Dr. P. Lindstr&ouml;m is acknowledged for providing ob/ob mice. J. Lehtonen is acknowledged for assistance with video production and J. Gilbert for help with editing. This study was supported by grants from the Diabetes Research Institute Foundation (A.P.), the Juvenile Diabetes Research Foundation (A.P. and U.A.), the European Commission (FP-7, Grant agreement no.: CP-IP 228933-2) (J.S. and U.A.), the Kempe Foundations, Ume&aring; University and the Swedish research council to U.A.

1. Sample Preparation and Scanning
1.1 Sample preparation
The following procedure is performed essentially as described previously7.
<ol>
	<li>Harvest the pancreas. Use ice-cold PBS to avoid proteolytic degradation.</li>
	<li>Fix the tissue in 4% PFA in PBS on ice for 2-3 hr. Make sure the lobes are &#34;spread out&#34; during fixation. This will facilitate identification of anatomical landmarks after reconstruction.</li>
	<li>Wash in excess PBS for 30 min.</li>
	<li>Dehydrate the pancreas stepwise in methanol (33, 66%, 100%), 15 min/step. This minimizes the formation of bubbles during bleaching (see step 5) and prevents cell destruction during freeze-thawing (see step 7).</li>
	<li>Incubate the tissue in freshly prepared MeOH:DMSO:H2O2 bleaching buffer in a 2:1:3 ratio at RT for 24 hr to quench endogenous tissue fluorescence. For larger samples, exchange to new bleaching buffer and incubate for another 24 hr.</li>
	<li>Wash in excess MeOH, ON.</li>
	<li>Freeze-thaw for at least 5 cycles in -80 &#176;C - RT to facilitate antibody penetration.</li>
	<li>Rehydrate stepwise back to TBST (33, 66%, 100%), 15 min/step.</li>
	<li>Block in TBST with 10% serum (preferably from the same species in which the secondary antibody was generated), 5% DMSO and 0.01% NaAz for 12-24 hr at RT</li>
	<li>Incubate with primary antibodies in blocking buffer for 48 hr, at RT, extend to 72 hr for larger samples (antibodies used here are listed in the table of reagents).</li>
	<li>Wash in excess TBST, ON.</li>
	<li>Incubate with fluorescently conjugated secondary antibodies for 48 hr, at RT, extend to 72 hr for larger samples.</li>
	<li>Wash in excess TBST, ON.</li>
</ol>
1.2
The following procedure describes how to mount the sample in agarose and attach it to the custom made sample holder (see Figure 7) prior to OPT scanning.
<ol>
	<li>Separate the splenic, duodenal and gastric lobes according to Figure 3A in H&#246;rnblad et al3. The relationship between the lobes is further clarified in H&#246;rnblad et al11.</li>
	<li>Prepare 1.5% (w/v) low melting temperature agarose in dH2O, filter, allow to cool to 37 &#176;C and rinse the tissue in dH2O to wash away the detergent and remove bubbles before agarose-embedding on ice.</li>
	<li>Cut out an agarose block enclosing your sample and leave a ~1 cm spacer between the sample and the base of the agarose. Trim sharp edges (&#8804; 90 &#176;) of the agarose block to reduce light scattering.</li>
	<li>Dehydrate the sample stepwise in MeOH (33, 66%, 100%), allowing time to equilibrate between each step. When the specimen sinks it is considered to be equilibrated.</li>
	<li>Clear the sample in a 1:2 solution of Benzyl Alcohol: Benzyl Benzoate (BABB) until it becomes transparent. Exchange BABB solution and incubate for an additional 12 hr.</li>
	<li>Position the cleared sample in the sample holder and secure it by inserting 2 needles through the agarose spacer via the drilled holes in the flanges of the holder.</li>
	<li>Place the sample in the scanner and submerge it into a cuvette filled with BABB clearing solution. When comparing a series of pancreata the same magnification should be used for all scans. The magnification should be optimized for the largest sample in the series.</li>
</ol>
1.3 Positioning of specimen at the AR
The following protocol describes the procedure to precisely position a sample using the COM-AR algorithm. This procedure is only applicable when the ROI includes the entire specimen. For detailed descriptions of the algorithms, please see Cheddad et al2.
<ol>
	<li>Acquire images of the specimen in two positions for both the anatomy and signal channels. Position1 at 0 &#176; (associated with the X-axis), displaying the largest projection area, and position2 at 90 &#176; (associated with the Z-axis). We are using the GFP channel to visualize the anatomy.</li>
	<li>Apply the expectation-maximization (EM) algorithm on the anatomy images to threshold the ROI.</li>
	<li>Calculate the COM points (x-coordinates) of the binary images obtained in step 2 for both the 0 &#176; and 90 &#176; projections.</li>
	<li>Superimpose a vertical line passing through the identified COM point calculated in step 3 on the 0 &#176; and 90 &#176; images of the signal channel.</li>
	<li>Use the images acquired in step 4 as references to move the sample so that the centre line of the Field of View passes through the found COM points of the sample.</li>
</ol>
1.4 Scanning
<ol>
	<li>Adjust exposure times to achieve the highest signal to noise ratio possible without saturating any areas of the projection images. Repeat for all channels to be scanned.</li>
	<li>Select the filter set for the first fluorescence channel to be scanned. Emission from shorter &#955; fluorophores may excite fluorophores with longer &#955; and thereby cause photo bleaching. To minimize this possibility, scan the fluorophore with longest excitation &#955; first.</li>
	<li>Open the shutter to illuminate the sample and collect the fluorescence signal over 360 &#176;, rotating the sample along the vertical axis for each channel. The step angle used for the NIR-OPT setup is 0.9 &#176; and for the Bioptonics 3001 scanner 0.45 &#176;.</li>
	<li>Select the appropriate filter for the next channel and proceed as above.</li>
</ol>
2. Computational Processing and Reconstruction
2.1 Post-acquisition misalignment detection and correction (A-value tuning)
In projection tomography, it is in general necessary to assign a post-alignment value to the projections to fine-tune the images position along the axis of rotation prior to reconstruction. However, a small aberration in the angle of the camera towards the optical axis can cause non-uniform A-values along the length of the sample and thus induce geometric distortions. To avoid such distortions, a computational method to find the accurate and unified post-alignment value (A-value) throughout the entire specimen may be applied2.
<ol>
	<li>Use a discrete Fourier transform to divide a projection of the specific signal at 0 &#176; and 180 &#176; into 8 pixels height blocks and calculate the shift along the x-axis (the A-value) between each block.</li>
	<li>Apply a linear least squares regression to help calculate the angle &#952;&#39;, which describes the slope of the x-axis-shift along the length of the specimen, and to find a rotational centre point.</li>
	<li>Correct for misalignments during scanning by rotating all projections &#952;&#39;/2 around the rotational centre point.</li>
</ol>
2.2 Contrast limited adaptive histogram equalization (CLAHE)
To facilitate detection and segmentation of objects (islets) exhibiting very weak signals, which are at risk to be &#34;thresholded out&#34; during reconstruction and/or segmentation for quantitative assessments, a CLAHE algorithm may be applied to the projection images. The CLAHE operation is performed with two major intensity transformations:
<ol>
	<li>The local contrast is estimated and equalized within non-overlapping blocks in the projection image.</li>
	<li>The intensities are then normalized at the border regions between blocks through bilinear interpolation.</li>
</ol>
The name contrast-limited refers to the clip limit, which is set to avoid saturating pixels in the image. In this protocol, the MATLAB built-in function &#34;adapthisteq&#34; was used and applied with the default clip limit of 0.01 and a tile size of 256. Note, the optimal tile size needs to be tested empirically and can vary depending on the specimen analyzed. More details on the algorithm and examples can be found in H&#246;rnblad et al3.
Note! The above listed computational processing steps (including COM-AR, A-Value tuning and CLAHE, see 1.3-2.2) are built upon standard algorithms and are executed in MATLAB (Mathworks).
2.3 Tomographic reconstruction and iso-surface rendering
<ol>
	<li>Using a filtered back-projection algorithm, the corrected and normalized images may now be reconstructed with unified misalignment compensation and minimal requirement for optimization of dynamic range. In this protocol, all reconstructions are carried out using the filtered back projection method available in the NRecon software (Skyscan), version 1.6.8 (<a href="http://www.skyscan.be/products/downloads.htm" target="_blank">http://www.skyscan.be/products/downloads.htm</a>). Note, the magnification of an imaged object depends on its distance from the focal point of the lens unless parallel beam geometry is implemented in the imaging setup. Therefore, when importing a projection dataset to the NRecon software it is important to include the correct object to source distance (mm) and rotational direction of the scanner (for counter clockwise input &#34;cc&#34; and for clockwise input &#34;cw&#34;) in the accompanying log file, to avoid cone beam induced artifacts during reconstruction.</li>
	<li>To visualize and quantify the stack of virtual sections obtained, generate 3D iso-surfaces using suitable image processing software such as Imaris or Volocity.</li>
</ol>
Murine islet isolation and transplantation procedures were performed at the Diabetes Research Institute&#39;s Preclinical Cell Processing and Translational Model Core under protocols reviewed and approved by the University of Miami Institutional Animal Care and Use Committee. The ethical committee for animal research, northern Sweden, approved all other experiments involving animals.

<ol>
	<li>Sharpe, 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=Optical+projection+tomography+as+a+tool+for+3D+microscopy+and+gene+expression+studies.">Optical projection tomography as a tool for 3D microscopy and gene expression studies.</a> Science. 296, 541-545 (2002).</li><li>Cheddad, A., Svensson, C., Sharpe, J., Georgsson, F., Ahlgren, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+Processing+Assisted+Algorithms+for+Optical+Projection+Tomography.">Image Processing Assisted Algorithms for Optical Projection Tomography.</a> IEEE Trans. Med. Imaging. , (2012).</li><li>Hornblad, A., Cheddad, A., Ahlgren, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+improved+protocol+for+optical+projection+tomography+imaging+reveals+lobular+heterogeneities+in+pancreatic+islet+and+beta-cell+mass+distribution.">An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and beta-cell mass distribution.</a> Islets. 3, 204-208 (2011).</li><li>Holmberg, D., Ahlgren, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+the+pancreas:+from+ex+vivo+to+non-invasive+technology.">Imaging the pancreas: from ex vivo to non-invasive technology.</a> Diabetologia. 51, 2148-2154 (2008).</li><li>Ahlgren, U., Gotthardt, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Approaches+for+imaging+islets:+recent+advances+and+future+prospects.">Approaches for imaging islets: recent advances and future prospects.</a> Adv. Exp. Med. Biol. 654, 39-57 (2010).</li><li>Sharpe, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+projection+tomography.">Optical projection tomography.</a> Annu. Rev. Biomed. Eng. 6, 209-228 (2004).</li><li>Alanentalo, T., 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=Tomographic+molecular+imaging+and+3D+quantification+within+adult+mouse+organs.">Tomographic molecular imaging and 3D quantification within adult mouse organs.</a> Nat. Methods. 4, 31-33 (2007).</li><li>Alanentalo, T., 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=High-resolution+three-dimensional+imaging+of+islet-infiltrate+interactions+based+on+optical+projection+tomography+assessments+of+the+intact+adult+mouse+pancreas.">High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas.</a> J. Biomed. Opt. 13, 054070 (2008).</li><li>Alanentalo, T., 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=Quantification+and+Three-Dimensional+Imaging+of+the+Insulitis-Induced+Destruction+of+beta-Cells+in+Murine+Type+1+Diabetes.">Quantification and Three-Dimensional Imaging of the Insulitis-Induced Destruction of beta-Cells in Murine Type 1 Diabetes.</a> Diabetes. 59, 1756-1764 (2010).</li><li>Sun, G., 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=Ablation+of+AMP-activated+protein+kinase+alpha1+and+alpha2+from+mouse+pancreatic+beta+cells+and+RIP2.Cre+neurons+suppresses+insulin+release+in+vivo.">Ablation of AMP-activated protein kinase alpha1 and alpha2 from mouse pancreatic beta cells and RIP2.Cre neurons suppresses insulin release in vivo.</a> Diabetologia. 53, 924-936 (2010).</li><li>Hornblad, A., Eriksson, A. U., Sock, E., Hill, R. E., Ahlgren, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impaired+spleen+formation+perturbs+morphogenesis+of+the+gastric+lobe+of+the+pancreas.">Impaired spleen formation perturbs morphogenesis of the gastric lobe of the pancreas.</a> PLoS One. 6, e21753 (2011).</li><li>Lindstr&#246;m, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+physiology+of+the+Obese-Hyperglycemic+Mice+(ob/ob+Mice).">The physiology of the Obese-Hyperglycemic Mice (ob/ob Mice).</a> The Scientific World JOURNAL. 7, 665-685 (2007).</li><li>Bock, T., Pakkenberg, B., Buschard, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+background+determines+the+size+and+structure+of+the+endocrine+pancreas.">Genetic background determines the size and structure of the endocrine pancreas.</a> Diabetes. 54, 133-137 (2005).</li></ol>

No conflicts of interest declared.

The described techniques for OPT imaging enables extraction of spatial and quantitative parameters throughout the volume of the murine pancreas. Due to limitations in achievable resolution for this type of mesoscopic imaging it should be noted that, as for most imaging modalities, the larger the specimen the lower the resolution (Although the use of a higher resolution CCD should increase the resolution of the OPT scan). Hence, for the assessment of intact pancreatic mouse lobes, the technique at present does not provide single cell resolution although close (approx. 15-20 &mu;m)7. Still, for the extraction of BCM distribution in the mouse pancreas the protocols have provided data that more than well match those obtained by e.g. point counting morphometry3,13 It should be noted that although implementation of the CLAHE protocol allows for detection of significantly more islets, these islets are generally smaller and do not contribute substantially to the overall &beta;-cell volumes.
The immunohistochemical protocols involved are relatively lengthy (up to two weeks), but the actual hands on time for specimen preparation is short and therefore the technique is well suited for the study of large cohorts of animals9. If the potential of heterogenic distribution patterns is a focus for the investigation, it should be stressed that care should be taken in the steps concerning fixation and mounting to avoid that the pancreatic tissue becomes fixed in an unfavorable way and a flat ("spread out") mount of the tissue should be strived for to facilitate such assessments.
An important issue when performing OPT is that the sample's COM is fixed at the axis of rotation and that it does not move, either vertically or horizontally, during the scanning procedure. Therefore it is essential to have a stable mechanical setup and a well-functioning system for attaching the sample. We solved this issue by constructing a new mount (Figure 7). 
Parallel geometry was not true for our NIR-OPT or Bioptonics 3001 scanner, which was detected as a vertical shift between the back and front positions of peripheral objects in the projection images recorded. By adjusting the object to source distance in the log file of the respective scanner (see 2.3.1) we could significantly improve the quality of our data and correct for geometric distortions at the far edges of the projection images, which is of particular importance when assessing larger specimens.
In the current protocol, we provide a suggestion of filter sets that allow visualization of three different specific channels and an "anatomy" channel in assessments of intact pancreatic preparations. Obviously these settings could be modulated to better fit the fluorochromes utilized for a given study although, as with all forms of fluorescent microscopy, the potential hazard of signal bleed-through should be carefully evaluated. The study of insulin labeled islets with fluorochromes that are excited above 750 nm has not yet been possible by us using the metal halide lamp that our set up utilizes. It is possible that a camera with even higher quantum efficiency in the pertinent wavelengths in combination with alternative light sources (e.g. diode lasers) could increase the potential of NIR-OPT further and allow for imaging at even higher wavelengths.
OPT imaging is a highly versatile technique for spatial and quantitative assessments of biomedical specimen on the mm-cm scale. Although the protocols presented here have been developed for the main purpose of pancreas/diabetes research they should be possible to translate to research on other species, specimen types and markers. By the potential to visualize several distinct channels in intact pancreatic preparations, NIR-OPT imaging further holds the potential as a tool to evaluate the uptake specificity of contrast agents intended for non-invasive assessments by other imaging modalities, as long as these contrast agents could be designed to carry also a fluorophore detectable by OPT.

A correction was made to <a href="/video/50238">Near Infrared Optical Projection Tomography for Assessments of &beta;-cell Mass Distribution in Diabetes Research</a>. In Protocol section 1.1, the order of the listed chemicals MeOH:H2O2:DMSO has accidentally been switched and should instead be MeOH:DMSO:H2O2.
Protocol section 1.1 was changed from:
Incubate the tissue in freshly prepared MeOH:H2O2:DMSO bleaching buffer in a 2:1:3 ratio at RT for 24 hr to quench endogenous tissue fluorescence. For larger samples, exchange to new bleaching buffer and incubate for another 24 hr.
to:
Incubate the tissue in freshly prepared MeOH:DMSO:H2O2 bleaching buffer in a 2:1:3 ratio at RT for 24 hr to quench endogenous tissue fluorescence. For larger samples, exchange to new bleaching buffer and incubate for another 24 hr.

A correction was made to <a href="/video/50238">Near Infrared Optical Projection Tomography for Assessments of &beta;-cell Mass Distribution in Diabetes Research</a>. In Protocol section 1.1, the order of the listed chemicals MeOH:H2O2:DMSO has accidentally been switched and should instead be MeOH:DMSO:H2O2.

Erratum: Near Infrared Optical Projection Tomography for Assessments of &beta;-cell Mass Distribution in Diabetes Research

The insulin producing &beta;-cells are key for the body's ability to control blood glucose homeostasis. Therefore, assessments of pancreatic BCM distribution are imperative to many areas of pre-clinical diabetes research. In evaluations of therapeutic regimes for example, the impact of targeted gene ablation on endocrine cell differentiation or studies of diabetes aetiology in rodent models for the disease often depend on such analyses. Traditionally, these types of assessments have relied on time consuming stereological approaches that are difficult to perform due to the size and complex anatomical constitution of the pancreas. Most high resolution imaging approaches at present (typically optical), do not provide sufficient penetration depth to allow whole pancreas imaging in rodents. Conversely, imaging approaches that are not limited by their penetration depth (typically nuclear) provide to poor resolution to resolve the full BCM distribution and are hampered by the lack of adequate contrast agents4,5. 
Optical projection tomography is a 3D imaging modality that enables high-resolution assessments of biomedical specimens on the mm to cm scale6. Hereby, information on the spatial position and volume of the individual insulin expressing islets of Langerhans may be extracted throughout the volume of the pancreas in normal and diabetic mice3,7-10. The aim of the current study is to further enhance the capacity of this technique for the assessment of pancreatic &beta;-cells; their endogenous distribution, when grafted into other tissues, their relation to other pancreatic constituents (such as infiltrating cell types) and in larger pancreatic preparations than previously possible.
The near infrared optical projection tomography (NIR-OPT) setup
In the below protocols, an OPT scanner based on the original set up described by Sharpe et al1, adapted to imaging in the near infrared range is described and used. For single channel assessments of the mouse pancreas (e.g. of BCM), the SkyScan 3001 (Bioptonics) scanner may be used. 
A metal halide lamp that provides higher excitation energy than a mercury arc lamp at wavelengths above 650nm, supplies the excitation light. The light is transferred through a liquid light guide. A useful combination of fluorochromes and band pass filters for NIR fluorescence imaging and channel separation are shown in Figure 3. The emitted light is detected with a back illuminated CCD camera, with high quantum efficiency in the NIR spectrum. The OPT scanning is automated using a LabView platform that controls the camera and stepper motor. To support samples in the size of intact rat pancreata, a protected silver coated mirror and a large cuvette is used. Finally, a sample holder that eliminates unwanted vertical movements of the sample during the scan was designed.

<table border="1">
<tbody>
 <tr>
 <td>Name of 			Reagent/Material</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td>Methanol</td>
 <td>Scharlau</td>
 <td>ME03162500</td>
 <td></td>
 </tr>
 <tr>
 <td>30% H2O2</td>
 <td>Scharlau</td>
 <td>HI01362500</td>
 <td></td>
 </tr>
 <tr>
 <td>Benzyl Alcohol</td>
 <td>Scharlau</td>
 <td>AL01611000</td>
 <td></td>
 </tr>
 <tr>
 <td>Benzyl Benzoate</td>
 <td>Scharlau</td>
 <td>BE01851000</td>
 <td></td>
 </tr>
 <tr>
 <td>Low-meltingpoint 			agarose</td>
 <td>LONZA</td>
 <td>50100</td>
 <td></td>
 </tr>
 <tr>
 <td>Paraformaldehyde 			(PFA)</td>
 <td>Sigma-Aldrich</td>
 <td>158127</td>
 <td></td>
 </tr>
 <tr>
 <td>DMSO</td>
 <td>Sigma-Aldrich</td>
 <td>D5879</td>
 <td></td>
 </tr>
 <tr>
 <td>Triton-X100</td>
 <td>Sigma-Aldrich</td>
 <td>T8787</td>
 <td></td>
 </tr>
 <tr>
 <td>Mouse anti-aSMA-Cy3</td>
 <td>Sigma-Aldrich</td>
 <td>C6198</td>
 <td>Primary antibody</td>
 </tr>
 <tr>
 <td>Rabbit anti-CD3</td>
 <td>Sigma-Aldrich</td>
 <td>C7930</td>
 <td>Primary antibody</td>
 </tr>
 <tr>
 <td>Guinea Pig anti-Ins </td>
 <td>DAKO</td>
 <td>A0564</td>
 <td>Primary antibody</td>
 </tr>
 <tr>
 <td>Donkey anti 			GP-IRDye680</td>
 <td>LI-COR Biosciences</td>
 <td>926-32421</td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			Rb-DyeLight750</td>
 <td>Thermo Scientific</td>
 <td>35570</td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			GP-Alexa594</td>
 <td>Molecular Probes</td>
 <td>A-11076</td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			GP-Alexa488</td>
 <td>Molecular Probes</td>
 <td>A-11008 </td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			GP-Alexa594</td>
 <td>Molecular Probes</td>
 <td>A-11012 </td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			GP-Alexa680</td>
 <td>Molecular Probes</td>
 <td>A-21076 </td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>Goat anti 			GP-Alexa750</td>
 <td>Molecular Probes</td>
 <td>A-21039 </td>
 <td>Secondary antibody</td>
 </tr>
 <tr>
 <td>OPT Skyscan 3001</td>
 <td>Bioptonics</td>
 <td></td>
 <td>OPT-Scanner</td>
 </tr>
 <tr>
 <td>Leica MZ FLIII </td>
 <td>Leica Microsystems</td>
 <td></td>
 <td>Stereomicroscope</td>
 </tr>
 <tr>
 <td>Leica Objective 0.5x</td>
 <td>Leica Microsystems</td>
 <td>10446157</td>
 <td></td>
 </tr>
 <tr>
 <td>Leica Camera adapter 			1.0x</td>
 <td>Leica Microsystems</td>
 <td>10445930</td>
 <td></td>
 </tr>
 <tr>
 <td>EL6000 Metal Halide </td>
 <td></td>
 <td>11504115</td>
 <td>Lightsource</td>
 </tr>
 <tr>
 <td>Liquid Light Guide</td>
 <td></td>
 <td>11504116</td>
 <td></td>
 </tr>
 <tr>
 <td>Cuvette</td>
 <td>Hellma Analytics</td>
 <td>6030-OG</td>
 <td>55 x 55 x 52.5 mm</td>
 </tr>
 <tr>
 <td>Mirror</td>
 <td>Edmund Optics</td>
 <td>F68-334</td>
 <td>50 x 50 mm</td>
 </tr>
 <tr>
 <td>Andor Ikon-M </td>
 <td>Andor Technology</td>
 <td>DU934N-BV</td>
 <td>Back-illuminated CCD</td>
 </tr>
 <tr>
 <td>Filterset</td>
 <td>Chroma Technology </td>
 <td>41021-MZFLIII</td>
 <td>TXR, Alexa-594, Cy3</td>
 </tr>
 <tr>
 <td>Filterset</td>
 <td>Chroma Technology </td>
 <td>41022-MZFLIII</td>
 <td>IRDye680, Alexa-680</td>
 </tr>
 <tr>
 <td>Filterset</td>
 <td>Chroma Technology </td>
 <td>49037-MZFLIII</td>
 <td>Dylight750, 			Alexa-750</td>
 </tr>
 <tr>
 <td>ProteinG-Sepharose 			beads </td>
 <td>GE Healthcare</td>
 <td>17-0618-01</td>
 <td>Protein G Sepharose 			4 Fast Flow </td>
 </tr>
 <tr>
 <td>Sodium Azide</td>
 <td>Sigma-Aldrich</td>
 <td>08591</td>
 <td>Sodium azide 0.1 M 			solution</td>
 </tr>
 </tbody>
</table>

near infrared optical projection tomography for assessments of &beta;-cell mass distribution in diabetes research

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of pancreatic tissue, such as the rat pancreas, and to increase the number of channels (cell types) that may be studied in a single specimen. We further describe the implementation of a number of computational tools that provide: 1/ accurate positioning of a specimen's (in our case the pancreas) centre of mass (COM) at the axis of rotation (AR)2; 2/ improved algorithms for post-alignment tuning which prevents geometric distortions during the tomographic reconstruction2 and 3/ a protocol for intensity equalization to increase signal to noise ratios in OPT-based BCM determinations3. In addition, we describe a sample holder that minimizes the risk for unintentional movements of the specimen during image acquisition. Together, these protocols enable assessments of BCM distribution and other features, to be performed throughout the volume of intact pancreata or other organs (e.g. in studies of islet transplantation), with a resolution down to the level of individual islets of Langerhans.

In the current report we describe a protocol for the extraction and computational processing of BCM data in rodent pancreata (and other tissues) using NIR-OPT (Figure 1). As illustrated in Figure 2, tissue autofluorescense from pancreatic specimen is as expected markedly decreased in the NIR spectrum. This leads to a significant increase in mean signal to noise (S:N) ratio for the assessment of insulin labeled islets of Langerhans. By the adaptations of OPT to imaging in the NIR part of the spectrum, as described herein, at least three specific channels may be visualized with sufficient S:N ratio to enable assessments of antibody labeled cell types throughout the volume of the murine pancreas with distinct channel separation (see Figure 3 and 4). Applied to imaging of diabetogenic processes and/or BCM assessments in general, the technique thus allows for the visualization and quantification of insulin positive areas in relation to surrounding and/or interacting cell types (see Figure 4). Such assessments are thanks to the increased tissue penetration depth obtained in the NIR range possible to perform in much larger specimens than previously, including the rat pancreas, which is 3-5 times larger than its mouse counterpart (see Figure 5). Regardless of whether visible or NIR wavelengths are utilized, the implementation of CLAHE may significantly facilitate OPT based assessments of BCM during different genetical and physiological conditions by increasing the detection sensitivity of the technique (see Figure 6). A blueprint for the developed sample holder is shown in Figure 7.
<img src="/files/ftp_upload/50238/50238fig1.jpg" alt="Figure 1" /> 
Figure 1. Flowchart depicting the critical steps for OPT based analyses of BCM in the murine pancreas. The time required to assess a typical mouse pancreas is 13-14 days. The majority of the time is consumed during tissue processing and immunohistochemical staining (10 days), tissue clearing requires approximately 2 days whereas the length of the scanning is dependent on the exposure time required (normally around 1 hr). The subsequent computational processing typically is performed within a day. Note, the relatively lengthy staining protocol is ideally suited for batch processing of larger amounts of specimens.
<img src="/files/ftp_upload/50238/50238fig2.jpg" alt="Figure 2" fo:content-width="6in" fo:src="/files/ftp_upload/50238/50238fig2highres.jpg" /> 
Figure 2. Signal to noise ratios for BCM assessments at different wavelengths. A mouse duodenal pancreatic lobe, stained for insulin and with a cocktail of fluorochrome-conjugated secondary antibodies (Alexa 488, 594, 680 and 750), was used to determine S:N ratios at different wavelengths. A, Images show the first projection frame for each signal channel. B, Graph illustrating the mean S:N for each signal channel. The ratios were determined as the mean islet intensity (based on 215 islets) divided by the background intensity (the endogenous tissue fluorescence from the exocrine tissue). C, Graph showing S:N ratios for the individual islets in each channel normalized to the S:N obtained for the Alexa 594 channel. One way ANOVA was used for statistical analyses. Significance levels indicated correspond to **p&lt;0.01. Scale bar in (A) corresponds to 1 mm. <a href="https://www.jove.com/files/ftp_upload/50238/50238fig2large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/50238/50238fig3.jpg" alt="Figure 3" fo:content-width="4in" fo:src="/files/ftp_upload/50238/50238fig3highres.jpg"/> 
Figure 3. Channel separation. A, Secondary antibodies conjugated with Alexafluor dyes listed in the table were immobilized separately on proteinG-sepharose beads. B, The fluorescent beads were then embedded at different levels in an agarose phantom and imaged using indicated filters.
<img src="/files/ftp_upload/50238/50238fig4.jpg" alt="Figure 4" fo:content-width="5.5in" fo:src="/files/ftp_upload/50238/50238fig4highres.jpg"/> 
Figure 4. OPT based multichannel imaging in diabetes research. A, OPT based iso-surface reconstruction of a pancreas (12 weeks, duodenal lobe) from the Non Obese Diabetic (NOD) model for type 1 diabetes. The specimen is stained for insulin (islet &beta;-cells, pseudo colored blue); smooth muscle &#x03B1;-actin (blood vessels, red) and CD3 (infiltrating T-lymphocytes, green). The corresponding secondary antibodies used were; Cy3, IRDye-680 and DyeLight-750 respectively. The insets (A'-A''') show the individual signal channels. B, OPT image (blow up view) of a mouse liver lobe (lobus sinister lateralis) grafted with syngenic islets and imaged with NIR-OPT two weeks post transplantation. The insulin expressing islets are pseuodocolored in blue and the smooth muscle &#x03B1;-actin positive vessels are in red. The approach enables assessments of islet graft distribution within the vascular network. Scale bars correspond to 1 mm.
<img src="/files/ftp_upload/50238/50238fig5.jpg" alt="Figure 5" fo:content-width="4in" fo:src="/files/ftp_upload/50238/50238fig5highres.jpg" /> 
Figure 5. NIR-OPT facilitates the imaging of larger specimens. A, Iso-surface rendering of the BCM distribution in a rat pancreas from the Zucker Fatty model for type 2 diabetes (splenic lobe at 9 months), exemplifying the possibility to image specimen on the rat pancreas scale by NIR-OPT. As determined by this technique the displayed lobe is ~6 times larger (v/v) than its mouse counterpart and harbors 10139 insulin expressing islets of Langerhans whose &beta;-cell volume makes up 1.32 % of the total lobular volume. B, Tomographic section corresponding to the broken line in (A) illustrating that islets from all depths of the tissue are detected. C, Iso-surface rendering of the BCM distribution in a mouse pancreas (splenic lobe at 8 weeks) displayed as a size reference. The displayed lobe harbors 2490 insulin expressing islets whose &beta;-cell volume makes up 0.89% of the total lobular volume. The pancreata are stained with GP anti-insulin followed by Alexa594 conjugated goat anti-GP (mouse) and IRDye 680 Conjugated Donkey anti-GP (rat) antibodies respectively. The specimens in (A-C) are depicted to scale and the scale bar in (C) corresponds to 2 mm.
<img src="/files/ftp_upload/50238/50238fig6.jpg" alt="Figure 6" fo:content-width="5in" fo:src="/files/ftp_upload/50238/50238fig6highres.jpg"/> 
Figure 6. CLAHE facilitates detection of islets in the murine pancreas by OPT imaging. A-C, Representative iso-surface rendered OPT images of a C57Bl/6 mouse pancreas (splenic lobe at 8 weeks) labeled for insulin. Iso-surface reconstructions of OPT images were performed before (A, pseudo colored green) and after the CLAHE protocol was applied (B, pseudo colored red). C, Overlay of the non-normalized data in (A) and the CLAHE processed data in (B). C'-C", Representative high magnification overlay of the non-normalized (A) and CLAHE processed (B) images. As shown by the presence of "red-only" islets, the CLAHE script facilitates the detection of small and low signal intensity islets. In the current example the depicted specimen (after CLAHE processing) harbored 2419 islets with a volume of 1.74 mm3 (Numbers based on the corresponding unprocessed projection data was 1057 islets with a volume of 1.77 mm3). D and E, Example data from control (D) and the ob/ob mouse model for type 2 diabetes12 (E) at 6 months implementing the CLAHE protocol. Note the massive general increase in islet size in the ob/ob pancreas (E). In (D) and (E) the pancreas outline (gray) is based on the signal from tissue autofluorescence. Scale bar in C is 500 &mu;m in A-C. Scale bar in C" corresponds 200 &mu;m in C' and C''. Scale bar in E corresponds to 1 mm in (D) and (E). Images in (A-C) are adapted from H&ouml;rnblad et al3 and were generated using the Bioptonics 3001 scanner.
<img src="/files/ftp_upload/50238/50238fig7.jpg" alt="Figure 7" /> 
Figure 7. Sample holder for attachment of OPT specimens. The specimen is secured by inserting needles through the agarose spacer via the pre-drilled holes in the flanges. The holder is hinged to the stepper motor via a strong magnet located in its base. This setup omits the use of unstable glues and prevents unwanted movements of the specimen during scanning.

Watch this Scientific Journal Video about Near Infrared Optical Projection Tomography for Assessments of Î²-cell Mass Distribution in Diabetes Research at JoVE.com

Near Infrared Optical Projection Tomography for Assessments of &amp;#946;-cell Mass Distribution in Diabetes Research

We describe the adaptation of optical projection tomography (OPT)1 to imaging in the near infrared spectrum, and the implementation of a number of computational tools. These protocols enable assessments of pancreatic &beta;-cell mass (BCM) in larger specimens, increase the multichannel capacity of the technique and increase the quality of OPT data.

Near Infrared Optical Projection Tomography for Assessments of &beta;-cell Mass Distribution in Diabetes Research

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of ...

near-infrared-optical-projection-tomography-for-assessments-cell-mass

Research

JoVE Journal

Medicine

16.3K vistas.  Umeå University. Se describe la adaptación de la tomografía óptica de proyección (OPT) 1 Para formación de imágenes en el espectro del infrarrojo cercano, y la aplicación de una serie de herramientas computacionales. Estos protocolos permiten la evaluación de páncreas β masa de células (BCM) en los especímenes más grandes, aumentar la capacidad de múltiples canales de la técnica y aumentar la calidad de los datos opt.

Video: Near Infrared Optical Projection Tomography for Assessments of &#946;-cell Mass Distribution in Diabetes Research

In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

The vascular response to injury is a well-orchestrated inflammatory response triggered by the accumulation of macrophages within the vessel wall leading to an accumulation of lipid-laden intra-luminal plaque, smooth muscle cell proliferation and progressive narrowing of the vessel lumen. The formation of such vulnerable plaques prone to rupture underlies the majority of cases of acute myocardial infarction. The complex molecular and cellular inflammatory cascade is orchestrated by the recruitment of T lymphocytes and macrophages and their paracrine effects on endothelial and smooth muscle cells.1
Molecular imaging in atherosclerosis has evolved into an important clinical and research tool that allows in vivo visualization of inflammation and other biological processes. Several recent examples demonstrate the ability to detect high-risk plaques in patients, and assess the effects of pharmacotherapeutics in atherosclerosis.4 While a number of molecular imaging approaches (in particular MRI and PET) can image biological aspects of large vessels such as the carotid arteries, scant options exist for imaging of coronary arteries.2 The advent of high-resolution optical imaging strategies, in particular near-infrared fluorescence (NIRF), coupled with activatable fluorescent probes, have enhanced sensitivity and led to the development of new intravascular strategies to improve biological imaging of human coronary atherosclerosis.
Near infrared fluorescence (NIRF) molecular imaging utilizes excitation light with a defined band width (650-900 nm) as a source of photons that, when delivered to an optical contrast agent or fluorescent probe, emits fluorescence in the NIR window that can be detected using an appropriate emission filter and a high sensitivity charge-coupled camera. As opposed to visible light, NIR light penetrates deeply into tissue, is markedly less attenuated by endogenous photon absorbers such as hemoglobin, lipid and water, and enables high target-to-background ratios due to reduced autofluorescence in the NIR window. Imaging within the NIR 'window' can substantially improve the potential for in vivo imaging.2,5
Inflammatory cysteine proteases have been well studied using activatable NIRF probes10, and play important roles in atherogenesis. Via degradation of the extracellular matrix, cysteine proteases contribute importantly to the progression and complications of atherosclerosis8. In particular, the cysteine protease, cathepsin B, is highly expressed and colocalizes with macrophages in experimental murine, rabbit, and human atheromata.3,6,7 In addition, cathepsin B activity in plaques can be sensed in vivo utilizing a previously described 1-D intravascular near-infrared fluorescence technology6, in conjunction with an injectable nanosensor agent that consists of a poly-lysine polymer backbone derivatized with multiple NIR fluorochromes (VM110/Prosense750, ex/em 750/780nm, VisEn Medical, Woburn, MA) that results in strong intramolecular quenching at baseline.10 Following targeted enzymatic cleavage by cysteine proteases such as cathepsin B (known to colocalize with plaque macrophages), the fluorochromes separate, resulting in substantial amplification of the NIRF signal. Intravascular detection of NIR fluorescence signal by the utilized novel 2D intravascular NIRF catheter now enables high-resolution, geometrically accurate in vivo detection of cathepsin B activity in inflamed plaque.
In vivo molecular imaging of atherosclerosis using catheter-based 2D NIRF imaging, as opposed to a prior 1-D spectroscopic approach,6 is a novel and promising tool that utilizes augmented protease activity in macrophage-rich plaque to detect vascular inflammation.11,12 The following research protocol describes the use of an intravascular 2-dimensional NIRF catheter to image and characterize plaque structure utilizing key aspects of plaque biology. It is a translatable platform that when integrated with existing clinical imaging technologies including angiography and intravascular ultrasound (IVUS), offers a unique and novel integrated multimodal molecular imaging technique that distinguishes inflammatory atheromata, and allows detection of intravascular NIRF signals in human-sized coronary arteries.

In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

We detail a new near-infrared fluorescence (NIRF) catheter for 2-dimensional intravascular molecular imaging of plaque biology in vivo. The NIRF catheter can visualize key biological processes such as inflammation by reporting on the presence of plaque-avid activatable and targeted NIR fluorochromes. The catheter utilizes clinical engineering and power requirements and is targeted for application in human coronary arteries. The following research study describes a multimodal imaging strategy that utilizes a novel in vivo intravascular NIRF catheter to image and quantify inflammatory plaque in proteolytically active inflamed rabbit atheromata.

The vascular response to injury is a well-orchestrated inflammatory response triggered by the accumulation of macrophages within the vessel wall leading ...

Doppler Optical Coherence Tomography of Retinal Circulation

Noncontact retinal blood flow measurements are performed with a Fourier domain optical coherence tomography (OCT) system using a circumpapillary double circular scan (CDCS) that scans around the optic nerve head at 3.40 mm and 3.75 mm diameters. The double concentric circles are performed 6 times consecutively over 2 sec. The CDCS scan is saved with Doppler shift information from which flow can be calculated. The standard clinical protocol calls for 3 CDCS scans made with the OCT beam passing through the superonasal edge of the pupil and 3 CDCS scan through the inferonal pupil. This double-angle protocol ensures that acceptable Doppler angle is obtained on each retinal branch vessel in at least 1 scan. The CDCS scan data, a 3-dimensional volumetric OCT scan of the optic disc scan, and a color photograph of the optic disc are used together to obtain retinal blood flow measurement on an eye. We have developed a blood flow measurement software called "Doppler optical coherence tomography of retinal circulation" (DOCTORC). This semi-automated software is used to measure total retinal blood flow, vessel cross section area, and average blood velocity. The flow of each vessel is calculated from the Doppler shift in the vessel cross-sectional area and the Doppler angle between the vessel and the OCT beam. Total retinal blood flow measurement is summed from the veins around the optic disc. The results obtained at our Doppler OCT reading center showed good reproducibility between graders and methods (&lt;10%). Total retinal blood flow could be useful in the management of glaucoma, other retinal diseases, and retinal diseases. In glaucoma patients, OCT retinal blood flow measurement was highly correlated with visual field loss (R2&gt;0.57 with visual field pattern deviation). Doppler OCT is a new method to perform rapid, noncontact, and repeatable measurement of total retinal blood flow using widely available Fourier-domain OCT instrumentation. This new technology may improve the practicality of making these measurements in clinical studies and routine clinical practice.

Total retinal blood flow is measured by Doppler optical coherence tomography and semi-automated grading software.

Noncontact retinal blood flow measurements are performed with a Fourier domain optical coherence tomography (OCT) system using a circumpapillary double ...

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

The generation and analysis of vascular lesions in appropriate animal models is a cornerstone of research into cardiovascular disease, generating important information on the pathogenesis of lesion formation and the action of novel therapies. Use of atherosclerosis-prone mice, surgical methods of lesion induction, and dietary modification has dramatically improved understanding of the mechanisms that contribute to disease development and the potential of new treatments.
Classically, analysis of lesions is performed ex vivo using 2-dimensional histological techniques. This article describes application of optical projection tomography (OPT) to 3-dimensional quantitation of arterial lesions. As this technique is non-destructive, it can be used as an adjunct to standard histological and immunohistochemical analyses.
Neointimal lesions were induced by wire-insertion or ligation of the mouse femoral artery whilst atherosclerotic lesions were generated by administration of an atherogenic diet to apoE-deficient mice.
Lesions were examined using OPT imaging of autofluorescent emission followed by complementary histological and immunohistochemical analysis. OPT clearly distinguished lesions from the underlying vascular wall. Lesion size was calculated in 2-dimensional sections using planimetry, enabling calculation of lesion volume and maximal cross-sectional area. Data generated using OPT were consistent with measurements obtained using histology, confirming the accuracy of the technique and its potential as a complement (rather than alternative) to traditional methods of analysis.
This work demonstrates the potential of OPT for imaging atherosclerotic and neointimal lesions. It provides a rapid, much needed ex vivo technique for the routine 3-dimensional quantification of vascular remodelling.

Ex vivo analysis of arterial lesions from animal models of cardiovascular disease classically relies on histological and immunohistochemical techniques. These provide 2-dimensional measurements in 3-dimensional lesions. This manuscript describes the generation of arterial lesions for quantitative analysis in 3-dimensions using optical projection tomography.

The generation and analysis of vascular lesions in appropriate animal models is a cornerstone of research into cardiovascular disease, generating ...

Tissue-simulating Phantoms for Assessing Potential Near-infrared Fluorescence Imaging Applications in Breast Cancer Surgery

Inaccuracies in intraoperative tumor localization and evaluation of surgical margin status result in suboptimal outcome of breast-conserving surgery (BCS). Optical imaging, in particular near-infrared fluorescence (NIRF) imaging, might reduce the frequency of positive surgical margins following BCS by providing the surgeon with a tool for pre- and intraoperative tumor localization in real-time. In the current study, the potential of NIRF-guided BCS is evaluated using tissue-simulating breast phantoms for reasons of standardization and training purposes.
Breast phantoms with optical characteristics comparable to those of normal breast tissue were used to simulate breast conserving surgery. Tumor-simulating inclusions containing the fluorescent dye indocyanine green (ICG) were incorporated in the phantoms at predefined locations and imaged for pre- and intraoperative tumor localization, real-time NIRF-guided tumor resection, NIRF-guided evaluation on the extent of surgery, and postoperative assessment of surgical margins. A customized NIRF camera was used as a clinical prototype for imaging purposes.
Breast phantoms containing tumor-simulating inclusions offer a simple, inexpensive, and versatile tool to simulate and evaluate intraoperative tumor imaging. The gelatinous phantoms have elastic properties similar to human tissue and can be cut using conventional surgical instruments. Moreover, the phantoms contain hemoglobin and intralipid for mimicking absorption and scattering of photons, respectively, creating uniform optical properties similar to human breast tissue. The main drawback of NIRF imaging is the limited penetration depth of photons when propagating through tissue, which hinders (noninvasive) imaging of deep-seated tumors with epi-illumination strategies.

Near-infrared fluorescence (NIRF) imaging may improve therapeutic outcome of breast cancer surgery by enabling intraoperative tumor localization and evaluation of surgical margin status. Using tissue-simulating breast phantoms containing fluorescent tumor-simulating inclusions, potential clinical applications of NIRF imaging in breast cancer patients can be assessed for standardization and training purposes.

Inaccuracies in intraoperative tumor localization and evaluation of surgical margin status result in suboptimal outcome of breast-conserving surgery ...

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Optical coherence tomography (OCT) is the optical equivalent of ultrasound imaging, based on the backscattering of near infrared light. OCT provides real time images with a 15 &#181;m axial resolution at an effective tissue penetration of 2-3 mm. Within the OCT images the loss of signal intensity per millimeter of tissue penetration, the attenuation coefficient, is calculated. The attenuation coefficient is a tissue specific property, providing a quantitative parameter for tissue differentiation. 
Until now, renal mass treatment decisions have been made primarily on the basis of MRI and CT imaging characteristics, age and comorbidity. However these parameters and diagnostic methods lack the finesse to truly detect the malignant potential of a renal mass. A successful core biopsy or fine needle aspiration provides objective tumor differentiation with both sensitivity and specificity in the range of 95-100%. However, a non-diagnostic rate of 10-20% overall, and even up to 30% in SRMs, is to be expected, delaying the diagnostic process due to the frequent necessity for additional biopsy procedures. 
We aim to develop OCT into an optical biopsy, providing real-time imaging combined with on-the-spot tumor differentiation. This publication provides a detailed step-by-step approach for percutaneous, needle based, OCT of renal masses.

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Optical coherence tomography (OCT) is a high resolution imaging technique that allows analysis of tissue specific optical properties providing the means for tissue differentiation. We developed needle based OCT, providing real-time imaging combined with on-the-spot tumor differentiation. This publication describes a method for percutaneous, needle based OCT of renal masses.

Optical coherence tomography (OCT) is the optical equivalent of ultrasound imaging, based on the backscattering of near infrared light. OCT provides real ...

Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli

Direct thrombus imaging visualizes the root cause of thromboembolic infarction. Being able to image thrombus directly allows far better investigation of stroke than relying on indirect measurements, and will be a potent and robust vascular research tool. We use an optical imaging approach that labels thrombi with a molecular imaging thrombus marker&#160;&#8212; a Cy5.5 near-infrared fluorescent (NIRF) probe that is covalently linked to the fibrin strands of the thrombus by the fibrin-crosslinking enzymatic action of activated coagulation factor XIIIa during the process of clot maturation. A micro-computed tomography (microCT)-based approach uses thrombus-seeking gold nanoparticles (AuNPs) functionalized to target the major component of the clot: fibrin. This paper describes a detailed protocol for the combined in vivo microCT and ex vivo NIRF imaging of thromboemboli in a mouse model of embolic stroke. We show that in vivo microCT and fibrin-targeted glycol-chitosan AuNPs (fib-GC-AuNPs) can be used for visualizing both in situ thrombi and cerebral embolic thrombi. We also describe the use of in vivo microCT-based direct thrombus imaging to serially monitor the therapeutic effects of tissue plasminogen activator-mediated thrombolysis. After the last imaging session, we demonstrate by ex vivo NIRF imaging the extent and the distribution of residual thromboemboli in the brain. Finally, we describe quantitative image analyses of microCT and NIRF imaging data. The combined technique of direct thrombus imaging allows two independent methods of thrombus visualization to be compared: the area of thrombus-related fluorescent signal on ex vivo NIRF imaging vs. the volume of hyperdense microCT thrombi in vivo.

This protocol describes the application of combined near-infrared fluorescent (NIRF) imaging and micro-computed tomography (microCT) for visualizing cerebral thromboemboli. This technique allows the quantification of thrombus burden and evolution. The NIRF imaging technique visualizes fluorescently labeled thrombus in excised brain, while the microCT technique visualizes thrombus inside living animals using gold-nanoparticles.

Direct thrombus imaging visualizes the root cause of thromboembolic infarction. Being able to image thrombus directly allows far better investigation of ...

Ultrahigh Resolution Mouse Optical Coherence Tomography to Aid Intraocular Injection in Retinal Gene Therapy Research

HR-SD-OCT is utilized to monitor the progression of photoreceptor degeneration in live mouse models, assess the delivery of therapeutic agents into the subretinal space, and to evaluate toxicity and efficacy in vivo. HR-SD-OCT uses near infrared light (800-880 nm) and has optics specifically designed for the unique optics of the mouse eye with sub-2-micron axial resolution. Transgenic mouse models of outer retinal (photoreceptor) degeneration and controls were imaged to assess the disease progression. Pulled glass microneedles were used to deliver sub retinal injections of adeno-associated virus (AAV) or nanoparticles (NP) via a trans-scleral and trans-choroidal approach. Careful positioning of the needle into the subretinal space was required prior to a calibrated pressure injection, which delivers fluid into the sub retinal space. Real time subretinal surgery was conducted on our retinal imaging system (RIS). HR-SD-OCT demonstrated progressive uniform retinal degeneration due to expression of a toxic mutant human mutant rhodopsin (P347S) (RHOP347S) transgene in mice. HR-SD-OCT allows rigorous quantification of all the retinal layers. Outer nuclear layer (ONL) thickness and photoreceptor outer segment length (OSL) measurements correlate with photoreceptor vitality, degeneration, or rescue. The RIS delivery system allows real-time visualization of subretinal injections in neonatal (~P10-14) or adult mice, and HR-SD-OCT immediately determines success of delivery and maps areal extent. HR-SD-OCT is a powerful tool that can evaluate the success of subretinal surgery in mice, in addition to measuring vitality of photoreceptors in vivo. HR-SD-OCT can also be used to identify uniform animal cohorts to evaluate the extent of retinal degeneration, toxicity, and therapeutic rescue in preclinical gene therapy research studies.

Here we demonstrate a novel approach to using high resolution spectral-domain optical coherence tomography (HR-SD-OCT) to assist delivery of gene therapy agents into the subretinal space, assess its areal coverage, and characterize photoreceptor vitality.

HR-SD-OCT is utilized to monitor the progression of photoreceptor degeneration in live mouse models, assess the delivery of therapeutic agents into the ...

A New Best Practice for Validating Tail Vein Injections in Rat with Near-infrared-Labeled Agents

Intravenous (IV) administration of agents into the tail vein of rats can be both difficult and inconsistent. Optimizing tail vein injections is a key part of many experimental procedures where reagents need to be introduced directly into the bloodstream. Unwittingly, the injection can be subcutaneous, possibly altering the scientific outcomes. Utilizing a nanoemulsion-based biological probe with an incorporated near-infrared fluorescent (NIRF) dye, this method offers the capability of imaging a successful tail vein injection in vivo. With the use of a NIRF imager, images are taken before and after the injection of the agent. An acceptable IV injection is then qualitatively or quantitatively determined based on the intensity of the NIRF signal at the site of injection.

Here we present a method to validate tail vein injections in rats by utilizing near-infrared fluorescence imaging data from dyes incorporated into agents or biological probes. The tail is imaged before and after the injection, the fluorescent signal is quantified, and an assessment of the injection quality is made.

Intravenous (IV) administration of agents into the tail vein of rats can be both difficult and inconsistent. Optimizing tail vein injections is a key part ...

Semi-Minimal Invasive Method to Induce Myocardial Infarction in Rats and the Assessment of Cardiac Function by an Isolated Working Heart System

Myocardial infarction (MI) remains the main contributor to morbidity and mortality worldwide. Therefore, research on this topic is mandatory. An easily and highly reproducible MI induction procedure is required to obtain further insight and better understanding of the underlying pathological changes. This procedure can also be used to evaluate the effects or potency of new and promising treatments (as drugs or interventions) in acute MI, subsequent remodeling and heart failure (HF). After intubation and pre-operative preparation of the animal, an anesthetic protocol with isoflurane was performed, and the surgical procedure was conducted&#160;quickly. Using a minimally invasive approach, the left anterior descending artery (LAD) was located and occluded by a ligature. The occlusion can be performed acutely for subsequent reperfusion (ischemia/reperfusion injury). Alternatively, the vessel can be ligated permanently to investigate the development of chronic MI, remodeling or HF. Despite common pitfalls, the drop-out rates are minimal. Various treatments such as remote ischemic conditioning can be examined for their cardioprotective potential pre-, peri- and post-operatively. The post-operative recovery was quick as the anesthesia was precisely controlled and the duration of the operation was short. Post-operative analgesia was administered for three days. The minimally invasive procedure reduces the risk of infection and inflammation. Furthermore, it facilitates rapid recovery. The &#8220;working heart&#8221; measurements were performed ex vivo and enabled precise control of preload, afterload and flow. This procedure requires specific equipment and training for adequate performance. This manuscript provides a detailed step-by-step introduction for conducting these measurements.

This article presents an efficient method to perform myocardial ischemia and subsequent chronic reperfusion in rats using a minimally invasive approach. In addition, left ventricular hemodynamic function of rats is assessed by echocardiography and isolated working heart methods.&#160;

Myocardial infarction (MI) remains the main contributor to morbidity and mortality worldwide. Therefore, research on this topic is mandatory. An easily ...

How to Administer Near-Infrared Spectroscopy in Critically ill Neonates, Infants, and Children

Near infrared spectroscopy (NIRS) calculates regional tissue oxygenation (rSO2) using the different absorption spectra of oxygenated and deoxygenated hemoglobin molecules. A probe placed on the skin emits light that is absorbed, scattered, and reflected by the underlying tissue. Detectors in the probe sense the amount of reflected light: this reflects the organ-specific ratio of oxygen supply and consumption - independent of pulsatile flow. Modern devices enable the simultaneous monitoring at different body sites. A rise or dip in the rSO2 curve visualizes changes in oxygen supply or demand before vital signs indicate them. The evolution of rSO2 values in relation to the starting point is more important for interpretation than are absolute values.
A routine clinical application of NIRS is the surveillance of somatic and cerebral oxygenation during and after cardiac surgery. It is also administered in preterm infants at risk for necrotizing enterocolitis, newborns with hypoxic ischemic encephalopathy and a potential risk of impaired tissue oxygenation. In the future, NIRS could be increasingly used in multimodal neuromonitoring, or applied to monitor patients with other conditions (e.g., after resuscitation or traumatic brain injury).

This protocol is designed to assist clinicians to measure regional tissue oxygenation at different body sites in infants and children. It can be used in situations where tissue oxygenation is potentially compromised, particularly during cardiopulmonary bypass, when using non-pulsatile cardiac-assist devices, and in critically ill neonates, infants and children.

Near infrared spectroscopy (NIRS) calculates regional tissue oxygenation (rSO2) using the different absorption spectra of oxygenated and deoxygenated ...