based detection of circular objects.

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

The purpose of this technique is to provide a consistent, accurate, and manageable process for large numbers of polysaccharide capsule measurements.
...

automated-measurement-cryptococcal-species-polysaccharide-capsule

Research

JoVE Journal

Immunology and Infection

7.2K Views.  Johns Hopkins Bloomberg School of Public Health. This technique describes an automated batch image processor designed to measure polysaccharide capsule and body radii. While initially designed for Cryptococcus neoformans capsule measurements the automated image processor can also be applied to other contrast based detection of circular objects.

Video: Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not representative of the behavior, status or phenotype of single cells. Due to this new insight the number of single cell studies rises continuously (for recent reviews see 1,2,3). However, many of the single cell techniques applied do not allow monitoring the development and behavior of one specific single cell in time (e.g. flow cytometry or standard microscopy).
Here, we provide a detailed description of a microscopy method used in several recent studies 4, 5, 6, 7, which allows following and recording (fluorescence of) individual bacterial cells of Bacillus subtilis and Streptococcus pneumoniae through growth and division for many generations. The resulting movies can be used to construct phylogenetic lineage trees by tracing back the history of a single cell within a population that originated from one common ancestor. This time-lapse fluorescence microscopy method cannot only be used to investigate growth, division and differentiation of individual cells, but also to analyze the effect of cell history and ancestry on specific cellular behavior. Furthermore, time-lapse microscopy is ideally suited to examine gene expression dynamics and protein localization during the bacterial cell cycle. The method explains how to prepare the bacterial cells and construct the microscope slide to enable the outgrowth of single cells into a microcolony. In short, single cells are spotted on a semi-solid surface consisting of growth medium supplemented with agarose on which they grow and divide under a fluorescence microscope within a temperature controlled environmental chamber. Images are captured at specific intervals and are later analyzed using the open source software ImageJ.

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

This protocol provides a step-by-step procedure to monitor single cell behavior of different bacteria in time using automated fluorescence time-lapse microscopy. Furthermore, we provide guidelines how to analyze the microscopy images.

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not ...

Measurement of &#947;HV68 Infection in Mice

&gamma;-Herpesviruses (&gamma;-HVs) are notable for their ability to establish latent infections of lymphoid cells1. The narrow host range of human &gamma;-HVs, such as EBV and KSHV, has severely hindered detailed pathogenic studies. Murine &gamma;-herpesvirus 68 (&gamma;HV68) shares extensive genetic and biological similarities with human &gamma;-HVs and is a natural pathogen of murid rodents2. As such, evaluation of &gamma;HV68 infection of mice inbred strains at different stages of viral infection provides an important model for understanding viral lifecycle and pathogenesis during &gamma;-HVs infection.
Upon intranasal inoculation, &gamma;HV68 infection results in acute viremia in the lung that is later resolved into a latent infection of splenocytes and other cells, which may be reactivated throughout the life of the host3,4. In this protocol, we will describe how to use the plaque assay to assess infectious virus titer in the lung homogenates on Vero cell monolayers at the early stage (5 - 7 days) of post-intranasal infection (dpi). While acute infection is largely cleared 2 - 3 weeks postinfection, a latent infection of &gamma;HV68 is established around 14 dpi and maintained later on in the spleen of the mice. Latent infection usually affects a very small population of cells in the infected tissues, whereby the virus stays dormant and shuts off most of its gene expression. Latently-infected splenocytes spontaneously reactivate virus upon explanting into tissue culture, which can be recapitulated by an infectious center (IC) assay to determine the viral latent load. To further estimate the amount of viral genome copies in the acutely and/or latently infected tissues, quantitative real-time PCR (qPCR) is used for its maximal sensitivity and accuracy. The combined analyses of the results of qPCR and plaque assay, and/or IC assay will reveal the spatiotemporal profiles of viral replication and infectivity in vivo.

&#947;-Herpesviruses (&#947;-HVs) establish life-long persistency in their host. Infection of mice with &#947;-HV68 provides a genetically tractable in vivo model for the characterization of the lifecycle/pathogenesis of &#947;HVs. This protocol describes the detection and quantitation of &#947;HV68 infection at acute and latent stages following infection by plaque-forming, infectious center, and qPCR assays.

&gamma;-Herpesviruses (&gamma;-HVs) are notable for their ability to establish latent infections of lymphoid cells1. The narrow host range of human ...

Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System

The adoptive transfer of pathogen-specific T cells can be used to prevent and treat opportunistic infections such as cytomegalovirus (CMV) infection occurring after allogeneic hematopoietic stem-cell transplantation. Viral-specific T cells from allogeneic donors, including third party donors, can be propagated ex vivo in compliance with current good manufacturing practice (cGMP), employing repeated rounds of antigen-driven stimulation to selectively propagate desired T cells. The identification and isolation of antigen-specific T cells can also be undertaken based upon the cytokine capture system of T cells that have been activated to secrete gamma-interferon (IFN-&#947;). However, widespread human application of the cytokine capture system (CCS) to help restore immunity has been limited as the production process is time-consuming and requires a skilled operator. The development of a second-generation cell enrichment device such as CliniMACS Prodigy now enables investigators to generate viral-specific T cells using an automated, less labor-intensive system. This device separates magnetically labeled cells from unlabeled cells using magnetic activated cell sorting technology to generate clinical-grade products, is engineered as a closed system and can be accessed and operated on the benchtop. We demonstrate the operation of this new automated cell enrichment device to manufacture CMV pp65-specific T cells obtained from a steady-state apheresis product obtained from a CMV seropositive donor. These isolated T cells can then be directly infused into a patient under institutional and federal regulatory supervision. All the bio-processing steps including removal of red blood cells, stimulation of T cells, separation of antigen-specific T cells, purification, and washing are fully automated. Devices such as this raise the possibility that T cells for human application can be manufactured outside of dedicated good manufacturing practice (GMP) facilities and instead be produced in blood banking facilities where staff can supervise automated protocols to produce multiple products.

The goal of this protocol is to manufacture pathogen-specific clinical-grade T cells using a bench-top, automated, second generation cell enrichment device that incorporates a closed cytokine capture system and does not require dedicated staff or use of a GMP facility. The cytomegalovirus pp65-specific-T cells generated can be directly administered to patients.

The adoptive transfer of pathogen-specific T cells can be used to prevent and treat opportunistic infections such as cytomegalovirus (CMV) infection ...

Highly Sensitive Assay for Measurement of Arenavirus-cell Attachment

Arenaviruses are a family of enveloped RNA viruses that cause severe human disease. The first step in the arenavirus life cycle is attachment of viral particles to host cells. While virus-cell attachment can be measured through the use of virions labeled with biotin, radioactive isotopes, or fluorescent dyes, these approaches typically require high multiplicities of infection (MOI) to enable detection of bound virus. We describe a quantitative (q)RT-PCR-based assay that measures Junin virus strain Candid 1 attachment via quantitation of virion-packaged viral genomic RNA. This assay has several advantages including its extreme sensitivity and ability to measure attachment over a large dynamic range of MOIs without the need to purify or label input virus. Importantly, this approach can be easily tailored for use with other viruses through the use of virus-specific qRT-PCR reagents. Further, this assay can be modified to permit measurement of particle endocytosis and genome uncoating. In conclusion, we describe a simple, yet robust assay for highly sensitive measurement of arenavirus-cell attachment.

The first step in the arenavirus life cycle is attachment of viral particles to host cells. We report a quantitative (q)RT-PCR-based assay for ultrasensitive detection and quantitation of arenavirus attachment events.

Arenaviruses are a family of enveloped RNA viruses that cause severe human disease. The first step in the arenavirus life cycle is attachment of viral ...

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

The measurement of immunological reactivity to donor antigens in transplant recipients is likely to be crucial for the successful reduction or withdrawal of immunosuppression. The mixed leukocyte reaction (MLR), limiting dilution assays, and trans-vivo delayed-type hypersensitivity (DTH) assay have all been applied to this question, but these methods have limited predictive ability and/or significant practical limitations that reduce their usefulness. Imaging flow cytometry is a technique that combines the multiparametric quantitative powers of flow cytometry with the imaging capabilities of fluorescent microscopy. We recently made use of an imaging flow cytometry approach to define the proportion of recipient T cells capable of forming mature immune synapses with donor antigen-presenting cells (APCs). Using a well-characterized mouse heart transplant model, we have shown that the frequency of in vitro immune synapses among T-APC membrane contact events strongly predicted allograft outcome in rejection, tolerance, and a situation where transplant survival depends on induced regulatory T cells. The frequency of T-APC contacts increased with T cells from mice during acute rejection and decreased with T cells from mice rendered unresponsive to alloantigen. The addition of regulatory T cells to the in vitro system reduced prolonged T-APC contacts. Critically, this effect was also seen with human polyclonally expanded, naturally occurring regulatory T cells, which are known to control the rejection of human tissues in humanized mouse models. Further development of this approach may allow for a deeper characterization of the alloreactive T-cell compartment in transplant recipients. In the future, further development and evaluation of this method using human cells may form the basis for assays used to select patients for immunosuppression minimization, and it can be used to measure the impact of tolerogenic therapies in the clinic.

This paper describes a method for measuring alloreactivity in a mixed population of T cells using imaging flow cytometry.

The measurement of immunological reactivity to donor antigens in transplant recipients is likely to be crucial for the successful reduction or withdrawal ...

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Low high-density lipoprotein cholesterol (HDL-C) levels are one of the most powerful independent negative predictors of atherosclerotic cardiovascular disease (CVD). The structure and function of HDL rather than HDL-C may more accurately predict atherosclerosis. Several HDL protein and lipid compositional changes that impair HDL function occur in inflammatory states such as atherosclerosis. HDL function is usually determined by cell based assays such as cholesterol efflux assay but these assays have numerous drawbacks lack of standardization. Cell-free assays may give more robust measures of HDL function compared to cell-based assays. HDL oxidation impairs HDL function. HDL has a major role in lipid peroxide transport and high amount of lipid peroxides is related to abnormal HDL function. Lipid-probe interactions should be considered when interpreting the results of non-enzymatic fluorescence assays for measuring the lipid oxidative state. This motivated us to develop a cell-free biochemical enzymatic method to assess HDL lipid peroxide content (HDLox) that contributes to HDL dysfunction. This method is based on the enzyme horseradish peroxidase (HRP) and the fluorochrome Amplex Red that can quantify (without cholesterol oxidase) the lipid peroxide content per mg of HDL-C. Here a protocol is describedfor determination of HDL-lipid peroxidation using the fluorochrome reagent. Assay variability can be reduced by strict standardization of experimental conditions. Higher HDLox values are associated with reduced HDL antioxidant function. The readout of this assay is associated with readouts of validated cell-based assays, surrogate measures of cardiovascular disease, systemic inflammation, immune dysfunction, and associated cardiovascular and metabolic risk phenotypes. This technical approach is a robust method to assess HDL function in human disease where systemic inflammation, oxidative stress and oxidized lipids have a key role (such as atherosclerosis).

We describe here a fluorometric cell-free biochemical assay for determination of HDL-lipid peroxidation. This rapid and reproducible assay can be used to determine HDL function in large scale studies and can contribute to our understanding of HDL function in human disease.

Low high-density lipoprotein cholesterol (HDL-C) levels are one of the most powerful independent negative predictors of atherosclerotic cardiovascular ...

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

Candida albicans is the most common fungal pathogen of humans, causing about 15% of hospital-acquired sepsis cases. A major virulence attribute of C. albicans is its ability to form biofilms, structured communities of cells attached to biotic and abiotic surfaces. C. albicans biofilms can form on host tissues, such as mucosal layers, and on medical devices, such as catheters, pacemakers, dentures, and joint prostheses. Biofilms pose significant clinical challenges because they are highly resistant to physical and chemical perturbations, and can act as reservoirs to seed disseminated infections. Various in vitro assays have been utilized to study C. albicans biofilm formation, such as microtiter plate assays, dry weight measurements, cell viability assays, and confocal scanning laser microscopy. All of these assays are single end-point assays, where biofilm formation is assessed at a specific time point. Here, we describe a protocol to study biofilm formation in real-time using an automated microfluidic device under laminar flow conditions. This method allows for the observation of biofilm formation as the biofilm develops over time, using customizable conditions that mimic those of the host, such as those encountered in vascular catheters. This protocol can be used to assess the biofilm defects of genetic mutants as well as the inhibitory effects of antimicrobial agents on biofilm development in real-time.

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

This protocol describes the use of a customizable automated microfluidic device to visualize biofilm formation in Candida albicans under host physiological conditions.

Candida albicans is the most common fungal pathogen of humans, causing about 15% of hospital-acquired sepsis cases. A major virulence attribute of C. ...

Size Matters: Measurement of Capsule Diameter in Cryptococcus neoformans

The polysaccharide capsule of Cryptococcus neoformans is the primary virulence factor and one of the most commonly studied aspects of this pathogenic yeast. Capsule size can vary widely between strains, has the ability to grow rapidly when introduced to stressful or low nutrient conditions, and has been positively correlated with strain virulence. For these reasons, the size of the capsule is of great interest to C. neoformans researchers. The growth of the C. neoformans capsule is induced during phenotypic testing to help understand the effects of different treatments on the yeast or size differences between strains. Here we describe one of the standard methods of capsule induction and compare two accepted methods of staining and measuring capsule diameter: (i) India ink, a negative stain, used in conjunction with conventional light microscopy and (ii) co-staining with fluorescent dyes of both the cell wall and capsule followed by confocal microscopy. Finally, we show how measurement of capsule diameter from India ink-stained samples can be automated using computational image analysis.

Size Matters: Measurement of Capsule Diameter in Cryptococcus neoformans

The polysaccharide capsule is the primary virulence factor in Cryptococcus neoformans, and its size correlates with strain virulence. Capsule diameter measurements are used in phenotypic testing and to gauge therapeutic efficacy. Here a standard method of capsule induction is presented, and two methods of staining and measuring diameter are compared.

The polysaccharide capsule of Cryptococcus neoformans is the primary virulence factor and one of the most commonly studied aspects of this pathogenic ...

Mouse Body Temperature Measurement Using Infrared Thermometer During Passive Systemic Anaphylaxis and Food Allergy Evaluation

Mouse body temperature measurement is of paramount importance for investigating allergies and anaphylactic symptoms. Rectal probes for temperature readings is common, and they have been proven to be accurate and invaluable in this regard. However, this method of temperature measurement requires the mice to be anesthetized in order to insert the probe without injury to the animal. This limits the ability to observe other phenotypes of the mouse simultaneously. In order to investigate other phenotypes while measuring temperatures, rectal probes are not ideal, and another method is desired. Here, we introduce a noninvasive method of temperature measurement that foregoes the requirement for mouse anesthesia while maintaining equal reliability to rectal probes in measuring body temperature. We use an infrared thermometer that detects body surface temperatures at ranges between 2 and 150 mm. This method of body temperature measurement is successful in reliably replicating temperature change trends during passive system anaphylaxis experiments in mice. We show that body surface temperatures are about 2.0 &#176;C lower than rectal probe measurements, but the degree of temperature drop follows the same trend. Furthermore, we use the same technique to observe mice in a food allergy model to evaluate temperature and activity levels simultaneously.

Here we present a new method to accurately measure body temperature differences in passive systemic anaphylaxis (PSA) and food allergy mouse models using an infrared thermometer. This procedure has been accurately duplicated in previous PSA results.

Mouse body temperature measurement is of paramount importance for investigating allergies and anaphylactic symptoms. Rectal probes for temperature ...

In Vivo Imaging of Reactive Oxygen Species in a Murine Wound Model

The generation of reactive oxygen species (ROS) is a hallmark of inflammatory processes, but in excess, oxidative stress is widely implicated in various pathologies such as cancer, atherosclerosis and diabetes. We have previously shown that dysfunction of the Nuclear factor (erythroid-derived 2)-like 2 (Nrf2)/ Kelch-like erythroid cell-derived protein 1 (Keap1) signaling pathway leads to extreme ROS imbalance during cutaneous wound healing in diabetes. Since ROS levels are an important indicator of progression of wound healing, specific and accurate quantification techniques are valuable. Several in vitro assays to measure ROS in cells and tissues have been described; however, they only provide a single cumulative measurement per sample. More recently, the development of protein-based indicators and imaging modalities have allowed for unique spatiotemporal analyses. L-012 (C13H8ClN4NaO2) is a luminol derivative that can be used for both in vivo and in vitro chemiluminescent detection of ROS generated by NAPDH oxidase. L-012 emits a stronger signal than other fluorescent probes and has been shown to be both sensitive and reliable for detecting ROS. The time lapse applicability of L-012-facilitated imaging provides valuable information about inflammatory processes while reducing the need for sacrifice and overall reducing the number of study animals. Here, we describe a protocol utilizing L-012-facilitated in vivo imaging to quantify oxidative stress in a model of excisional wound healing using diabetic mice with locally dysfunctional Nrf2/Keap1.

In Vivo Imaging of Reactive Oxygen Species in a Murine Wound Model

We describe a non-invasive in vivo imaging protocol that is streamlined and cost-effective, utilizing L-012, a chemiluminescent luminol-analog, to visualize and quantify reactive oxygen species (ROS) generated in a mouse excisional wound model.

The generation of reactive oxygen species (ROS) is a hallmark of inflammatory processes, but in excess, oxidative stress is widely implicated in various ...