Name: validation of nanobody and antibody based in vivo tumor xenograft nirf-imaging experiments in mice using ex vivo flow cytometry and microscopy
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Description: Watch this Scientific Journal Video about Validation of Nanobody and Antibody Based In Vivo Tumor Xenograft NIRF-imaging Experiments in Mice Using Ex Vivo Flow Cytometry and Microscopy at JoVE.com

This work was supported by the graduate school &#8216;Inflammation and regeneration&#8217; of the Collaborative Research Centre 841 of the Deutsche Forschungsgemeinschaft (Alexander Lenz, Valentin Kunick, William Fumey), by the Collaborative Research Centre 877 of the Deutsche Forschungsgemeinschaft (Friedrich Koch-Nolte), by the Werner Otto Foundation (Peter Bannas), by the Wilhelm Sander Foundation (Peter Bannas, Friedrich Koch-Nolte), and by the Deutsche Forschungsgemeinschaft (Martin Trepel, Friedrich Haag and Friedrich Koch-Nolte). We thank the University Cancer Center Hamburg (UCCH) In Vivo Optical Imaging Core Facility and staff at UKE for consultation and their high quality service. The Core Facility was supported in part by grants from Deutsche Krebshilfe (German Cancer Aid).

NOTE: Experiments were performed in accordance with international guidelines on the ethical use of animals and were approved by the local animal welfare commission of the&#160;University Medical Center, Hamburg.
1. Preparation of Tumor Cells, Mice, and Antibody Constructs
<ol>
	<li>Preparation of Lymphoma Cells and Aliquoting of Basement Matrix (Matrigel).
	<ol>
		<li>The day before injection of tumor cells put a sterilized tip box (1,000 &#181;l tips) and the appropriate pipette in -20 &#176;C freezer.</li>
		<li>Thaw the bottle with the basement matrix on ice in the 4 &#176;C fridge O/N.</li>
		<li>On the day of injection fill an ice bucket and place the basement matrix along with pipette, tips, and 1 ml syringes with 30 G&#160;needles on ice.</li>
		<li>Aliquot lymphoma cells in a volume of 100 &#181;l of RPMI medium in 1.5 ml microcentrifuge tubes and mix carefully with 100 &#181;l of the basement matrix. Draw up into pre-cooled syringes and put on ice until injection. 
		NOTE: Use good sterile technique and to work on ice the whole time to prevent clogging of the basement matrix.</li>
	</ol>
	</li>
	<li>Mice Preparation
	<ol>
		<li>Use 8-10 week old athymic nude mice (NMRI-Foxn1nu).</li>
		<li>To reduce autofluorescence of the intestine keep mice on an alfalfa-free diet for 1 week prior to in vivo imaging.</li>
		<li>For injection of lymphoma cells anesthetize mice to effect with 2% isoflurane in an induction chamber. Maintain 1-2% isoflurane for the duration of the procedure using an isofluorane manifold.</li>
		<li>Inject lymphoma cells subcutaneously into the shoulder flanks. For direct intra-individual comparison inject antigen-positive and antigen-negative cells on the right and left side, respectively.
		<ol>
			<li>Use a thumb and index finger to pinch the skin of the mouse and pull it away from the body of the mouse. Inject slowly and evenly into the pouch created by the fingers, creating a single cluster of cells beneath the skin. The basement matrix helps keeping injected cells in place.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Preparation of Antibody Constructs
	<ol>
		<li>Label monoclonal antibodies and single domain nanobodies with a commercially available protein labelling kit to the fluorescent dye AlexaFluor-680 (AF680) (excitation wavelength = 679 nm, emission wavelength = 702 nm) according the manufacturer&#8217;s instructions. Calculate the number of dyes per nanobody and conventional antibody using molar extinction coefficients of 15,720 mol-1 cm-1 and 203,000 mol-1 cm-1, respectively. Assess the purity of antibody constructs by SDS-PAGE size fractionation and Coomassie brilliant blue stain.</li>
		<li>Ensure the specificity of the labeled conjugates by conducting a series of in vitro experiments before the actual imaging studies. Test the specificity of labeled antibody constructs in vitro through blocking studies and unspecific isotype controls 8,20. 
		NOTE: In step 1.2.4 cell numbers for antigen-negative and antigen-positive cells (or different cells lines) may have to be adapted according to different growth rates. For these experiments, 0.5x106 DC27.10 ARTC2 negative and 1.5x106 DC27.10 ARTC2 positive cells were used to obtain tumors of similar size. In step 1.3.1 the process of labeling and labeling efficacy of antibody probes with fluorophores may differ by the labeling kit used.</li>
	</ol>
	</li>
</ol>
2. In Vivo Imaging
<ol>
	<li>After 7-9 days, when tumors reach ~8 mm in diameter, inject 50 &#181;g of AlexaFluor680 labelled antibody constructs in a volume of 200 &#181;l saline intravenously into the tail vein of the mouse (mAb-AF680: 50 &#181;g with 2 dyes/molecule &#8776; ~4^14 fluorochromes &#8776; ~0.8 &#181;g fluorochromes; Nanobody-AF680: 50 &#181;g with 0.3 dyes/molecule &#8776; ~5.6^14 fluorochromes &#8776; ~1.1 &#181;g fluorochromes).</li>
	<li>Initialize the imaging system and anesthetize mice to effect with 3% isoflurane using an XGI-8 anesthesia system in the induction chamber prior to imaging. Maintain 1-2% isoflurane for the duration of the imaging procedure using the isoflurane manifold housed in the imaging chamber.
	<ol>
		<li>Position mice on heated imaging stage with tumors directed towards the camera. Monitor the proper anesthetization by pinching the toe or tail of the animal; any reaction of the animal indicates that anesthesia is too light.</li>
		<li>Monitor the respiration rate; the anesthesia is too light if the respiration rate is increased and too deep if the respiration rate is decreased, deep or irregular. Protect animal&#8217;s corneas with an eye ointment to prevent dryness while under anesthesia.</li>
	</ol>
	</li>
	<li>Choose fluorescent filter sets of 615-665 nm for excitation, 695-770 nm for emission, and 580-610 nm excitation for background subtraction with a 51 2x 512 pixel matrix size. Set exposure time to auto, pixel binning to medium and F/Stop to 2. 
	NOTE: Shorter exposure times enable faster frame rates; longer exposure times provide greater sensitivity. Binning controls the pixel size on the CCD camera. Increasing the binning increases the pixel size, sensitivity, and frame rate, but reduces spatial resolution. F/Stop sets the size of the camera lens aperture. The aperture size controls the amount of light detected and the depth of field.Note that settings can differ by imaging device and experimental set up. Consult the manufacturer&#8217;s manual for optimal results.
	<ol>
		<li>In the imaging software optimize exposure time, F/stop and pixel binning based on the expression level of the cell line. Change these settings at any time during an experiment without impacting the quantitative result. Alternatively, let the Imaging Wizard software automatically determine the parameters.</li>
	</ol>
	</li>
	<li>Image mice before injection and 6 hr after injection of antibody constructs. 
	NOTE: The labelling efficacy may affect the required dose needed for optimal imaging results. Therefore the amount of required antibody-construct for optimal imaging results has to be determined empirically. It may vary depending on labelling efficacy and size of the construct as well as the tumor model and the target-antigen expression.</li>
</ol>
3. Harvesting and Preparation of Tumors
<ol>
	<li>Fill an ice bucket and place 4%, paraformaldehyde (PFA) phosphate buffered saline (PBS)/protease inhibitor (AEBSF) and PBS/0.2% bovine serum albumin (BSA) on ice.</li>
	<li>Once the imaging session is finished, increase the concentration of isoflurane to 4%. After animal ceases breathing, remove it from the imaging stage and perform cervical dislocation.</li>
	<li>Mount mouse on a Styrofoam block and spray with 70% ethanol.</li>
	<li>Use scissors and forceps to cut the outer skin and pull it back carefully to expose tumors. Remove tumors with scalpel to ensure tumor tissue remains intact.</li>
	<li>Cut tumors in half using a scalpel. Place one half in a collection tube with PBS/AEBSF for FACS analyses and the other half in a 50 ml tube with 4% PFA for immunohistochemistry.</li>
	<li>Preparation for immunohistochemistry
	<ol>
		<li>Keep tumors in 4% PFA in the fridge at 4 &#176;C for 24 hr. Transfer tumors to a tube with PBS/30% sucrose and keep in the fridge at 4 &#176;C until the tumor sinks to the bottom of the tube.</li>
		<li>Cut the tumors in appropriate pieces for the cryomolds. Put the tumors in cryomolds and fill with OCT compound so that the tumors are covered.</li>
		<li>Put molds on dry ice and wait until the compound is completely frozen. Transfer frozen tumors to -80 &#176;C freezer and store for immunohistochemistry.</li>
		<li>Cut frozen tumors in sections of 8 &#956;m with a microtome. Use standard immunohistochemistry protocol to stain with antibody against CD31-AF488 to visualize blood vessels and diamidino-phenylindole (DAPI) to visualize nuclei. 
		NOTE: Tumor sections are very sensitive to light as they already contain fluorescently labelled antibodies. Minimize exposure to light as much as possible.</li>
	</ol>
	</li>
	<li>Preparation for FACS analysis
	<ol>
		<li>Place petri dish on ice and remove the plunger from a 2.0 ml syringe. Put the tumor in the cell strainer and cut it gently into 3-4 pieces using a scalpel. Pour the initial PBS in the petri dish and use the flat end of the plunger to mash the tumor within the cell strainer.</li>
		<li>Flush the cell strainer with the initial PBS to wash out all cells with a 10 ml pipette. Transfer cell suspension in a new tube and spin at 500 x g for 5 min. Discard the supernatant and resuspend the cells in 10 ml PBS/0.2% BSA. Count cells.</li>
		<li>Aliquot 1-5 x 106 lymphoma cells in a 5 ml FACS tube. Spin cells again (500 x g), discard supernatant and resuspend in 100 &#181;l PBS/0.2% BSA.</li>
		<li>Optionally, block FcR using an anti-CD16/CD32-mAb (FcgR3/2) that binds to Fc&#947;R on ice for 10 min. Wash cells once with PBS/0.2% BSA.</li>
		<li>Add anti-CD45-mAb to discriminate leucocytes from other cells. Incubate for 20 min on ice in the dark, followed by two washes with PBS/0.2% BSA.</li>
		<li>Right before FACS analysis stain with propidium iodide for 15 min on ice to discern dead cells, followed by two washes with plain PBS. Resuspend in 150 &#181;l for FACS analysis. 
		NOTE: In step 3.7.4 and step 3.7.5 other antibodies may be used depending on the tumor entity.</li>
	</ol>
	</li>
</ol>
4. FACS&#160;Analysis
<ol>
	<li>Use a series of gating tools to gate the population of interest in the form of large scatter, doublet discrimination, exclusion of dead cells, gating for CD45 positive cells, and gating for antigen-positive cells.
	<ol>
		<li>At first, gate out cell debris in a forward scatter (FSC-A) versus side scatter (SSC-A) using a two-parameter plot. Next discard cell doublets. Finally, gate out non CD45-antigen-positive (CD45) and dead/dying cells (LD &#8211; live/dead-stain).</li>
	</ol>
	</li>
	<li>Record samples using the same template for all experiments. 
	NOTE: Refer to the manufacturer&#8217;s protocol for technical advice regarding the use of hardware and analytic software.</li>
</ol>
5. Microscopic Analysis
<ol>
	<li>Analyze stained tumor cryosections using a confocal microscope with an oil immersion lens (40X objective). Use a He-Neon 633 nm laser excitation of AF680, an Argon Laser for AF488, and a 405-Diode for DAPI.</li>
	<li>Process the raw image data using a software compatible with the microscope. Perform background-correction and noise filtering if necessary. Perform additional image adjustments such as sectioning, cropping as well as brightness and contrast adjustments.
	<ol>
		<li>Finally generate a single composite overlay image from all detection channels. Adjust the brightness and contrast of the individual layers. The composite images will show localization of cells (DAPI-stain, blue), distribution of labelled antibody constructs (AF680-stain, red) and blood vessels (AF488-stain, green).</li>
	</ol>
	</li>
</ol>
6. In Vivo Imaging Analysis
<ol>
	<li>Open image files in imaging software and create an overlay image by combining photograph image data with fluorescence image data. Optimize image display by removing tissue autofluorescence background signal from the specific fluorescent signal. This can be done by subtracting the image acquired with a background filter set from the image acquired with the filter set specific for the tracer.</li>
	<li>Draw identical circular measurement regions of interest (ROI) around the antigen-positive tumor and the antigen-negative tumor. To determine background signal intensity, place a circular ROI in an area of the animal where fluorescence signal is expected to be low (e.g., hind leg).
	<ol>
		<li>Use the same ROIs for all animals at all time points. For positioning, use the photographic black-and-white images to identify tumor margins.</li>
	</ol>
	</li>
	<li>Display ROI data in a measurement table. Use average radiant efficiency data, which enables a more quantitative comparison of fluorescent signals for further statistical analyses.
	<ol>
		<li>Display and compare data as absolute signal intensities or calculate the signal-to-background ratio using measured ROI data from targeted tissue and background tissue. Calculate the tumor-to-background ratio by dividing the tumor uptake value by the background value determined from the hind limb.</li>
		<li>As controls, also analyze antigen-negative and antigen-positive tumors in the same animals as well as animals injected with labelled isotype controls to assess the specificity of signals in vivo.</li>
	</ol>
	</li>
</ol>

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Friedrich Koch-Nolte and Friedrich Haag receive a share of antibody and protein sales via MediGate GmbH, a wholly owned subsidiary of the University Medical Center Hamburg-Eppendorf.

We used near-infrared fluorophore labelled nanobodies and conventional monoclonal antibodies directed against the same target on lymphoma cells for a multimodal comparison of in vivo and ex vivo analyses. We showed that nanobodies are well suited as diagnostic tools for rapid and specific in vivo detection of lymphomas.
In vivo, s+16a680 allowed a fast and more specific detection of ARTC2-positive xenografts. Apart from the different kinetics for best tumor visualization in vivo, the major drawback of Nika102680 was the high nonspecific signal from ARTC2-negative tumors and nonspecific background signals.
Ex vivo flow cytometric analyses of dispersed cells from dissected tumors showed no nonspecific binding to ARTC2-negative lymphoma cells of injected AF680-conjugates. Ex vivo fluorescence revealed strong and almost homogenous staining of cells in ART2C-positive tumor sections in case of nanobody s+16a, confirming that the nanobody was able to reach even remote areas within the tumor after 6 h. In contrast, the monoclonal antibody showed weaker and inhomogeneous staining of cells in ARTC2-positive tumors after 6 hr. Better imaging results with the conventional antibody can be achieved after 24 hr or 48 hr (data not shown). In order to perform a thorough comparison of two differently sized constructs, imaging at different time points (serial-imaging) has to be performed to identify the optimal imaging time point for each construct.
Like other previous studies, the results reported here emphasize that in vivo molecular imaging with labelled nanobodies allows rapid and specific same-day tumor imaging with high tumor-to-background ratios 12-15,17-19. Contrariwise, conventional antibodies result in low tumor-to-background ratios and nonspecific signals from antigen-negative tumors early after injection due to their slow clearance from the body. In order to obtain optimal imaging results with conventional antibodies, imaging time points 24 hr or even 48 hr after injection are commonly needed. These findings are in accord with previous studies that have suggested that conventional antibodies with proven therapeutic benefit have limited utility in molecular imaging 17,19,26. Therefore conventional antibodies might be rather suited for therapeutic purposes due to their long plasma half-life while nanobodies are rather suited for imaging purposes due to their rapid clearance from the circulation. These differences are due to the fact that any excess of the smaller nanobodies (15-17 kDa) is rapidly cleared via renal elimination while excess of larger conventional antibodies (150 kDa) is retained in the circulation. So the major advantage of nanobodies for molecular imaging is the low background signal at early imaging time points regardless of the injected dose. This allows same day imaging and could be translatable to the clinical setting. Contrariwise, conventional antibodies have to be exactly titrated to minimize nonspecific background signals, while maintaining enough specific signal from the targeted tissue (unpublished data).
One of the limitations of the in vivo NIRF-imaging technique is the low penetration depth which generally allows only imaging of subcutaneous but not of orthotopic tumor models. However, this limitation might be overcome in an experimental setting by the recently developed tomographic photo-acoustic techniques that allow whole-body imaging of living mice 27. Another limitation of the NIRF-imaging technique is the assessment of the tissue dose as compared to radionuclide-mediated imaging. However, the nanobodies may be radiolabelled for positron emission tomography (PET) imaging of xenograft models and exact quantitative assessment of tracer biodistribution. Indeed, our NIRF-imaging results are in accordance with a recent study that compared nanobodies and conventional antibodies for PET imaging. The authors also came to the conclusion that nanobodies allow same-day imaging with high tumor-to-background ratios 15.
However, only the labelling of antibody constructs with the near-infrared fluorescent dye AF680 allowed us the comprehensive in vitro, in vivo and ex vivo near-infrared fluorescence imaging comparison using flow cytometry, fluorescence microscopy, and NIRF-imaging. For this reason, and because it is nonradioactive, highly sensitive, inexpensive, and uses comparatively easy-to-produce targeted probes, we advocate the use of the NIRF-imaging technique for evaluation of new antibody constructs in preclinical molecular imaging.

In the present report, we describe the implementation of near-infrared fluorophore labelled probes for validation of in vivo xenograft imaging experiments by using ex vivo flow cytometry and fluorescence microscopy of the dissected xenograft tumors. We compare a single domain nanobody (s+16a, 17 kDa) 1 and a monoclonal antibody (Nika102, 150 kDa) 2,3 directed to the same target antigen for specific in vivo near-infrared fluorescence imaging in a lymphoma xenograft model. The target antigen ADP-ribosyltransferase ARTC2.2 is expressed as a GPI-anchored cell surface ecto-enzyme by lymphoma cells 4-9.
Nanobodies derived from camelid heavy-chain-only antibodies are the smallest available antigen-binding fragments 10,11. With only ~15 kDa, these small antibody fragments are soluble, very stable and are renally cleared from circulation 8,10. These properties make them particularly suited for specific and efficient targeting of tumor antigens in vivo 12-20. Common antigen targets of available nanobodies are the epidermal growth factor receptor (EGFR1 or HER-1), human epidermal growth factor type 2 (HER-2 or CD340), carcinoembryonic antigen (CEA) and vascular cell adhesion molecule-1 (VCAM-1) 21. Nanobody conjugates are promising tools for cancer immunotherapy and treatment of inflammatory diseases 22.
Recent studies have shown that nanobodies allow higher tumor-to-background (T/B)-ratios than conventional antibodies in in vivo molecular imaging applications 8,17,19. This is explained mainly by the relatively poor and slow tissue penetration of conventional antibodies, slow clearance from circulation, and long retention in non-targeted tissues 23. Moreover, excess of conventional antibodies leads to non-specific accumulation in target antigen-negative tumors caused by the enhanced permeability and retention (EPR) effect 24,25. This means that higher doses of conventional antibodies may increase not only specific signals but also nonspecific signals, thus reducing the maximum achievable tumor-to-background ratio. In contrast, increasing the dose of nanobodies increases the signals of antigen-positive tumors but not of normal tissue or antigen-negative tumors (unpublished data).
Beyond the comparison of nanobodies and conventional antibodies, we outline an intraindividual assessment of antigen-positive and -negative xenografts in the same mice for direct comparison of specific and nonspecific signals due to the EPR effect. The near-infrared fluorophore conjugated probes allowed us to exploit a single probe in vivo and ex vivo&#160;using near-infrared fluorescence imaging, flow cytometry, and fluorescence microscopy. Applying our protocols allows for nonradioactive, highly sensitive, and inexpensive optimization of in vivo molecular imaging experiments such as evaluation of new antibody constructs for specific tumor targeting.
The aim of this tutorial study is to highlight the use of NIRF-imaging for evaluation of new antibody constructs in preclinical molecular imaging.
In this protocol, all experiments were performed with a small-animal NIRF-Imaging system, a fluorescence activated cell sorter (FACS) flow cytometer, and a confocal microscope.

<table border="0" cellpadding="0" cellspacing="0" width="722">
	<tbody>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Name of Material/ Equipment</td>
			<td style="width:147px;">Company</td>
			<td style="width:176px;">Catalog Number</td>
			<td style="width:183px;">Comments/Description</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">AF680 protein labelling kit</td>
			<td style="width:147px;">Invitrogen</td>
			<td style="width:176px;">A20172</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Anti-CD16/CD32-antibody</td>
			<td style="width:147px;">BioXCell</td>
			<td style="width:176px;">BE0008</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Anti-CD31-antibody</td>
			<td style="width:147px;">Santa Cruz</td>
			<td style="width:176px;">sc-1506</td>
			<td style="width:183px;">labeled with secondary antibody with AF488</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Anti-CD45-antibody V450</td>
			<td style="width:147px;">BD Biosciences</td>
			<td style="width:176px;">560501</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">AxioVision LE software</td>
			<td style="width:147px;">Zeiss</td>
			<td style="width:176px;">www.zeiss.com</td>
			<td style="width:183px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Basement membrane matrix</td>
			<td style="width:147px;">BD Biosciences</td>
			<td style="width:176px;">354234</td>
			<td style="width:183px;">Alternative product can be used</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Cell strainer 70&#181;m</td>
			<td style="width:147px;">Corning</td>
			<td style="width:176px;">431751</td>
			<td style="width:183px;">Alternative product can be used</td>
		</tr>
		<tr height="100">
			<td height="100" style="height:100px;width:216px;">Confocal microscope</td>
			<td style="width:147px;">Leica&#160;</td>
			<td style="width:176px;">www.leica-microsystems.com</td>
			<td style="width:183px;">Leica SP5 with the following lasers: He-Neon for AF680, Argon Laser for AF488, and a 405-Diode for DAPI</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DAPI</td>
			<td style="width:147px;">Molcular Probes</td>
			<td style="width:176px;">D1306</td>
			<td></td>
		</tr>
		<tr height="60">
			<td height="60" style="height:60px;width:216px;">DC27.10 cells&#160;</td>
			<td style="width:147px;">laboratory specific</td>
			<td style="width:176px;">n/a</td>
			<td style="width:183px;">Other cells with different surface targets can be used</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DPBS</td>
			<td style="width:147px;">Sigma Aldrich</td>
			<td style="width:176px;">D8662</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">FACS Canto II</td>
			<td style="width:147px;">BD Biosciences</td>
			<td style="width:176px;">www.bdbiosciences.com</td>
			<td></td>
		</tr>
		<tr height="80">
			<td height="80" style="height:80px;width:216px;">Flourescence mircoscope</td>
			<td style="width:147px;">Zeiss</td>
			<td style="width:176px;">www.zeiss.com</td>
			<td style="width:183px;">Zeiss Axiovert 200 with Filter Set #32 for AF680: 000000-1031-354</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ software</td>
			<td style="width:147px;">NIH</td>
			<td>http://imagej.nih.gov/ij/</td>
			<td></td>
		</tr>
		<tr height="60">
			<td height="60" style="height:60px;width:216px;">IVIS 200</td>
			<td>Perkin Elmer</td>
			<td style="width:176px;">www.perkinelmer.com</td>
			<td style="width:183px;">Alternative in vivo imaging system can be used</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Leica LAS software</td>
			<td style="width:147px;">Leica</td>
			<td style="width:176px;">www.leica-microsystems.com</td>
			<td style="width:183px;">Software specific to microscope used</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Living Image software</td>
			<td style="width:147px;">Perkin Elmer</td>
			<td style="width:176px;">www.perkinelmer.com</td>
			<td style="width:183px;">Software specific to imaging system used</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Needles 30 G</td>
			<td style="width:147px;">BD Biosciences</td>
			<td style="width:176px;">305128</td>
			<td style="width:183px;">Alternative product can be used</td>
		</tr>
		<tr height="60">
			<td height="60" style="height:60px;width:216px;">Nika102-antibody AF680</td>
			<td style="width:147px;">laboratory specific</td>
			<td style="width:176px;"></td>
			<td style="width:183px;">Other antibodies against different surface targets can be used</td>
		</tr>
		<tr height="102">
			<td height="102" style="height:103px;width:216px;">Paraformaldehyde</td>
			<td style="width:147px;">Sigma Aldrich</td>
			<td style="width:176px;">P6148</td>
			<td style="width:183px;">Potential hazards: carcinogenic, can irritate the eyes and skin, contact may cause drying of the skin and/or allergic dermatitis</td>
		</tr>
		<tr height="60">
			<td height="60" style="height:60px;width:216px;">s+16a-nanobody AF680</td>
			<td style="width:147px;">laboratory specific</td>
			<td style="width:176px;"></td>
			<td style="width:183px;">Other antibodies against different surface targets can be used</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Syringes 1ml</td>
			<td style="width:147px;">Braun</td>
			<td style="width:176px;">916 1406 V</td>
			<td style="width:183px;">Alternative product can be used</td>
		</tr>
	</tbody>
</table>

validation of nanobody and antibody based in vivo tumor xenograft nirf-imaging experiments in mice using ex vivo flow cytometry and microscopy

This protocol outlines the steps required to perform ex vivo validation of in vivo near-infrared fluorescence (NIRF) xenograft imaging experiments in mice using fluorophore labelled nanobodies and conventional antibodies.
First we describe how to generate subcutaneous tumors in mice, using antigen-negative cell lines as negative controls and antigen-positive cells as positive controls in the same mice for intraindividual comparison. We outline how to administer intravenously near-infrared fluorophore labelled (AlexaFluor680) antigen-specific nanobodies and conventional antibodies. In vivo imaging was performed with a small-animal NIRF-Imaging system. After the in vivo imaging experiments the mice were sacrificed. We then describe how to prepare the tumors for parallel ex vivo analyses by flow cytometry and fluorescence microscopy to validate in vivo imaging results.
The use of the near-infrared fluorophore labelled nanobodies allows for non-invasive same day imaging in vivo. Our protocols describe the ex vivo quantification of the specific labeling efficiency of tumor cells by flow cytometry and analysis of the distribution of the antibody constructs within the tumors by fluorescence microscopy. Using near-infrared fluorophore labelled probes allows for non-invasive, economical in vivo imaging with the unique ability to exploit the same probe without further secondary labelling for ex vivo validation experiments using flow cytometry and fluorescence microscopy.

Fluorescently labelled probes allow for the combination of different NIRF-imaging techniques (Figure 1A). We aimed to perform in vivo NIRF-imaging, flow cytometry, and fluorescence microscopy sequentially in order to compare fluorescently labelled nanobodies and monoclonal antibodies for specific in vivo imaging (Figure 1B).
Mice were injected with 50 &#956;g of nanobody and monoclonal antibody to evaluate the specificity of the fluorescently labelled constructs for in vivo imaging. The results showed specific labeling of antigen-positive tumors with both nanobody and monoclonal antibody at 6 hr after injection (Figure 2). ROI analyses of the antigen-positive tumors showed a much higher T/B ratio of ~12 for the nanobody compared to ~6 for the monoclonal antibody. Moreover, the nanobody showed no nonspecific signal in the antigen-negative tumors, whereas the monoclonal antibody showed nonspecific confounding signals in the antigen-negative tumors.
Besides the nonspecific signal of the negative tumors, the monoclonal antibody also induced nonspecific background signals in the entire animal. This is likely due to excessive free circulating antibodies, which are too large to be renally excreted. Contrariwise, animals injected with nanobodies showed nonspecific signals only in the kidneys due to the renal elimination of the small nanobodies.
Flow cytometry analyses of tumor cell suspensions showed specific labeling of antigen-positive tumor cells with both AF680-conjugates 6 hr after injection. The stronger fluorescence signal of the nanobody labelled cells compared to monoclonal antibody labelled cells reflects the in vivo NIRF-imaging results. Importantly, the flow cytometric analyses reveal that there is no nonspecific labelling of antigen-negative cells with either of the two constructs (Figure 3).
Fluorescence microscopy of tumor cryosections showed a strong and almost homogenous labeling of antigen-positive cells with the nanobody 6 hr after injection. Contrariwise, the monoclonal antibody showed a much weaker and rather inhomogeneous staining (Figure 4A). Antigen-negative tumors show no staining 6 hr after injection of the nanobody, whereas antigen-negative tumors injected with the conventional antibody show nonspecific scattered staining in the interstitial space (Figure 4B).
<img alt="Figure 1" src="/files/ftp_upload/52462/52462fig1highres.jpg" /> 
Figure 1: Flourescence imaging and antibody constructs. (A) Imaging setup for evaluation of AF680-conjugates: In vivo NIRF-imaging followed by flow cytometry and fluorescence microscopy. (B) Schematic of AlexaFluor680 labelled nanobody s+16a (red) and monoclonal antibody Nika102 (blue). Orange stars indicate the AlexaFluor680 fluorochromes.
<img alt="Figure 2" src="/files/ftp_upload/52462/52462fig2highres.jpg" /> 
Figure 2: In vivo NIRF-imaging. Images of the fluorescence signal of antigen-positive (+) and antigen-negative (-) tumors in mice that have been injected with nanobody s+16a (A) and monoclonal antibody Nika102 (B). In vivo imaging was performed before (0 h) and 6 h after injection. Signal intensities are displayed as radiant efficiency (p/sec/cm2/sr) / (&#181;W/cm2).
<img alt="Figure 3" src="/files/ftp_upload/52462/52462fig3highres.jpg" /> 
Figure 3: Ex vivo FACS analyses of cell bound antibody constructs from tumor cell suspensions. (A) Gating strategy for FACS analyses of tumor cells. (B) Histograms display the amount of the intravenously injected AF680-conjugated nanobody s+16a and antibody Nika102 specifically bound to the tumor cells in vivo. Antigen-negative tumors are displayed as unfilled histograms and antigen-positive tumors are displayed as filled histograms.
<img alt="Figure 4" src="/files/ftp_upload/52462/52462fig4highres.jpg" /> 
Figure 4: Ex vivo fluorescence microscopy. (A) Overview fluorescence microscopy of entire antigen-positive tumor cryosections 6 h after injection of s+16a680 or Nika102680. Signal intensities of the in vivo intravenously injected AF680-conjugates without any secondary-labelling agents are displayed in red. Ex vivo counterstained nuclei are displayed in blue and vessels in green. Dotted lines indicate outer margins of the entire tumors. (B) Close-up fluorescence microscopy of antigen-positive and antigen-negative tumors.

Watch this Scientific Journal Video about Validation of Nanobody and Antibody Based In Vivo Tumor Xenograft NIRF-imaging Experiments in Mice Using Ex Vivo Flow Cytometry and Microscopy at JoVE.com

Validation of Nanobody and Antibody Based In Vivo Tumor Xenograft NIRF-imaging Experiments in Mice Using Ex Vivo Flow Cytometry and Microscopy

This protocol outlines the steps required to perform ex vivo validation of in vivo near-infrared fluorescence xenograft imaging experiments in mice using fluorophore labelled nanobodies and conventional antibodies.

Validation of Nanobody and Antibody Based In Vivo Tumor Xenograft NIRF-imaging Experiments in Mice Using Ex Vivo Flow Cytometry and Microscopy

This protocol outlines the steps required to perform ex vivo validation of in vivo near-infrared fluorescence (NIRF) xenograft imaging experiments in mice ...

validation-nanobody-antibody-based-vivo-tumor-xenograft-nirf-imaging

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Medicine

In vivo Imaging of Tumor Angiogenesis using Fluorescence Confocal Videomicroscopy

Fibered confocal fluorescence in vivo imaging with a fiber optic bundle uses the same principle as fluorescent confocal microscopy. It can excite fluorescent in situ elements through the optical fibers, and then record some of the emitted photons, via the same optical fibers. The light source is a laser that sends the exciting light through an element within the fiber bundle and as it scans over the sample, recreates an image pixel by pixel. As this scan is very fast, by combining it with dedicated image processing software, images in real time with a frequency of 12 frames/sec can be obtained.
We developed a technique to quantitatively characterize capillary morphology and function, using a confocal fluorescence videomicroscopy device. The first step in our experiment was to record 5 sec movies in the four quadrants of the tumor to visualize the capillary network. All movies were processed using software (ImageCell, Mauna Kea Technology, Paris France) that performs an automated segmentation of vessels around a chosen diameter (10 &mu;m in our case). Thus, we could quantify the 'functional capillary density', which is the ratio between the total vessel area and the total area of the image. This parameter was a surrogate marker for microvascular density, usually measured using pathology tools.
The second step was to record movies of the tumor over 20 min to quantify leakage of the macromolecular contrast agent through the capillary wall into the interstitium. By measuring the ratio of signal intensity in the interstitium over that in the vessels, an 'index leakage' was obtained, acting as a surrogate marker for capillary permeability.

In vivo Imaging of Tumor Angiogenesis using Fluorescence Confocal Videomicroscopy

In this paper, we present a method to analyze tumor microvessels in vivo using dynamic contrast-enhanced fluorescence videomicroscopy. Two quantitative parameters were acquired: functional capillary density reflecting the vascularity of the tumor, and index leakage reflecting the leakiness of the endothelial wall.

Fibered confocal fluorescence in vivo imaging with a fiber optic bundle uses the same principle as fluorescent confocal microscopy. It can excite ...

In vivo 19F MRI for Cell Tracking

In vivo 19F MRI allows quantitative cell tracking without the use of ionizing radiation. It is a noninvasive technique that can be applied to humans. Here, we describe a general protocol for cell labeling, imaging, and image processing. The technique is applicable to various cell types and animal models, although here we focus on a typical mouse model for tracking murine immune cells. The most important issues for cell labeling are described, as these are relevant to all models. Similarly, key imaging parameters are listed, although the details will vary depending on the MRI system and the individual setup. Finally, we include an image processing protocol for quantification. Variations for this, and other parts of the protocol, are assessed in the Discussion section. Based on the detailed procedure described here, the user will need to adapt the protocol for each specific cell type, cell label, animal model, and imaging setup. Note that the protocol can also be adapted for human use, as long as clinical restrictions are met.

In vivo 19F MRI for Cell Tracking

We describe a general protocol for in vivo cell tracking using MRI in a mouse model with ex vivo labeled cells. A typical protocol for cell labeling, image acquisition processing and quantification is included.

In vivo 19F MRI allows quantitative cell tracking without the use of ionizing radiation. It is a noninvasive technique that can be applied to humans. ...

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases in children and adults. These antibodies have been used to guide diagnosis and treatment. Cell-based assays have improved the detection of antibodies in patient serum. They are based on the surface expression of brain antigens on eukaryotic cells, which are then incubated with diluted patient sera followed by fluorochrome-conjugated secondary antibodies. After washing, secondary antibody binding is then analyzed by flow cytometry. Our group has developed a high-throughput flow cytometry live cell-based assay to reliably detect antibodies against specific neurotransmitter receptors. This flow cytometry method is straight forward, quantitative, efficient, and the use of a high-throughput sampler system allows for large patient cohorts to be easily assayed in a short space of time. Additionally, this cell-based assay can be easily adapted to detect antibodies to many different antigenic targets, both from the central nervous system and periphery. Discovering additional novel antibody biomarkers will enable prompt and accurate diagnosis and improve treatment of immune-mediated disorders.

Over the recent years, live cell-based assays have been used successfully to detect antibodies against surface and conformational antigens. Here, we describe a method using high-throughput flow cytometry enabling the analysis of large cohorts of patients. Detection of novel antibodies will improve diagnosis and treatment of immune-mediated disorders.

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases ...

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent marker. Mouse models (brain tumor and arthritis) were used to evaluate the usefulness of this method. Saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles tagged with CellVue Maroon (CVM) fluorophore were administered intravenously. Animals were then placed in the rotational holder (MARS) of the in vivo imaging system. Images were acquired in 10&#176; steps over 380&#176;. A rectangular&#160;region of interest (ROI) was placed across the full image width at the model disease site. Within the ROI, and for every image, mean fluorescence intensity was computed after background subtraction. In the mouse models studied, the labeled nanovesicles were taken up in both the orthotopic and transgenic brain tumors, and in the arthritic sites (toes and ankles). Curve analysis of the multi angle image ROIs determined the angle with the highest signal. Thus, the optimal angle for imaging each disease site was characterized. The MAROI method applied to imaging of fluorescent compounds is a noninvasive, economical, and precise tool for in vivo quantitative analysis of the disease states in the described mouse models.

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo quantitation of a fluorescent marker delivered by saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles. Employing mouse models of cancer and arthritis, we demonstrate how the MAROI signal curve analysis can be used for the precise mapping and biological characterization of disease processes.

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent ...

Adaptation of Semiautomated Circulating Tumor Cell (CTC) Assays for Clinical and Preclinical Research Applications

The majority of cancer-related deaths occur subsequent to the development of metastatic disease. This highly lethal disease stage is associated with the presence of circulating tumor cells (CTCs). These rare cells have been demonstrated to be of clinical significance in metastatic breast, prostate, and colorectal cancers. The current gold standard in clinical CTC detection and enumeration is the FDA-cleared CellSearch system (CSS). This manuscript outlines the standard protocol utilized by this platform as well as two&#160;additional adapted protocols that describe the detailed process of user-defined marker optimization for protein characterization of patient CTCs and a comparable protocol for CTC capture in very low volumes of blood, using standard CSS reagents, for studying in vivo preclinical mouse models of metastasis. In addition, differences in CTC quality between healthy donor blood spiked with cells from tissue culture versus patient blood samples are highlighted. Finally, several commonly discrepant items that can lead to CTC misclassification errors are outlined. Taken together, these protocols will provide a useful resource for users of this platform interested in preclinical and clinical research pertaining to metastasis and CTCs.

Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications

Circulating tumor cells (CTCs) are prognostic in several metastatic cancers. This manuscript describes the gold standard CellSearch system (CSS) CTC enumeration platform and highlights common misclassification errors. In addition, two&#160;adapted protocols are described for user-defined marker characterization of CTCs and CTC enumeration in preclinical mouse models of metastasis using this technology.

The majority of cancer-related deaths occur subsequent to the development of metastatic disease. This highly lethal disease stage is associated with the ...

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the extracellular matrix (ECM). Characterizing the biology of this phenotypically distinct group of cells could substantially improve our understanding of early events during the metastatic cascade.

Tumor invasion is a dynamic process facilitated by bidirectional interactions between malignant epithelium and the cancer associated stroma. In order to examine cell-specific responses at the tumor stroma-interface we have combined organotypic co-culture and laser micro-dissection techniques.
Organotypic models, in which key stromal constituents such as fibroblasts are 3-dimentioanally co-cultured with cancer epithelial cells, are highly manipulatable experimental tools which enable invasion and cancer-stroma interactions to be studied in near-physiological conditions.

Laser microdissection (LMD) is a technique which entails the surgical dissection and extraction of the various strata within tumor tissue, with micron level precision.
By combining these techniques with genomic, transcriptomic and epigenetic profiling we aim to develop a deeper understanding of the molecular characteristics of invading tumor cells and surrounding stromal tissue, and in doing so potentially reveal novel biomarkers and opportunities for drug development in CRC.&#160;&#160;&#160;

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Molecular profiling of laser microdissected cells and stroma from synthetic, tissue-engineered colorectal cancer models represents a novel and manipulatable approach to characterize and dissect the distinctive biology at the interface between tumor cells at the invasive front and cancer associated stromal cells. &#160;

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the ...

Measuring Ascending Aortic Stiffness In Vivo in Mice Using Ultrasound

We present a protocol for measuring in vivo aortic stiffness in mice using high-resolution ultrasound imaging. Aortic diameter is measured by ultrasound and aortic blood pressure is measured invasively with a solid-state pressure catheter. Blood pressure is raised then lowered incrementally by intravenous infusion of vasoactive drugs phenylephrine and sodium nitroprusside. Aortic diameter is measured for each pressure step to characterize the pressure-diameter relationship of the ascending aorta. Stiffness indices derived from the pressure-diameter relationship can be calculated from the data collected. Calculation of arterial compliance is described in this protocol.
This technique can be used to investigate mechanisms underlying increased aortic stiffness associated with cardiovascular disease and aging. The technique produces a physiologically relevant measure of stiffness compared to ex vivo approaches because physiological influences on aortic stiffness are incorporated in the measurement. The primary limitation of this technique is the measurement error introduced from the movement of the aorta during the cardiac cycle. This motion can be compensated by adjusting the location of the probe with the aortic movement as well as making multiple measurements of the aortic pressure-diameter relationship and expanding the experimental group size.

Measuring Ascending Aortic Stiffness In Vivo in Mice Using Ultrasound

We describe a technique for measuring aortic stiffness from its pressure-diameter relationship in vivo in mice. Aortic diameter is recorded by ultrasound and aortic pressure is measured invasively with a solid-state pressure catheter. Blood pressure is changed incrementally and the resulting diameter is measured.

We present a protocol for measuring in vivo aortic stiffness in mice using high-resolution ultrasound imaging. Aortic diameter is measured by ultrasound ...

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of women are alive 5 years after diagnosis. The omentum is a preferred site of HGSC metastasis formation. Despite the clinical importance of this microenvironment, the contribution of omental adipose tissue to ovarian cancer progression remains understudied. Omental adipose is unusual in that it contains structures known as milky spots, which are comprised of B, T, and NK cells, macrophages, and progenitor cells surrounding dense nests of vasculature. Milky spots play a key role in the physiologic functions of the omentum, which are required for peritoneal homeostasis. We have shown that milky spots also promote ovarian cancer metastatic colonization of peritoneal adipose, a key step in the development of peritoneal metastases. Here we describe the approaches we developed to evaluate and quantify milky spots in peritoneal adipose and study their functional contribution to ovarian cancer cell metastatic colonization of omental tissues both in vivo and ex vivo. These approaches are generalizable to additional mouse models and cell lines, thus enabling the study of ovarian cancer metastasis formation from initial localization of cells to milky spot structures to the development of widespread peritoneal metastases.

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

We outline a protocol that implements both in vivo and ex vivo approaches to study ovarian cancer colonization of peritoneal adipose tissues, particularly the omentum. Furthermore, we present a protocol to quantitate and analyze immune cell-structures in the omentum known as milky spots, which promote metastases of peritoneal adipose.

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of ...

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

The use of patient-derived xenografts for modeling cancers has provided important insight into cancer biology and drug responsiveness. However, they are time consuming, expensive, and labor intensive. To overcome these obstacles, many research groups have turned to spheroid cultures of cancer cells. While useful, tumor spheroids or aggregates do not replicate cell-matrix interactions as found in vivo. As such, three-dimensional (3D) culture approaches utilizing an extracellular matrix scaffold provide a more realistic model system for investigation. Starting from subcutaneous or intracranial xenografts, tumor tissue is dissociated into a single cell suspension akin to cancer stem cell neurospheres. These cells are then embedded into a human-derived extracellular matrix, 3D human biogel, to generate a large number of microtumors. Interestingly, microtumors can be cultured for about a month with high viability and can be used for drug response testing using standard cytotoxicity assays such as 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and live cell imaging using Calcein-AM. Moreover, they can be analyzed via immunohistochemistry or harvested for molecular profiling, such as array-based high-throughput kinomic profiling, which is detailed here as well. 3D microtumors, thus, represent a versatile high-throughput model system that can more closely replicate in vivo tumor biology than traditional approaches.

Patient-derived xenografts of glioblastoma multiforme can be miniaturized into living microtumors using 3D human biogel culture system. This in vivo-like 3D tumor assay is suitable for drug response testing and molecular profiling, including kinomic analysis.

The use of patient-derived xenografts for modeling cancers has provided important insight into cancer biology and drug responsiveness. However, they are ...

Depletion of Mouse Cells from Human Tumor Xenografts Significantly Improves Downstream Analysis of Target Cells

The use of in vitro cell line models for cancer research has been a useful tool. However, it has been shown that these models fail to reliably mimic patient tumors in different assays1. Human tumor xenografts represent the gold standard with respect to tumor biology, drug discovery, and metastasis research 2-4. Tumor xenografts can be derived from different types of material like tumor cell lines, tumor tissue from primary patient tumors4 or serially transplanted tumors. When propagated in vivo, xenografted tissue is infiltrated and vascularized by cells of mouse origin. Multiple factors such as the tumor entity, the origin of xenografted material, growth rate and region of transplantation influence the composition and the amount of mouse cells present in tumor xenografts. However, even when these factors are kept constant, the degree of mouse cell contamination is highly variable.
Contaminating mouse cells significantly impair downstream analyses of human tumor xenografts. As mouse fibroblasts show high plating efficacies and proliferation rates, they tend to overgrow cultures of human tumor cells, especially slowly proliferating subpopulations. Mouse cell derived DNA, mRNA, and protein components can bias downstream gene expression analysis, next-generation sequencing, as well as proteome analysis 5. To overcome these limitations, we have developed a fast and easy method to isolate untouched human tumor cells from xenografted tumor tissue. This procedure is based on the comprehensive depletion of cells of mouse origin by combining automated tissue dissociation with the benchtop tissue dissociator and magnetic cell sorting. Here, we demonstrate that human target cells can be can be obtained with purities higher than 96% within less than 20 min independent of the tumor type.

Human tumor xenografts are vascularized and infiltrated by cells of mouse origin during the growth phase in vivo. To circumvent the bias caused by these contaminating cells during downstream analysis, we developed a method allowing for comprehensive depletion of all mouse cells by magnetic cell sorting.

The use of in vitro cell line models for cancer research has been a useful tool. However, it has been shown that these models fail to reliably mimic ...