The study was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research.

The described protocol was employed in accordance with NIH rules on the use of human tissue samples.
1. Tissue Preparation
<ol>
	<li>Prior to beginning, obtain human tissue specimens according to Institutional Review Board (IRB) protocols.</li>
	<li>Choose the type of tissue/cell block and corresponding processing method [formalin-fixed paraffin-embedded (FFPE), frozen, or ethanol-fixed paraffin-embedded (EFPE)]. Formalin fixation provides optimal histology, followed by ethanol fixation and flash frozen. However, fixation and tissue processing methods can affect DNA, RNA, and protein quantity and quality for downstream molecular analysis and should be considered.</li>
	<li>Cut the tissue/cell block sections onto the slide type (glass, membrane glass, or metal frame membrane) of choice.&#160;SIVQ analysis works equally well on all three slide types. Please note that the pseudo-coverslip method (described below) with xylenes and ethanol cannot be performed on the metal frame membrane slides since the slide must be inverted on the stage.</li>
	<li>Select a chemical- or IHC-based tissue stain to identify cells of interest from the background. Note that staining methods can also affect DNA, RNA, and protein quality and quantity of the tissue. Test the tissue after staining to assess baseline quality of biomolecules before proceeding with the protocol. SIVQ-LCM has been performed on tissue/cytology slides stained with: immunohistochemistry (IHC) with DAB15, immunofluorescence, fast red, de novo red, toluidine blue, and hematoxylin and eosin (H&#38;E) (unpublished data).</li>
</ol>
2. Specimen Imaging
<ol>
	<li>Load slides onto the motorized stage of the microdissection instrument and launch the related software. Select the check boxes to designate the positions of the loaded slides and ensure the captured image files are to be saved in jpeg format.</li>
	<li>Optimize the image quality by adjusting the brightness and focus of the image on the screen, using either the manual focus wheel or through the dissection instrument&#8217;s software.
	<ol>
		<li>Using the Image Toolbox within the dissection instrument&#8217;s software, set the lamp brightness and camera gain appropriately. Example values are brightness = 60 and gain = 220, with the diffuser.</li>
		<li>Focus either manually or with the autofocus feature in the software.</li>
	</ol>
	</li>
	<li>Capture a thumbnail overview image of the slide to provide a roadmap for the dissection process.
	<ol>
		<li>For optimal image quality of an uncoverslipped slide, utilize the diffuser below the condenser on the instrument, or, add a small amount (~30 &#956;l) of either ethanol or xylenes to improve the refractory index (pseudo-coverslip). When using xylenes, be aware they are toxic and proper safety measures must be used, including the use of a fume hood and protective lab coat, eye goggles, and gloves.</li>
		<li>Do NOT place the LCM cap on the slide until the ethanol or xylenes solution has completely cleared or the polymer on the cap will be distorted.</li>
	</ol>
	</li>
	<li>Navigate the slide and capture images of the areas to be dissected at 10X, 20X, or 40X magnification. If necessary, enhance the image with software such as AutoCorrect (in Microsoft Office Picture Manager) as previously described22.&#160;Images must be captured in jpeg format to allow them to be re-imported into the automated selection software.</li>
</ol>
3. Algorithm Analysis of the Image
<ol>
	<li>Transfer captured images from the microdissection instrument to the SIVQ folder.&#160;Install and open the ArcturusXT, AutoScan and SIVQ software packages on the computer attached to the dissection instrument. For access to the SIVQ software, please contact Dr. Ulysses Balis (ulysses@med.umich.edu).</li>
	<li>Open SIVQ and load the captured image (jpeg) of interest.</li>
	<li>Navigate to the area of interest and/or adjust the size of the display windows (Viewport 5 &#38; 6).&#160;In the SIVQ software, Viewport 5 shows the pre-processing image and Viewport 6 shows the post-processing image16.</li>
	<li>Choose the size of the ring vector and number of rings to be used.</li>
	<li>Select the predicate image feature to be captured by right clicking on it in Viewport 6.</li>
	<li>Click &#8220;scan&#8221; to analyze the image.</li>
	<li>Adjust the statistical probability of image matching using the two sliding bars.&#160;The upper bar adjusts the overall vector specificity, and is used to exclude area from the initial scan (with sensitivity as defined by the selected &#8220;Stat&#8221; variable) that may represent excessive included area.&#160;Conversely, the lower slider is utilized to increase sensitivity, after a scan is performed, with the intent of increasing area that is classified as being a match.&#160;Both of these slider controls utilize the &#8220;Stat&#8221; variable as the initial baseline sensitivity threshold.</li>
	<li>To save the image, click &#8220;save as jpeg&#8221; (image is now saved in c:/vq_test folder/pics).</li>
	<li>Analyze the image with the algorithm. The output of the algorithm analysis needs to result in an annotated image for it to be used in SIVQ-LCM. At present, the current version of the SIVQ core engine is in beta version testing with the expectation that the full production version (available Q1 2014) will incorporate a full software development kit (SDK) and application programming interface (API) for simplified integration of user-generated spatial filters and downstream workflow data processing steps with the core ring matching engine.&#160;This SDK will be distributed with a complete set of documentation.
	<ol>
		<li>Ensure that the SIVQ heatmap is changed to a uniform red color.</li>
	</ol>
	</li>
	<li>Export the image. It is essential to re-embed the positional coordinates in the post-analysis jpeg image by using a HEX editor to paste in the file header from the original dissection instrument image.&#160;The appropriate data may be found between &#8220;Start of Image&#8221; marker (0xFF, 0xD8) and the first&#160;&#8220;Define Quantization Table&#8221; marker (0xFF, 0xDB).</li>
</ol>
4. Microdissection
<ol>
	<li>Place the LCM cap at the center of the region of interest, where the images were captured for the SIVQ analysis.</li>
	<li>Calibrate and Quality Control (QC) the UV/IR lasers and optimize the parameters, including power, duration, laser locations, and UV cutting speed (as recommended by the manufacturer). Perform these calibrations before re-importing the analyzed image.&#160;</li>
	<li>Open AutoScanXT (automated selection software) and import the analyzed image from c:/vq_test folder/pics.</li>
	<li>Train the automated selection software to recognize the SIVQ annotation and create the dissection map.
	<ol>
		<li>To create a training file, select four regions of interest (marked by &#8220;blue circles&#8221;) on the &#8220;red paint&#8221; of the SIVQ analyzed image.</li>
		<li>Select the background areas (marked by &#8220;red squares&#8221;) that are not to be dissected.</li>
		<li>Click on the &#8220;Analyze&#8221; button to generate the training file, which can be saved for subsequent uses.&#160;</li>
	</ol>
	</li>
	<li>Perform microdissection using the appropriate infrared (IR) and/or ultraviolet (UV) lasers.
	<ol>
		<li>Copy the selected areas onto the &#8220;live&#8221; image.</li>
		<li>In the &#8220;microdissect&#8221; toolbox, select the appropriate IR capture or UV cutting buttons.</li>
	</ol>
	</li>
	<li>After the dissection is complete, move the LCM cap to the QC station and capture an image of the dissected tissue/cells. Another approach is to place the cap on a blank area of the slide prior to moving it to the QC station, this allows the user to take pictures at various magnifications.</li>
	<li>Capture an image of the tissue area of interest after microdissection to further assess lifting efficiency.</li>
	<li>If the desired cells have been dissected successfully, click on the &#34;present stage&#34; button in the ArcturusXT software and remove the LCM cap to initiate the molecular extraction procedure for downstream analysis.</li>
</ol>

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L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laser+capture+microdissection+of+enriched+populations+of+neurons+or+single+neurons+for+gene+expression+analysis+after+traumatic+brain+injury.">Laser capture microdissection of enriched populations of neurons or single neurons for gene expression analysis after traumatic brain injury.</a> J Vis Exp. (74), (2013).</li><li>Iyer, E. P., Cox, D. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laser+capture+microdissection+of+Drosophila+peripheral+neurons.">Laser capture microdissection of Drosophila peripheral neurons.</a> J Vis Exp. (39), (2010).</li><li>Hipp, 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=SIVQ-aided+laser+capture+microdissection:+A+tool+for+high-throughput+expression+profiling.">SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling.</a> Journal of pathology informatics. 2, 19 (2011).</li><li>Hipp, J. D., Cheng, J. Y., Toner, M., Tompkins, R. G., Balis, U. 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Michael R. Emmert-Buck is an inventor on NIH-held patents covering laser capture microdissection and receives royalty-based payments through the NIH Technology Transfer Program.

We present a protocol for the application of SIVQ-LCM to microdissect immunostained epithelial cells from FFPE human breast tissue. The use of an image analysis algorithm, such as SIVQ, reduces the amount of hands-on time required for the microdissection process. This is a potentially important advance for the field since operator time and effort is typically the rate-limiting step for the precise dissection of cells of interest. In the present protocol, we specifically adapted our procedure to the ArcturusXT instrument, although it likely will be possible to adapt SIVQ and other algorithms to other commercially available microdissection instruments as well. In addition to improving efficiency, this new approach may allow users who are non-histopathology experts to perform microdissection. Finally, the use of an algorithm to drive the microdissection process may enable greater reproducibility from site-to-site by removing user subjectivity in identifying cells of interest.
While SIVQ exhibits broad adaptability to multiple classes of image subject matter, including histology, it is important to recognize that it is not intended to serve as a universal image processing/segmentation tool, but rather, as a high-efficiency first-pass foreground selection tool. Used in this capacity, it has high utility for predicting whether or not a particular class of imagery subject matter is suitable for numerical segmentation approaches. When SIVQ is successful, there is high predictive power that either: a) a more refined and targeted image analysis/segmentation approach will be highly successful and/or b) an optimized, machine-selected SIVQ vector will be effective in rendering a clinical-workflow-ready solution. In either circumstance, the use of SIVQ serves as an effective triaging tool to identify imagery subject matter that poses computationally tractable solutions in terms of foreground segmentability.
While we previously described the use of the SIVQ-LCM protocol only for mRNA expression microarray experiments15, we believe the protocol is applicable to most downstream molecular assays, and may be particularly useful for molecular assays that require large numbers of cells, such as proteomics. For these studies, large amounts of cells need to be procured due to the inability to amplify proteins. In addition, the advent of newer technologies, such as Next-Gen Sequencing (NGS), emphasizes the need to begin with as pure of a sample as possible. The ability for SIVQ-LCM to isolate cells via both morphological features and/or staining qualities could enable the ability to more easily isolate pure cell populations.
It is critical to have properly prepared tissue for reproducible results for both the SIVQ analysis and microdissection. For optimal conditions for LCM it is best to perform dissects in a low humidity room and maintain properly dehydrated tissue. In addition, ensure that the staining of the tissue is reproducible. Finally, familiarity with the dissection instrument is essential to successful SIVQ-LCM.
The SIVQ-LCM protocol presented here is the first steps towards semi-automated microdissection, which may have applications in future clinical testing since current dissection methods are not easily capable of handling large workloads. In summary, we believe that SIVQ-LCM is a new foray into the field of automated microdissection and provides a starting point from which investigators and algorithm developers can further improve the technology.

Originally developed in the mid-1990s, laser capture microdissection (LCM) enables the user to precisely capture specific cells or cellular regions from a histological tissue section via microscopic visualization1,2. Many studies comparing molecular analysis of LCM versus tissue scrapes illustrate the value of the method3-12. In addition, there are three video protocol publications on the technology that are available for viewing13,14. However, despite its proven value, LCM can be tedious and laborious when the target of interest is a dispersed cell population in a heterogeneous tissue section, or when large numbers of cells are required for specific downstream applications such as proteomics. The burden placed on the human operator led us to develop a semi-automated dissection approach for LCM by combining a powerful image analysis algorithm to guide the LCM process15.
In collaboration with the University of Michigan, our laboratory at the NIH extended the previously developed and reported Spatially invariant vector quantization (SIVQ) algorithm in a manner to allow it to semi-automate the tissue selection process intrinsic to guided microdissection, thus making available a tool with the pathologist or life scientist in mind. Spatially invariant vector quantization (SIVQ) is an algorithm that allows the user to simply &#8220;click&#8221; on a histological feature of interest to create a ring vector (predicate image feature) that can be used to search the entire histological image, adjusting the statistical threshold as needed16-21. The resultant heat map displays the quality of matches to the initial predicate image feature and is subsequently converted into a single color (red) annotation map that can be imported into the LCM instrument. The automated selection software, AutoScanXT, is then used to draw a map based on SIVQ&#39;s annotation guiding the capture of the target cells from the tissue sample. The detailed protocol below describes the implementation of SIVQ into the microdissection workflow.

<table border="0" cellpadding="0" cellspacing="0" width="586">
	<tbody>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Positive Charged Glass Slides</td>
			<td style="width:84px;">Thermo Scientific</td>
			<td style="width:119px;">4951Plus-001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="64">
			<td height="64" style="height:64px;width:216px;">Xylenes, ACS reagent, &#8805;98.5% xylenes + ethylbenzene basis&#160;</td>
			<td style="width:84px;">Sigma Aldrich</td>
			<td style="width:119px;">247642</td>
			<td style="width:167px;">CAUTION: PLEASE USE PROPER SAFETY PROCEDURES.</td>
		</tr>
		<tr height="64">
			<td height="64" style="height:64px;width:216px;">Ethyl Alcohol, U.S.P. 200 Proof, Anhydrous</td>
			<td style="width:84px;">The Warner-Graham Company</td>
			<td style="width:119px;">6.505E+12</td>
			<td style="width:167px;">CAUTION: PLEASE USE PROPER SAFETY PROCEDURES.</td>
		</tr>
		<tr height="64">
			<td height="64" style="height:64px;width:216px;">Arcturus CapSure Macro LCM Caps</td>
			<td style="width:84px;">Life Technologies</td>
			<td style="width:119px;">LCM0211</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="64">
			<td height="64" style="height:64px;width:216px;">ArcturusXT Laser Microdissection Instrument</td>
			<td style="width:84px;">Life Technologies</td>
			<td style="width:119px;">ARCTURUSXT</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="127">
			<td height="127" style="height:127px;width:216px;">AutoScanXT Software</td>
			<td style="width:84px;">Life Technologies</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">An optional image analysis program for the ArcturusXT Laser Microdissection Device. This is software is required for SIVQ-LCM.</td>
		</tr>
		<tr height="169">
			<td height="169" style="height:169px;width:216px;">Spatially Invariant Vector Quantization (SIVQ)</td>
			<td style="width:84px;">University of Michigan</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">This tool suite is publicly available for academic collaborations. For access to the SIVQ algorithm, please contact Dr. Ulysses Balis [Ulysses@med.umich.edu]</td>
		</tr>
	</tbody>
</table>

sivq-lcm protocol for the arcturusxt instrument

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process.&#160;It aims to create an advanced, rapidly customizable laser dissection platform technology. In this report, we describe the integration of the image analysis software Spatially Invariant Vector Quantization (SIVQ) onto the ArcturusXT instrument. The ArcturusXT system contains both an infrared (IR) and ultraviolet (UV) laser, allowing for specific cell or large area dissections. The principal goal is to improve the speed, accuracy, and reproducibility of the laser dissection to increase sample throughput. This novel approach facilitates microdissection of both animal and human tissues in research and clinical workflows.

A FFPE human breast tissue section was immunostained for cytokeratin AE1/AE3 using a standard IHC protocol23. After staining, the tissue slide was placed on the ArcturusXT stage and the SIVQ-LCM protocol was initiated as described above. Since the tissue cannot be coverslipped for microdissection, the IHC+ stained cells can be difficult to discern visually (Figure 1A). Thus, to provide better index matching and an improved image, xylenes were added to the tissue section to create a pseudo-coverslip15 (Figure 1B). A jpeg image was then captured of the pseudo-coverslipped area and imported into SIVQ for the algorithm analysis. A predicate image feature (dark brown DAB stain) selected by the user initiated the SIVQ algorithm to analyze the image (Figure 1C). The SIVQ heatmap was then converted to a &#8220;red paint&#8221; that is recognized by the automated selection software (Figure 1D). The xylenes were allowed to evaporate and the SIVQ heatmap was imported into the automated selection software (Figure 1E) and the highlighted cells were dissected with the IR laser. The LCM cap was then moved to the QC station of the ArcturusXT instrument and visually inspected to assess dissection efficiency (Figure 1F). The remaining tissue was also inspected (Figure 1G) and the SIVQ heatmap was re-imported to further assess the microdissection efficiency (Figure 1H).
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51662/51662fig1highres.jpg" src="/files/ftp_upload/51662/51662fig1.jpg" /> 
Figure 1. SIVQ-LCM of IHC stained FFPE breast tissue. A) Uncoverslipped image of cytokeratin AE1/AE3 stained FFPE breast tissue. B) Xylenes coated (pseudo-coverslipped) image of panel A. C) SIVQ generated heatmap using a ring vector (predicate image) captured from the brown chromagen. D) The heatmap was converted to a single color (red). In the ArcturusXT, the AutoScanXT software was trained to recognize the red annotation. E) The microdissection map generated from the AutoScanXT algorithm. F) Image of the microdissected breast epithelial cells on the LCM cap. G) Image of the tissue on the slide after microdissection. H) Overlap of the microdissection map (panel E) on the tissue area that was dissected. <a href="https://www.jove.com/files/ftp_upload/51662/51662fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about SIVQ-LCM Protocol for the ArcturusXT Instrument at JoVE.com

SIVQ-LCM Protocol for the ArcturusXT Instrument

SIVQ-LCM is an innovative approach that harnesses a computer algorithm, Spatially Invariant Vector Quantization (SIVQ), to drive the laser capture microdissection (LCM) process. The SIVQ-LCM workflow greatly improves the speed and accuracy of microdissection, with applications in both the research and clinical settings.

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process.&#160;It aims to create an ...

sivq-lcm-protocol-for-the-arcturusxt-instrument

Nanyang Technological University

Research

JoVE Journal

Biology

8.6K Views.  National Institutes of Health. SIVQ-LCM is an innovative approach that harnesses a computer algorithm, Spatially Invariant Vector Quantization (SIVQ), to drive the laser capture microdissection (LCM) process. The SIVQ-LCM workflow greatly improves the speed and accuracy of microdissection, with applications in both the research and clinical settings.

Video: SIVQ-LCM Protocol for the ArcturusXT Instrument

Protocol for Mosquito Rearing (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night cycle. For this procedure, you'll need mosquito cages, 10% sterile sucrose solution, paper towels, beaker, whatman filter paper, glass feeders, human blood and serum, water bath, parafilm, distilled water, clean plastic trays, mosquito food (described below), mosquito net to cover the trays, vacuum, and a collection chamber to collect adults.

Protocol for Mosquito Rearing A. gambiae

This video illustrates the general techniques used to rear Anopheles gambiae in the laboratory. The methods for caring for laboratory mosquitoes are demonstrated through all stages of the organism's life cycle from larvae to pupae to blood-feeding adults.

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night ...

Neutrophil Isolation Protocol

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory immune response. Due to their key role in inflammation, neutrophil functions such as locomotion, cytokine production, phagocytosis, and tumor cell combat are extensively studied. To characterize the specific functions of neutrophils, a clean, fast, and reliable method of separating them from other blood cells is desirable for in vitro studies, especially since neutrophils are short-lived and should be used within 2-4 hours of collection. Here, we demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media that is a mixture of sodium metrizoate and Dextran 500. The procedure consists of layering whole blood over the density gradient medium, centrifugation, separation of neutrophil layer, and lysis of residual erythrocytes. Cells are then washed, counted, and resuspended in buffer to desired concentration. When performed correctly, this method has been shown to yield samples of >95% neutrophils with >95% viability.

Neutrophils are among the first cells to arrive on the site of inflammatory immune response, and their functions and mechanisms have been studied extensively in vitro. We demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media.

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory ...

The Preparation of Drosophila Embryos for Live-Imaging Using the Hanging Drop Protocol

Green fluorescent protein (GFP)-based timelapse live-imaging is a powerful technique for studying the genetic regulation of dynamic processes such as tissue morphogenesis, cell-cell adhesion, or cell death. Drosophila embryos expressing GFP are readily imaged using either stereoscopic or confocal microscopy. A goal of any live-imaging protocol is to minimize detrimental effects such as dehydration and hypoxia. Previous protocols for preparing Drosophila embryos for live-imaging analysis have involved placing dechorionated embryos in halocarbon oil and sandwiching them between a halocarbon gas-permeable membrane and a coverslip1-3. The introduction of compression through mounting embryos in this manner represents an undesirable complication for any biomechanical-based analysis of morphogenesis. Our method, which we call the hanging drop protocol, results in excellent viability of embryos during live imaging and does not require that embryos be compressed. Briefly, the hanging drop protocol involves the placement of embryos in a drop of halocarbon oil that is suspended from a coverslip, which is, in turn, fixed in position over a humid chamber. In addition to providing gas exchange and preventing dehydration, this arrangement takes advantage of the buoyancy of embryos in halocarbon oil to prevent them from drifting out of position during timelapse acquisition. This video describes in detail how to collect and prepare Drosophila embryos for live imaging using the hanging drop protocol. This protocol is suitable for imaging dechorionated embryos using stereomicroscopy or any upright compound fluorescence microscope.

The Preparation of Drosophila Embryos for Live-Imaging Using the Hanging Drop Protocol

A simple, inexpensive, and effective method of preparing Drosophila embryos for live-imaging analysis is presented. Our protocol provides humidity and gas exchange and does not compress the Drosophila embryo. This method is suitable for GFP-based live imaging of Drosophila embryos using a stereomicroscope or upright compound microscope.

Green fluorescent protein (GFP)-based timelapse live-imaging is a powerful technique for studying the genetic regulation of dynamic processes such as ...

A Protocol for the Production of KLRG1 Tetramer

Killer cell lectin-like receptor G1 (KLRG1) is a type II transmembrane glycoprotein inhibitory receptor belonging to the C type lectin-like superfamily. KLRG1 exists both as a monomer and as a disulfide-linked homodimer. This well-conserved receptor is found on the most mature and recently activated NK cells as well as on a subset of effector/memory T cells.
Using KLRG1 tetramer as well as other methods, E-, N-, and R-cadherins were identified as KLRG1 ligands. These Ca2+-dependent cell-cell adhesion molecules comprises of an extracellular domain containing five cadherin repeats responsible for cell-cell interactions, a transmembrane domain and a cytoplasmic domain that is linked to the actin cytoskeleton. 
Generation of the KLRG1 tetramer was essential to the identification of the KLRG1 ligands. KLRG1 tetramer is also a unique tool to elucidate the roles cadherin and KLRG1 play in regulating the immune response and tissue integrity.

This protocol describes the production of KLRG1 tetramer, which is a powerful tool for the analysis of KLRG1 ligands.

Killer cell lectin-like receptor G1 (KLRG1) is a type II transmembrane glycoprotein inhibitory receptor belonging to the C type lectin-like superfamily. ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

A Simple Protocol for Extracting Hemocytes from Wild Caterpillars

Insect hemocytes (equivalent to mammalian white blood cells) play an important role in several physiological processes throughout an insect's life cycle 1. In larval stages of insects belonging to the orders of Lepidoptera (moths and butterflies) and Diptera (true flies), hemocytes are formed from the lymph gland (a specialized hematopoietic organ) or embryonic cells and can be carried through to the adult stage. Embryonic hemocytes are involved in cell migration during development and chemotaxis regulation during inflammation. They also take part in cell apoptosis and are essential for embryogenesis 2. Hemocytes mediate the cellular arm of the insect innate immune response that includes several functions, such as cell spreading, cell aggregation, formation of nodules, phagocytosis and encapsulation of foreign invaders 3. They are also responsible for orchestrating specific insect humoral defenses during infection, such as the production of antimicrobial peptides and other effector molecules 4, 5. Hemocyte morphology and function have mainly been studied in genetic or physiological insect models, including the fruit fly, Drosophila melanogaster 6, 7, the mosquitoes Aedes aegypti and Anopheles gambiae 8, 9 and the tobacco hornworm, Manduca sexta 10, 11. However, little information currently exists about the diversity, classification, morphology and function of hemocytes in non-model insect species, especially those collected from the wild 12.
Here we describe a simple and efficient protocol for extracting hemocytes from wild caterpillars. We use penultimate instar Lithacodes fasciola (yellow-shouldered slug moth) (Figure 1) and Euclea delphinii (spiny oak slug) caterpillars (Lepidoptera: Limacodidae) and show that sufficient volumes of hemolymph (insect blood) can be isolated and hemocyte numbers counted from individual larvae. This method can be used to efficiently study hemocyte types in these species as well as in other related lepidopteran caterpillars harvested from the field, or it can be readily combined with immunological assays designed to investigate hemocyte function following infection with microbial or parasitic organisms 13.

Insect hemocytes carry out many important functions, both immune and non-immune, throughout all stages of insect development. Our present knowledge of hemocyte types and function comes from studies on insect genetic models. Here, we present a method for extracting, quantifying and visualizing hemocytes from wild caterpillars.

Insect hemocytes (equivalent to mammalian white blood cells) play an important role in several physiological processes throughout an insect's life cycle ...

Optimized Protocol for Retinal Wholemount Preparation for Imaging and Immunohistochemistry

Working with delicate tissue can be a complicating factor when performing immunohistochemical assessment. Here, we present a method that utilizes a ring-supported hydrophilized PTFE membrane to provide structural support to both living and fixed tissue during immunohistochemical processing, which allows for the use of a variety of protocols that would otherwise cause damage to the tissue. First, this is demonstrated with bolus loading of fluorescent markers into living retinal tissue. This method allows for quick visualization of targeted structures, while the membrane support maintains tissue integrity during the injection and allows for easy transfer of the preparation for further imaging or processing.
Second, a procedure for antibody staining in tissue fixed with carbodiimide is described. Though paraformaldehyde fixation is more common, carbodiimide fixation provides a superior substrate for the visualization of synaptic proteins. A limitation of carbodiimide is that the resulting fixed tissue is relatively fragile; however, this is overcome with the use of the supporting membrane. Retinal tissue is used to demonstrate these techniques, but they may be applied to any fragile tissue.

The protocol described here is for structural assessment of a wholemount retinal preparation. This includes descriptions of tissue dissection, mounting onto a hydrophilized polytetrafluoroethylene (PTFE) membrane insert, bolus loading with fluorescent markers, and a comparison of fixation with carbodiimide and paraformaldehyde for immunohistochemical analysis of cellular and synaptic components.

Working with delicate tissue can be a complicating factor when performing immunohistochemical assessment. Here, we present a method that utilizes a ...

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, most notably developmental arrest and reduced metabolism. Global gene expression profiling via RNA-Seq can provide important insights into the transcriptional mechanisms of photoperiodic diapause. The Asian tiger mosquito, Aedes albopictus, is an outstanding organism for studying the transcriptional bases of diapause due to its ease of rearing, easily induced diapause, and the genomic resources available. This manuscript presents a general experimental workflow for identifying diapause-induced transcriptional differences in A. albopictus. Rearing techniques, conditions necessary to induce diapause and non-diapause development, methods to estimate percent diapause in a population, and RNA extraction and integrity assessment for mosquitoes are documented. A workflow to process RNA-Seq data from Illumina sequencers culminates in a list of differentially expressed genes. The representative results demonstrate that this protocol can be used to effectively identify genes differentially regulated at the transcriptional level in A. albopictus due to photoperiodic differences. With modest adjustments, this workflow can be readily adapted to study the transcriptional bases of diapause or other important life history traits in other mosquitoes.

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

RNA-Seq analyses are becoming increasingly important for identifying the molecular underpinnings of adaptive traits in non-model organisms. Here, a protocol to identify differentially expressed genes between diapause and non-diapause Aedes albopictus mosquitoes is described, from mosquito rearing, to RNA sequencing and bioinformatics analyses of RNA-Seq data.

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. 
This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison ...

Clock Scan Protocol for Image Analysis: ImageJ Plugins

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a closed or segmented convex-shaped region of interest, leading to the generation of an averaged integral radial pixel-intensity profile. This protocol was originally developed in 2006, as a visual basic 6 script, but as such, it had limited distribution. To address this problem and to join similar recent efforts by others, we converted the original clock scan protocol code into two Java-based plugins compatible with NIH-sponsored and freely available image analysis programs like ImageJ or Fiji ImageJ. Furthermore, these plugins have several new functions, further expanding the range of capabilities of the original protocol, such as analysis of multiple regions of interest and image stacks. The latter feature of the program is especially useful in applications in which it is important to determine changes related to time and location. Thus, the clock scan analysis of stacks of biological images may potentially be applied to spreading of Na+ or Ca++ within a single cell, as well as to the analysis of spreading activity (e.g., Ca++ waves) in populations of synaptically-connected or gap junction-coupled cells. Here, we describe these new clock scan plugins and show some examples of their applications in image analysis.

This paper describes two novel ImageJ plugins for &#39;Clock Scan&#39; image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a ...