Translation of Intravital microscopy findings is challenged by its shallow depth penetration into tissue. Here we describe a dorsal window chamber mouse model that enables co-registration of intravital microscopy and clinically applicable imaging modalities (e.g., CT, MRI) for direct spatial correlation, potentially streamlining clinical translation of intravital microscopy findings.
Preclinical intravital imaging such as microscopy and optical coherence tomography have proven to be valuable tools in cancer research for visualizing the tumor microenvironment and its response to therapy. These imaging modalities have micron-scale resolution but have limited use in the clinic due to their shallow penetration depth into tissue. More clinically applicable imaging modalities such as CT, MRI, and PET have much greater penetration depth but have comparatively lower spatial resolution (mm scale).
To translate preclinical intravital imaging findings into the clinic, new methods must be developed to bridge this micro-to-macro resolution gap. Here we describe a dorsal skinfold window chamber tumor mouse model designed to enable preclinical intravital and clinically applicable (CT and MR) imaging in the same animal, and the image analysis platform that links these two disparate visualization methods. Importantly, the described window chamber approach enables the different imaging modalities to be co-registered in 3D using fiducial markers on the window chamber for direct spatial concordance. This model can be used for validation of existing clinical imaging methods, as well as for the development of new ones through direct correlation with “ground truth” high-resolution intravital findings.
Finally, the tumor response to various treatments-chemotherapy, radiotherapy, photodynamic therapy-can be monitored longitudinally with this methodology using preclinical and clinically applicable imaging modalities. The dorsal skinfold window chamber tumor mouse model and imaging platforms described here can thus be used in a variety of cancer research studies, for example, in translating preclinical intravital microscopy findings to more clinically applicable imaging modalities such as CT or MRI.
Tumor microvasculature is an important component of the tumor microenvironment that can be a target for therapy and a determinant of treatment response. In the preclinical setting, the microvasculature is typically studied using intravital microscopy in orthotopic or heterotopic window chamber animal models1,2. This has several advantages over histological studies since the imaging is done in live tissues and the tumor can be monitored longitudinally over several weeks or even months2,3. These studies can leverage the high-resolution imaging capabilities of intravital microscopy to study the delivery of therapeutics to the tumor4,5, the causes of treatment resistance6, and the response of the micro vessels to therapies such as antiangiogenic treatment7,8 and radiotherapy2,9.
Intravital microscopy clearly plays an important role in preclinical cancer research; however, how can tumor microenvironmental features be measured in the clinic? Microvascular information would be useful in the clinic for measuring blood supply and tumor cell hypoxia, which is important for determining treatment resistance in radiotherapy10, as well as the ability of the microvasculature to deliver chemotherapeutic agents to the surrounding tumor cells11. For example, in radiotherapy, spatial information on the structure and function of the tumor microvasculature may help personalize a patient's treatment plan by adjusting the fractionation schedule or by preferentially boosting the dose to avascular and likely hypoxic regions12.
Intravital microscopy can measure these important microvascular features since it has a very high resolution (μm scale); however, its depth penetration into tissue is limited to several hundred microns or a few millimeters, at most making clinical implementation challenging. Indeed, there are some novel applications of intravital microscopy in the clinic13; however, these are still limited to examinations of near-surface level tissue such as the skin14 or mucosal/endothelial linings of various body cavities via flexible catheters/endoscopes15,16.
More commonly, the microvasculature is studied using imaging modalities such as CT17 or MRI18. These clinical imaging modalities can image to any depth within the body, but they have a much lower spatial resolution (mm scale). Thus, there is a need to bridge this resolution gap between preclinical intravital microscopy and clinical imaging modalities to bring high-resolution and detailed microvascular information into the clinic19. Several functional imaging methods have been developed to improve the microvascular imaging capabilities of clinical imaging modalities such as dynamic contrast-enhanced (DCE) MRI and CT20, and Intravoxel incoherent motion (IVIM) MRI21. However, these are model-based methods that provide indirect measurements of the microvasculature and thus, must be validated with appropriate "ground truth" measurements of the microvasculature19,22.
We have developed a dorsal skinfold window chamber (DSFC) tumor mouse model to bridge this gap between preclinical intravital microscopy and clinically applicable imaging modalities such as CT and MRI. The DSFC provides direct access to the tumor for high-resolution, intravital microscopy imaging through a glass window but also clinically applicable imaging such as MRI as it is made of MR-compatible materials (plastic and glass). Furthermore, an included MATLAB code performs multimodality 3D co-registration for direct spatial correlations between preclinical intravital microscopy and clinically applicable imaging modalities. Here we will describe the design and surgery to install the DSFC as well as the procedure to co-register intravital microscopy and clinically applicable imaging modalities.
All animal procedures were performed in accordance with the Guide to the Care and Use of Experimental Animals which is set forth by the Canadian Council on Animal Care. Experiments were performed according to a protocol approved by the University Health Network Institutional Animal Care and Use Committee in Toronto, Canada.
1. Tumor inoculation landmarking
NOTE: "Landmarking" refers to the process of marking the skin of the mouse to indicate where the tumor cells should be injected to optimize DSFC placement. This landmarking procedure should be done on the same day or 1 day before the inoculation. The immunocompromised NOD.Cg-Rag1tm1Mom Il2rgtm1Wjl/SzJ (NRG) female mouse was used for this work.
2. Tumor inoculation
NOTE: In this study, we are using a human pancreatic cancer cell line (BxPC3). Other cell lines can also be used; however, specific cell culture steps may vary across different cell lines. Refer to the instructions included with cells for modifications to the below procedure.
3. Window chamber surgery
NOTE: The DSFC consists of four 3D-printed parts as shown in Figure 1. Schematics of each part are included in Supplementary File 1. All parts are printed with a biocompatible clear plastic resin. The main window chamber assembly consists of three parts (Figure 1A-C) with an additional fiducial marker ring (Figure 1D) that can be affixed during MRI or CT imaging.
Figure 1: Dorsal skinfold window chamber schematic. The main window chamber contains three parts. First, (A) the front frame is sutured underneath the skin of the mouse and contains a glass coverslip affixed using UV-cured glue. (B) The back frame is sutured to the front frame on the outside of the skin. (C) The support clip affixes to the bottom of the back frame and keeps the DSFC upright on the mouse body. (D) The fiducial marker ring contains seven 'wells' where fiducial markers can be inserted. The fiducial marker ring can be affixed to the front frame of the DSFC using the three support posts. (E) The full DSFC assembly with a fiducial marker ring is shown. Scale bars = 1 cm (A–D, at bottom left; E). Abbreviation: DSFC = dorsal skinfold window chamber. Please click here to view a larger version of this figure.
Figure 2: DSFC surgery procedure. (A) The mouse is prepared for surgery by removing the hair and disinfecting the skin. The subcutaneous tumor is indicated by the arrow. (B) The back frame is placed in the appropriate position and secured by three syringes as well as temporary sutures affixed to the black surgical guide. (C,D) The spacer locations (points 1-6) and hole are marked on both sides of the skin. (E) The skin is removed. (F–K) A temporary suture is threaded through the two layers of skin, front, and back frames of the DSFC to secure all the parts together. (L,M) The temporary suture is tightened, and the front frame is inserted underneath the skin. (N) Eight permanent sutures are placed to secure the DSFC. (O) Finally, the temporary suture is removed, and the support clip is attached. (P,Q) The same mouse is shown 2 weeks after surgery from both sides. Abbreviation: DSFC = dorsal skinfold window chamber. Please click here to view a larger version of this figure.
4. Optical imaging
5. Magnetic resonance imaging
Figure 3: DSFC MR imaging setup. (A) Side and (B) top views of the mouse positioned on the MRI bed with DSFC secured and immobilized. The mouse has a tail vein catheter for contrast agent injection and the fiducial maker ring is affixed to the front frame of the DSFC. Abbreviations: DSFC = dorsal skinfold window chamber; MR = magnetic resonance imaging. Please click here to view a larger version of this figure.
Figure 4: MRI slice locations with respect to fiducial markers and window chamber. (A) A diagram of the DSFC with fiducial marker ring attachment with the 11 overlaid MRI slices. Several T2-weighted images must be acquired to ensure that the slices are correctly aligned with the DSFC and tissue. (B,C) Correct positioning of the 11 slices with respect to the tissue in the DSFC from different orientations. (D) Slice 5 is the most superficial slice where intermodality correlation analysis will be performed. (E) Slice 6 contains no tissue signal indicating that it is properly aligned with the DSFC. (F) Finally, the 7 fiducial markers are clearly visible in slice 9. Scale bars = 5 mm. An 'X' on the axis indicates that the axis is going into the page and a circle indicates that the axis is coming out of the page. Abbreviations: DSFC = dorsal skinfold window chamber; MRI = magnetic resonance imaging. Please click here to view a larger version of this figure.
6. MRI to intravital microscopy co-registration
Figure 5: Multimodal point-based co-registration. (A) Color depth-encoded microvascular svOCT dataset; scale bar = 1 mm. (B) Brightfield microscopy image of the window chamber; scale bar = 2 mm. (C) Average of T2w MRI slices 8-11 showing the seven fiducial markers contained in the fiducial marker ring; scale bar = 5 mm. (C) First, the 'moving' T2w MRI dataset is co-registered to the 'fixed' brightfield microscopy image using the user-inputted green markers on both image sets. Next, the 'moving' brightfield microscopy image and co-registered MRI image are co-registered to the 'fixed svOCT dataset' using the blue markers in A and B. The final co-registered dataset includes the (D) svOCT, (E) brightfield microscopy image, and (F) functional MRI parameter map. The black voxels in F are outside of the tumor and are therefore not considered in the analysis. For D-F, scale bar = 1 mm. Abbreviations: svOCT = speckle variance optical coherence tomography; MRI = magnetic resonance imaging. Please click here to view a larger version of this figure.
Speckle variance optical coherence tomography (svOCT) was performed to obtain large field-of-view (FOV) 3D microvascular images (6 x 6 mm2 lateral x 1 mm depth). To obtain these images, a previously described swept source OCT system based on a quadrature interferometer was used23. OCT images were acquired by stitching together two laterally adjacent 3 x 6 mm2 FOV scans. Each B-scan consisted of 400 A-scans and was performed 24x per location (25 ms apart) to enable accurate speckle variance processing as described in our previous work24.
To co-register the MRI dataset to the microvascular svOCT dataset several steps were performed. The first step was done in step 5.12 and is illustrated in Figure 4. This step co-registers the two datasets along the depth direction (z-axis in Figure 4). To co-register the datasets along the lateral dimensions (xy-plane in Figure 4), the fiducial marker ring along with a series of two point-based affine registrations are used. First, the MRI dataset is co-registered to the brightfield microscopy image by matching the fiducial marker locations in the ring, with the corresponding divots around the perimeter of the glass window on the front frame (green points in Figure 5B,C). Next, vascular landmarks that are identifiable in both the svOCT microvascular dataset and the brightfield microscopy image are used to co-register the brightfield image (and corresponding MRI dataset) to the svOCT dataset (blue points in Figure 5A,B). Therefore, the brightfield microscopy image acts as an intermediate-resolution image that contains features from both the MRI (divot locations for fiducial markers) and svOCT microvascular image (microvascular landmarks) to enable multimodality co-registration across vastly different imaging resolutions.
Images of the successful co-registration between all three modalities (microvascular imaging, widefield microscopy, and MRI) for a window chamber preparation mouse with no tumor (healthy bare skin) and tumor are shown in Figure 6A–C and Figure 6D–F, respectively. Once all three modalities are co-registered, correlation analysis can be performed. To take advantage of the co-registered datasets, spatial correlations should be performed either on a voxel-by-voxel basis or by using appropriate averaging techniques over selected volumes of interest. Figure 7 shows some representative results of the correlation between svOCT-derived microvascular metrics and functional MRI-derived macrovascular metrics. These plots include the data from two tumor-bearing mice and one 'healthy' bare skin mouse. These plots were generated using a 1 mm2 lateral x 500 μm depth sliding window volume of interest (VOI) technique with a 500 μm lateral step size with each point representing one position of the VOI. For the healthy mouse, vascular analysis was restricted to a 3 x 3 mm2 region of interest in the center of the FOV to match the 3D spatial extent of the examined tumors. A more detailed study involving svOCT to MRI correlations using this analysis method can be found in our recent work22.
Figure 6: Co-registered healthy and tumor imaging datasets. The top row shows the (A) color depth-encoded svOCT, (B) co-registered brightfield microscopy image with analysis region shown by the blue dotted line, and (C) co-registered DCE-MRI parameter map for a healthy bare skin mouse. The bottom row shows the (D) color depth-encoded svOCT, (E) co-registered brightfield microscopy image with tumor contour shown by the blue dotted line, and (F) co-registered DCE-MRI parameter map for a tumor-bearing mouse. The black voxels in C and F are outside the analysis region and tumor, respectively, and are, therefore, not considered. Scale bars = 1 mm. Abbreviations: svOCT = speckle variance optical coherence tomography; DCE-MRI = dynamic contrast-enhanced magnetic resonance imaging. Please click here to view a larger version of this figure.
Figure 7A shows MRI's ktrans parameter versus the svOCT-measured vascular volume fraction (VVF). VVF is the proportion of tissue that is occupied by vessels and ktrans is the rate constant for gadolinium moving from the intravascular space to the extravascular space. ktrans is obtained by fitting of the Toft's model to the gadolinium time concentration curves on a voxel-by-voxel basis25,26. VVF measurements from svOCT show that tumors have a smaller VVF than healthy tissue, potentially indicating the presence of poorly vascularized regions inside the tumor. As ktrans is dependent on blood flow, vascular permeability, and capillary surface area25,26, a positive correlation between ktrans and VVF is expected since a larger VVF implies a larger vascular surface area and blood flow. However, more studies such as Dextran extravasation experiments are needed to further elucidate the impact of tumor vessel permeability in this relationship27.
Figure 7B shows the correlation between MRI's time to peak contrast enhancement (TTP) versus svOCT's mean distance to the nearest vessel (). TTP is a semi-quantitative metric that measures the time required for the gadolinium time concentration curve to reach its maximum value and this is calculated on a voxel-by-voxel basis26. The is a svOCT microvascular metric that measures the average distance to the nearest vessel within the analyzed volume22. In general, there is a smaller in healthy tissue compared to tumor tissue, indicating that the healthy microvasculature is more densely packed leading to more uniformly oxygenated tissue, and the tumor tissue is less well vascularized28. The higher values in tumor tissue may indicate the presence of avascular tissue regions. Figure 7B shows a positive correlation between TTP and . This demonstrates that healthy tissue is well vascularized with gadolinium washing into and out of the tissue efficiently and quickly. However, tumor microvasculature is more malformed with larger intervascular distances, which increases the time for gadolinium to reach the tissue thus resulting in an increase in TTP.
Figure 7: MRI to svOCT macro to micro-vascular spatial correlations. These plots contain the findings from two tumor-bearing mice and one healthy bare-skin mouse using a x 1 x 1 mm2 lateral × 500 μm depth, sliding window VOI with a 500 μm step size, with each point representing the average values of the given metric for one position of the sliding window VOI. Abbreviations: svOCT = speckle variance optical coherence tomography; DCE-MRI = dynamic contrast-enhanced magnetic resonance imaging; VOI = volume of interest. Please click here to view a larger version of this figure.
Supplementary File 1: 3D STL files of window chamber components. A zip file containing the 4 components of the DSFC chamber (front frame, back frame, support clip, and fiducial marker ring), which was used to 3D print the parts. Abbreviation: DSFC = dorsal skinfold window chamber. Please click here to download this File.
Supplementary File 2: MATLAB script for multimodal image co-registration. This MATLAB script (Multimodal_Image_Register.m) takes in the brightfield microscopy, microvascular image data, and structural and functional (DCE and/or IVIM parameter maps) MR images and outputs all datasets co-registered to the microvascular dataset. The MATLAB script is written to support the following file formats: MRI – .dcm, svOCT – .mat, and microscopy – .jpg, or .TIF. The MATLAB code is written to accommodate the file sizes/resolutions that are described in the protocol section: MRI – 32 x 32 mm field-of-view with 64 x 64 matrix for 0.5 x 0.5 mm in-plane resolution; 10 contiguous imaging slices, microscopy – 2D image with >1.5 cm2 field of view (any resolution acceptable), and svOCT – 6 x 6 mm field of view 7.5 µm resolution. The MATLAB code should be modified if different file formats, image resolution, or imaging fields of view are used. Abbreviations: DCE = dynamic contrast-enhanced; IVIM = intravoxel incoherent motion; MRI = magnetic resonance imaging; svOCT = speckle variance optical coherence tomography. Please click here to download this File.
Supplementary Figure S1: Temporary sutures to affix window chamber to the skin of the mouse. The suture starts at (1) and goes through the front side skin of the mouse and through the front frame of the window chamber. (2) The suture then goes through the back skin of the mouse and out the back frame of the window chamber. The suture is then threaded back through the back frame, (3) into the backside skin, through the front frame, and (4) back out the front skin. (5) The suture is then brought back through both DSFC frames and skin layers. (6) Finally, the suture is brought back through the top hole of the window chamber and all layers of the skin. The DSFC + skin assembly is then brought together by tightening and knotting the sutures at (1). Abbreviation: DSFC = dorsal skinfold window chamber. Please click here to download this File.
Supplementary Figure S2: Optimal placement of permanent sutures. The permanent sutures should be threaded through the front skin/frame out of the back skin/frame, back through the entire assembly, and knotted in the front. Each number represents one individual suture. The sutures should be placed in the order of the numbers to evenly apply pressure to the entire assembly as it is secured. Please click here to download this File.
Supplementary Figure S3: 3D printed stage for intravital microscopy imaging. (A) A 3D printed stage with isoflurane gas anesthesia attachment is used for intravital microscopy imaging. (B) The window chamber is secured to the stage by three bolts to immobilize it and eliminate any breathing motion. (C) Two heating elements connected in series to a DC power source are glued to the underside of the stage to maintain the mouse's body temperature while anesthetized. Please click here to download this File.
In this work, we have developed a workflow to perform both intravital microscopy and clinically applicable imaging (CT, MRI, and PET) in the same animal. This was done with the goal of translating preclinical microscopy findings to the clinic by direct correlation of intravital microscopy with clinical imaging modalities such as MRI. Although conventional DSFC designs are made of metal2,3, we have adapted the DSFC to be MR-compatible by using 3D-printed window chambers made of a biocompatible plastic resin. The registration of the modalities was accomplished by the use of fiducial markers for the clinical imaging modalities and by obtaining a widefield microscopy image to match the fiducial marker locations on the DSFC with the vascular landmarks that can be seen on the widefield microcopy image as well as the high-resolution intravital microscopy microvascular images. A MATLAB code to co-register the modalities was developed and is included in Supplementary File 2.
DSFCs are not new in preclinical research2,3,27,29,30,31,32 with some studies developing MR-compatible DSFCs out of biocompatible plastics for intravital microscopy and clinically applicable imaging27,29,30,31,32. However, these studies perform whole-tumor level correlations with MRI and intravital microscopy findings. The novelty of this work is our ability to directly co-register intravital microcopy images to clinically applicable imaging modalities using a fiducial marker attachment to the DSFC enabling direct spatial correlation analysis. Gaustad et al. have also performed spatial correlation analysis between MRI and intravital microscopy; however, specific detailed steps on the co-registration process between modalities are not included29. This work provides a protocol for spatially correlating multiple modalities with each other using the fiducial marker ring attachment and includes a MATLAB pipeline.
There are several details of the DSFC design and procedure that offer improved imaging and tumor growth characteristics compared to other similar studies. First, most window chamber methods involve inoculation of the tumor after window chamber surgery. This is typically done since it is easier to center the tumor in the window chamber FOV; however, previous work has shown that this is suboptimal, leading to an 'unrealistic' mono-layer tumor growth with spontaneous regression in many instances33. Instead, we allow the tumor to grow unrestricted in the skin for several weeks before DSFC implantation, which allows for a more realistic 3D tumor with improved growth characteristics33.
This DSFC design is also extremely lightweight at 0.83 g with a large imaging window FOV (1.2 cm diameter glass coverslip). A unique characteristic of our DSFC design is that it does not include a snap ring to secure the glass coverslip to the front frame. Instead, the glass coverslip is permanently affixed using UV-cured glue allowing the bottom portion of the front frame to be completely flat. Furthermore, the front frame is secured underneath the skin of the mouse. This was done to minimize the chance of any air gap between the tumor-glass interface. It has been noted in previous designs that if there is a gap at the tumor-glass interface there is a high probability of fluid buildup in this region. This significantly impacts results by attenuating the intravital microscopy signal thus reducing image quality. Additionally, it makes it more difficult to target the MRI slices to the tumor since this fluid buildup will appear bright on the T2w localization scans. This makes it challenging to identify which slices are imaging the tissue and which slices are imaging the fluid buildup in the DSFC. One drawback to this design is that if the glass coverslip breaks, it cannot be easily replaced. We have also designed a front frame that is made entirely out of an optically clear plastic thus eliminating the glass breakage issue34.
Throughout this procedure, several crucial steps are more technically challenging than others. One step that may cause difficulties is during the DSFC surgery when suturing the front frame to the skin (protocol steps 3.18-3.24). It is important to keep some extra loose skin along the top of the front frame of the DSFC. If this is not done, then the skin in this region will lose blood supply and become necrotic, thus ruining the preparation. Another technically challenging task is the targeting of the MRI slice package to include the most superficial part of tissue underneath the glass. If this slice is not targeted precisely to the region, then partial volume effects with the glass may occur, thus leading to erroneous MRI results. Uneven surfaces of the tissue underneath the glass of the DSFC may make this even more challenging. It is important to ensure that the skin is taut inside the DSFC to keep the tissue surface as flat and uniform as possible to reduce this source of error.
Here we have demonstrated the use of this DSFC workflow using svOCT, which has a large imaging FOV of 6 x 6 mm2. This large FOV is not common in other types of intravital microscopy with many imaging methods having FOVs of 1 mm2 or smaller13. This may cause some challenges when co-registering the widefield microscopy image to the high-resolution intravital microscopy images since microvascular landmarks may be difficult to identify. To overcome this, another widefield microscopy image may be required with a smaller FOV (~6 x 6 mm2) to act as an additional intermediate co-registration step between the imaging modalities to further refine the region of interest to the one imaged by intravital microscopy.
In this work, we have focused on imaging the tumor microvasculature, but other tumor microenvironmental components may be imaged using this model as well. For example, intravital microscopy has been shown to successfully image the tumor stroma35, and diffusion-weighted MRI methods such as diffusion tensor imaging or diffusion kurtosis imaging may have the ability to provide information on the structure of the tumor stroma36,37. This workflow may provide exciting opportunities to translate stromal tumor information into the clinic by directly co-registering high-resolution intravital microscopy images of the tumor stroma with MRI-based measurements of the stroma. However, it should be noted that because this method uses vascular landmarks for co-registration, those should also be present in intravital microscopy stromal images.
The presented workflow may prove to be an important step in bridging the gap between preclinical intravital microscopy and clinically applicable imaging modalities. This model can be used to validate clinical imaging methods with ground truth intravital microscopy images, develop new clinically applicable imaging methods through comparison with intravital microscopy, and longitudinally monitor the response of treatments using multiple imaging modalities.
The authors have nothing to disclose.
We thank Dr. Carla Calçada (Postdoctoral Fellow, Princess Margaret Cancer Centre) and Dr. Timothy Samuel (Ph.D. Student, Princess Margaret Cancer Centre) for help with tumor cell culturing and inoculation protocol development. Dr. Kathleen Ma, Dr. Anna Pietraszek, and Dr. Alyssa Goldstein (Animal Research Centre, Princess Margaret Cancer Centre) helped with surgery protocol development. Jacob Broske (Medical Engineering Technologist, Princess Margaret Cancer Centre) and Wayne Keller (Hardware Client Executive, Javelin Technologies – A TriMech Group Company) 3D printed the window chambers. James Jonkman (Advanced Optical Microscopy Facility, University Health Network) provided valuable guidance for brightfield and fluorescence microscopy image acquisition.
Cell Culture Materials | |||
BxPC-3 Human Pancreatic Cancer Cells | ATCC (American Type Culture Collection) | CRL-1687 | |
Corning Matrigel Basement Membrane Matrix, LDEV-free, 10 mL | Corning | 354234 | |
Corning Stripettor Ultra Pipet Controller | Corning | 07-202-350 | |
Dulbecco Phospphate buffered saline without Calcium, Magnesium, or phenol red, 500 mL | Gibco | 14190144 | |
Fetal Bovine Serum (Canada), 500 mL | Sigma-Aldrich | F1051-500ML | |
Penicillin-Streptomycin 100x (liquid,stabilized, sterile-filtered, cell culture tested) | Sigma-Aldrich | P4333-100ML | |
RPMI Medium 1640 (1x), liquid; with L-Glutamine, 500 mL | Gibco | 11875093 | |
TrypLE Express Enzyme, 500 mL | Gibco | 12605028 | |
Window Chamber Materials | |||
12 mm Glass Coverslip | Harvard Apparatus | CS-12R No. 1.5 | |
Connex 500 3D Printer | Stratasys | N/A | |
Biocompatible clear MED610 resin | Stratasys | RGD810 | |
Loctite AA 3105 UV curable glue | Loctite | LCT1214249 | |
Window chamber back frame | Trimech Inc | N/A | |
Window chamber fiducial marker | Trimech Inc | N/A | |
Window Chamber front frame | Trimech Inc | N/A | |
Window chamber support clip | Trimech Inc | N/A | |
inoculation and Surgery Materials | |||
BD SafetyGlide Insulin Syringes with Permanently Attached Needles, 0.5 mL, 29 G x 1/2" | BD | CABD305932 | |
Betadine Solution | Betadine | AP-B002C2R98U | |
Cidex OPA 14 Day Solution 3.8 L | ASP | JOH20394 | |
Disposable Surgical Underpads 23 inch x 24 inch | Kendall | 7134 | |
Eye lubricant | Optixcare | 50-218-8442 | |
Hair removal cream | Nair | 061700222611 | |
Halstead Hemostatic Forceps | Almedic | 7742-A12-150 | |
Heating pad | Sunbeam | B086MCN59R | |
Iris Scissors | Almedic | 7601-A8-690 | |
Isoflurane | Sigma | 792632 | |
Metacam | Boehringer Ingelheim Animal Health USA Inc | NDC 0010-6015-03 | |
NOD.Cg-Rag1tm1Mom Il2rgtm1Wjl/SzJ mouse | the Jackson laboratory | 7799 | |
Peanut Clipper & Trimmer | Wahl | 8655-200 | |
SOFSILK Nonabsorbable Surgical Suture #5-0 with 3/8" Taper point needle (17 mm) (Wax Coated,Braided Black Silk, Sterile) | Syneture | VS880 | |
Splinter Forceps | Almedic | 7725-A10-634 | |
MR Imaging | |||
3D printed window chamber immobilization device. | custom 3D printed, refer to figure 3 for details. | ||
Convection heating device | 3M Bair Hugger | 70200791401 | |
Drug injection system | Harvard Apparatus | PY2 70-2131 | PHD 22/2200 MRI compatible Syringe Pump |
Gadovist 1.0 | Bayer | 2241089 | |
Respiratory monitoring system | SAII | Model 1030 | MR-compatible monitoring and gating system for small animals. |
Tail vein catheter (27 G 0.5" ) | Terumo Medical Corp | 15253 | |
Optical Imaging | |||
3D printed imaging stage | Custom 3D printed, refer to supplementary figure 3 for details. | ||
12 V 7 W Flexible Polyimide Heater Plate Thin Adhesive PI Heating Film 25 mm x 50 mm | BANRIA | B09X16XCVS | Heating element used for mouse body temeprature regulation. |
DC power supply | BK Precission | 1761 | Used to power the heating element. |
Leica MZ FLIII | Leica Microsystems | 15209 | |
svOCT imaging system | In-house made imaging system. Details can be found in reference 23. | ||
Software | |||
MATLAB Software | MathWorks | R2020A |