This work was supported by NSF grants IDBR (DBI-1455613), PFI:AIR-TT (IIP-1640707), NIH grants R21EY026380, R15EB019704 and R01EB025209, and Lehigh University startup fund.

1. Preparation of Cells
<ol>
	<li>Obtain cell lines from a qualified supplier. 
	NOTE: Verify that cells from the cell lines of interest can form spheroid in the culture media or with the help of a substrate (basement membrane matrix like Matrigel). Look into the literature9 or perform one round of a pre-experiment for a check.</li>
	<li>Thaw the frozen cells following the specific procedure provided by the cell-line supplier. A general procedure can be found elsewhere43.</li>
	<li>Culture the cells for 1-2 passages in 25 cm2 culture flasks. The cells are then ready to use for 3D cell culture.</li>
	<li>Monitor the health status of the cells every day and maintain them in an incubator under standard conditions (37 &#176;C, 5% CO2, 95% humidity). Refresh the media as needed. 
	NOTE: The culture medium consists of DMEM (4.5 g/L glucose), 1% antibiotic-antimycotic, 10% fetal bovine serum. Subculture cells before they reach confluence in the culture flask. Follow the cell culture guideline provided by the supplier. A general procedure can be found elsehwhere44.</li>
	<li>Perform 3D cell culture in multi-well plates based on the following general protocol9.
	<ol>
		<li>Remove the culture media from the culture flask and wash it with sterilized phosphate buffered saline (PBS, heated to 37 &#176;C).</li>
		<li>Resuspend the cells by adding 1 mL of trypsin ethylenediaminetetraacetic acid (EDTA, 0.5%) into the flask for 3 min. Then, add culture media to dilute the trypsin.</li>
		<li>Transfer the cell suspension into a 15 mL centrifuge tube and centrifuge for 5 min at 500 x g and room temperature.</li>
		<li>Remove the supernatant and resuspend cells with 4 mL of pre-warmed culture medium. Pipette one drop of sample onto a hemocytometer for cell counting to determine cell concentration. Dilute the cells to appropriate concentration for seeding (e.g., 3,000 cells/mL). 
		NOTE: Optimize the initial cell concentration of the spheroid for each cell-line and each type of multi-well plate (96-well, 384-well or 1536-well).</li>
		<li>Seed cells into an ultra-low attachment (ULA) round-bottomed multi-well plate. Add 200 &#181;L of cells suspension into each well at the concentration of 3,000 cells/mL so that each well has about 600 cells.</li>
		<li>At RT, centrifuge the whole plate using a plate adapter for 7 min, right after seeding, at a speed of 350 x g or the lowest speed available. 
		NOTE: The centrifuge helps gather cells to the center of the well to facilitate forming a single, uniform spheroid. The centrifuge step is performed only once at the beginning to form the tumor spheroids. It will not be repeated when the tumor spheroids start growing.</li>
		<li>Maintain the multi-well plate at 37 &#176;C and 5% CO2 in a culture incubator and refresh the culture media every 3 days. 
		NOTE: Growth time may vary for different 3D culture conditions. In our study, 3,000 cells/mL is used for both U-87 MG and HCT 116 cell lines in 96-well plates, so that the spheroid can grow to ~500 &#956;m in 4&#8210;7 days for HCT 116 cells. Consider adding media supplements and growth factors for different spheroid models, based on the general 3D culture protocol.</li>
		<li>Perform OCT imaging of tumor spheroids every 3&#8210;4 days for a longitudinal study of their growth. 
		NOTE: Recommended time points for OCT imaging would be day 4, day 7, day 11, day 14, day 18 and day 21.</li>
	</ol>
	</li>
</ol>
2. High-throughput OCT Imaging Platform
NOTE: See referenced work29,30,31,32,33,34 for a thorough review of principles and applications of OCT. See Figure 1 and Huang et al.42 for details of the custom OCT imaging system used in this study.
<ol>
	<li>Choose an appropriate broadband light source for the OCT system for tumor spheroid imaging. 
	NOTE: Here, a superluminescent diode (SLD, Figure 1A,B) with a central wavelength of ~1,320 nm and ~110 nm bandwidth was used as a broadband light source.</li>
	<li>Construct the reference arm and sample arm of the OCT system following the schematics (See Figure 1A,B for details). See the Table of Materials for a list of optical components to construct the OCT system. Ensure that the optical path length of the reference arm and sample arm are closely matched.</li>
	<li>Construct the spectrometer, including a collimator, a grating, an F-theta lens and a line-scan camera (See Figure 1C for setup34) for details of spectrometer design of OCT. Alternatively, select a commercial spectrometer that matches the center wavelength of the light source. Make sure that the spectrometer is aligned correctly to cover the entire laser bandwidth, to achieve high photon collection efficiency and to provide slow wash-out of the interference pattern.</li>
	<li>Characterize the performance of the OCT system, including the following metrics such as sample arm power, total imaging depth, depth-dependent sensitivity, axial resolution, depth of focus and lateral resolution. Place a weak reflector (e.g., a mirror with a neutral density filter) as a sample to measure the depth-dependent sensitivity, axial resolution, and depth of focus. Place a USAF resolution test chart target as the sample to check the lateral resolution. 
	NOTE: See references34,45 for definitions of metrics of OCT performance and protocols to characterize these metrics45. See Table 1 for a list of measured parameters for the custom OCT system used in our study.</li>
	<li>Select a motorized translation stage to provide horizontal movement of the multi-well plate to image tumor spheroids in different wells (See Figure 1B). Use a stage with a travel range larger than 108 mm x 72 mm to ensure a full scanning of all the wells of the multi-well plate. Use a 2D or 3D motorized translation stage with software control to enable precise location of each well and automation of the OCT system for high-throughput imaging.</li>
	<li>Use a plate adapter or design a plate holder (by 3D printing) to hold the multi-well plate in a fixed position.</li>
	<li>Correct the tilting and rotation of the multi-well plate using a 2D tilting stage and a rotation stage mounted on the translational stage (See Figure 1D), before conducting any OCT imaging to minimize variation of the focus plane from different wells. Use D2, D11, B6, D6, G6 as the guiding wells when monitoring their relative positions in the OCT images (Figure 1A).</li>
	<li>Adjust the rotation of the plate to ensure the edges of the plate are parallel with the direction of stage movement so that the wells remain at the same horizontal positions in the OCT images (Figure 1E). Adjust the tilting of the plate to be parallel to the optical table so that the wells remain at the same vertical locations for OCT imaging (Figure 1F). 
	NOTE: Adjustment of the tilting angle and focus help optimize the OCT image quality for all the wells. However, variations of the height of culture media in different wells may cause changes in optical paths which may lead to defocusing of the spheroid image. Auto-focus may be implemented to control the focal plane of OCT imaging to achieve optimized image quality. The adjustment step does not resolve poor OCT image quality of the tumor spheroid due to the following issues: the spheroid decentering due to initial seeding location; spheroid elevation when embedded in biofabricated extracellular matrices; poor plate quality with large variations of the height of well bottoms. Additional software control with auto-focus or self-alignment functions can be implemented to optimize the performance of the OCT imaging system.</li>
	<li>Use a custom computer program to control the OCT image acquisition and the stage movement to collect data from each well sequentially.</li>
</ol>
3. OCT Scanning and Processing of Tumor Spheroids
<ol>
	<li>On the day of the OCT imaging of tumor spheroids, take the multi-well plate from the incubator. Transfer the multi-well plate under the OCT imaging system. Place it on top of the plate adapter. 
	NOTE: OCT imaging of tumor spheroids may be performed with the polystyrene plate lid on or off. However, the water condensations on the lid due to evaporation from the wells may affect light transmission and distort the light path, yielding less optimal OCT images from the spheroids.</li>
	<li>Adjust the height of the plate by moving along the z-direction of the translation stage. Maintain the focal plane position at ~100&#8211;200 &#956;m below the top surface of each spheroid, to minimize the effect of the non-uniform depth-wise focal profile.</li>
	<li>Set a proper OCT scanning range (e.g., 1 mm x 1 mm) in the custom software to cover the whole tumor spheroid according to its development stages. Click Save Parameters to save the setting.</li>
	<li>Use the custom software to acquire 3D OCT images of tumor spheroids one by one for all the wells of the plate containing spheroids. Click the Preview button to view the preview image and click the Acquire button to acquire the OCT image. 
	NOTE: Ensure that the OCT spheroid data are collected when the stage is not in motion. The spheroid is usually located at the center of the U-bottom well. However, the spheroid may be shifted in the culture media when the stage is accelerating or decelerating due to the inertia of the spheroid in the culture media.</li>
	<li>Process 3D OCT datasets of tumor spheroids to generate OCT structural images with a custom C++ processing code. See Figure 2A for a flowchart of post-processing of OCT data. 
	NOTE: See Figure 4A for the generated 3D OCT structural images.
	<ol>
		<li>See Chapter 5 of Drexler and Fujimoto34 and Jian et al.46 for detail descriptions of post-processing steps of OCT data. Calibrate the pixel size in all three dimensions. Re-scale the OCT structural images on corrected scales. 
		NOTE: The distance in the axial direction (z-direction) of OCT images is a measure of the optical path difference between the reference arm and sample arm. Thus, the refractive index of the sample (n) needs to be taken into consideration when calibrating the pixel size in the axial direction for rescaling. In our study, we use n = 1.37 as the refractive index of the tumor spheroid42.</li>
	</ol>
	</li>
	<li>Generate the collage of spheroid images using 2D OCT Images in three cross-sectional XY, XZ, and YZ planes across the centroid of the spheroid. See Figure 3C&#8211;E for the representative output of collages of spheroid images. Perform image registration for all the spheroids, using the MATLAB function dftregistration47, to ensure that the centroids of all the spheroid are located approximately at the same location.</li>
	<li>Obtain 3D rendering of the spheroid using a commercial or custom software. 
	NOTE: The following steps show how to obtain the 3D rendering of tumor spheroids using a commercial software.
	<ol>
		<li>Load the 3D OCT data into the software.</li>
		<li>Click the Surpass panel. Then, click Add New Volume. Choose the Blend mode to use for 3D rendering.</li>
		<li>Adjust the viewing angle by dragging the image using the mouse pointer.</li>
	</ol>
	</li>
</ol>
4. Morphological Quantification of 3D Tumor Spheroids
NOTE: A custom written code in MATLAB processes this quantification. Click the Run button to initiate the process. See Figure 2B for the flowchart of the steps of morphological quantification of spheroids.
<ol>
	<li>Quantify spheroid diameter, height, and diameter-based volume.
	<ol>
		<li>Select 2D OCT Images in three cross-sectional XY, XZ, and YZ planes that cross the centroid of the spheroid.</li>
		<li>Measure the diameter and height of the spheroid in XY and XZ planes, respectively.</li>
		<li>Calculate diameter-based spheroid volume using: <img alt="Equation 1" src="/files/ftp_upload/59020/59020eq1v2.jpg" />, with a presumption of the spherical shape of tumor.</li>
	</ol>
	</li>
	<li>Quantify voxel-based spheroid volume.
	<ol>
		<li>Apply a 3D averaging filter on the OCT structural data of spheroid to remove speckles.</li>
		<li>Segment tumor spheroids using the Canny edge detection48 filter, frame by frame, with a proper threshold separating the tumor spheroid region from the well bottom.</li>
		<li>Group connective voxels for 3D data (see built-in function: bwconncomp).</li>
		<li>Calculate the mean distance between each connective voxel in the group and the spheroid centroid (manually chosen), for each group. Identify the spheroid region as the group with the minimum mean distance.</li>
		<li>Count the number of voxels within the spheroid region and then multiply by the actual volume of an individual voxel (volume/voxel), yielding the total volume of the spheroid.</li>
	</ol>
	</li>
</ol>
5. Dead-Cell Region Detection of 3D Tumor Spheroids
NOTE: In a homogeneous medium, OCT back-scattered intensity detected as a function of depth (I(z)) can be described by the Beer-Lambert Law49: <img alt="Equation 2" src="/files/ftp_upload/59020/59020eq2v2.jpg" />, where z represents the depth, &#956; is the optical attenuation coefficient, and I0 is the incident intensity to the sample. Hence the derived optical attenuation coefficient can be expressed as: <img alt="Equation 3" src="/files/ftp_upload/59020/59020eq3v2.jpg" />. Since OCT images are often plotted on a logarithmic scale, the slope of the OCT intensity profile can be retrieved to derive the optical attenuation coefficient. See Figure 2C for a flowchart of the generation of the optical attenuation maps.
<ol>
	<li>Perform segmentation to remove unwanted regions outside the spheroid. Perform 3D average filter to suppress the speckle noise that is inherent in OCT images.</li>
	<li>Obtain pixel-wise optical attenuation coefficients by linear fitting the log-scale OCT intensity profile over a certain depth range (moving window), extract its slope, and multiply the slope by -1/2. 
	NOTE: The attenuation coefficient at each voxel within the segmented spheroid region is calculated based on the slope of OCT intensity profile in a 10-voxel depth window (~40 &#956;m in depth), with the voxel located in the middle of the window.</li>
	<li>Apply the methods above (steps 5.1 and 5.2) to each axial scan in a frame and each frame in a 3D dataset containing the segmented spheroid region until optical attenuation coefficients for all voxels of the segmented spheroid region are calculated.</li>
	<li>Perform the binary thresholding to highlight the high-attenuation region. 
	NOTE: See Huang et al.42 for the determination of threshold of the high-attenuation region using histogram analysis.</li>
	<li>Highlight the binarized optical attenuation map on the original image to label the dead-cell region (blending). Generate the 3D-rendered image of the blended attenuation map to visualize the 3D distribution of the dead-cell region.</li>
</ol>
6. Histology and Immunohistochemistry
NOTE: Histology and immunohistochemistry (IHC) stained images of tumor spheroids are obtained to correlate with the corresponding OCT results.
<ol>
	<li>At selected time points, select 1-2 tumor spheroids from the multi-well plate for histology and IHC staining. Use a pipette with the 1 mL pipette tips to transfer the spheroid from the well to a 1.5 mL centrifuge tube. 
	NOTE: Cut the 1 mL pipette tip before transfer to ensure that the opening of the tip is larger than the size of the tumor spheroid to avoid damaging the structure of the spheroid.</li>
	<li>Collect each tumor spheroid in a single 1.5 mL microcentrifuge tube filled with 10% formaldehyde and fix for 48 h.</li>
	<li>Perform the histology and IHC processes for each spheroid, using standard paraffin embedding techniques. 
	NOTE: Stain 5 &#956;m thick sections of tumor spheroids for hematoxylin and eosin (H&#38;E) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) apoptosis detection. A counterstaining of hematoxylin is applied to TUNEL. A digital slide scanner was used to scan the stained sample and obtain high-resolution histological and IHC images.</li>
</ol>

The authors disclose no competing interest.

Tumor activity is highly relevant to its morphological structure. Similar to monitoring characteristic growth curve for 2D cell cultures, tracking the growth curve for 3D tumor spheroids is also a conventional approach to characterize the long-term spheroid growth behavior for different cell lines. Notably, we can characterize the drug response by analyzing tumor degradation or tumor regrowth directly reflected in the growth curve. Therefore, quantitative assessment of 3D tumor spheroids, including the size and volume, t...

Cancer is the second leading cause of death in the world1. Developing drugs targeting cancer is of crucial importance for patients. However, it is estimated that more than 90% of new anti-cancer drugs fail in the development phase because of a lack of efficacy and unexpected toxicity in clinical trials2. Part of the reason can be attributed to the use of simple two-dimensional (2D) cell culture models for compound screening, which provide results with limited predictive values of compound efficacy and toxicity for the following stages of drug discovery2,3,4. Recently, three-dimensional (3D) tumor spheroid models have been developed to provide clinically relevant physiological and pharmacological data for anti-cancer drug discovery3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25. Since these spheroids can mimic tissue-specific properties of tumors in vivo, such as nutrient and oxygen gradient, hypoxic core as well as drug resistance19, the use of these models can potentially shorten drug discovery timelines, reduce costs of investment, and bring new medicines to patients more effectively. One critical approach to evaluating compound efficacy in 3D tumor spheroid development is to monitor the spheroid growth and recurrence under treatments9,26. To do this, quantitative characterizations of the tumor morphology, involving its diameter and volume, with high-resolution imaging modalities, are imperative. 
Conventional imaging modalities, such as bright-field, phase contrast7,9,22,24, and fluorescence microscopy8,9,16,18,22 can provide a measurement of the spheroid's diameter but cannot resolve the overall structure of the spheroid in 3D space. Many factors contribute to these limitations, including penetration of the probing light in the spheroid; diffusion of the fluorescent dyes into the spheroid; emitting fluorescent signals from excited fluorescent dyes inside or on the opposite surface of the spheroid due to strong absorption and scattering; and depth-resolvability of these imaging modalities. This often leads to an inaccurate volume measurement. Development of the necrotic core in spheroids mimics necrosis in in vivo tumors6,10,15,19,25. This pathological feature is unlikely reproduced in 2D cell cultures19,25,27,28. With a spheroid size larger than 500 &#181;m in diameter, a three-layer concentric structure, including an outer layer of proliferating cells, a middle layer of quiescent cells, and a necrotic core, can be observed in the spheroid6,10,15,19,25, due to lack of oxygen and nutrients. Live and dead cell fluorescence imaging is the standard approach to label the boundary of the necrotic core. However, again, penetrations of both these fluorescent dyes and visible light hinder the potential to probe into the necrotic core to monitor its development in its actual shape.
An alternative 3D imaging modality, optical coherence tomography (OCT) is introduced to characterize the tumor spheroids. OCT is a biomedical imaging technique that is capable of acquiring label-free, non-destructive 3D data from up to 1-2 mm depths in biological tissues29,30,31,32,33,34. OCT employs low-coherence interferometry to detect back-scattered signals from different depths of the sample and provides reconstructed depth-resolved images at micron-level spatial resolutions in both lateral and vertical directions. OCT has been widely adopted in ophthalmology35,36,37 and angiography38,39. Previous studies have used OCT to observe the morphology of in vitro tumor spheroids in basement membrane matrix (e.g., Matrigel) and evaluate their responses to photodynamic therapy40,41. Recently, our group established a high-throughput OCT imaging platform to systematically monitor and quantify the growth kinetics of 3D tumor spheroids in multi-well plates42. Precise volumetric quantification of 3D tumor spheroids using a voxel counting approach and label-free necrotic tissue detection in the spheroids based on intrinsic optical attenuation contrast were demonstrated. This paper describes the details of how the OCT imaging platform was constructed and employed to obtain high-resolution 3D images of tumor spheroids. The step-by-step quantitative analyses of the growth kinetics of 3D tumor spheroids, including accurate measurements of spheroid diameter and volumes, is described. Also, the method of the non-destructive detection of necrotic tissue regions using OCT, based on the intrinsic optical attenuation contrast is presented.

<table>
	<tbody>
		<tr>
			<td>Custom Spectral Domain OCT imaging system</td>
			<td></td>
			<td></td>
			<td>Developed in our lab</td>
		</tr>
		<tr>
			<td>Superluminescent Diode (SLD)</td>
			<td>Thorlabs</td>
			<td>SLD1325</td>
			<td>light source</td>
		</tr>
		<tr>
			<td>2&#215;2 single mode fused fiber coupler, 50:50 splitting ratio</td>
			<td>AC Photonics</td>
			<td>WP13500202B201</td>
			<td></td>
		</tr>
		<tr>
			<td>Reference Arm</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Lens Tube</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Adapter</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Collimating Lens</td>
			<td>Thorlabs</td>
			<td>AC080-020-C</td>
			<td></td>
		</tr>
		<tr>
			<td>Focusing Lens</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Kinematic Mirror Mount</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mirror</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>1D Translational Stage</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Continuous neutral density filter</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pedestrial Post</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Clamping Fork</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sample Arm</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Lens Tube</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Adapter</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Collimating Lens</td>
			<td>Thorlabs</td>
			<td>AC080-020-C</td>
			<td></td>
		</tr>
		<tr>
			<td>Galvanometer</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Relay Lens</td>
			<td>Thorlabs</td>
			<td>AC254-100-C</td>
			<td>two Relay lens to make a telescope setup</td>
		</tr>
		<tr>
			<td>Triangle Mirror Mount</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mirror</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Objective</td>
			<td>Mitutoyo</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pedestrial Post</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Clamping Fork</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Polarization Controller</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>30mm Cage Mount</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cage Rod</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Stage</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>3D motorized translation stage</td>
			<td>Beijing Mao Feng Optoelectronics Technology Co., Ltd.</td>
			<td>JTH360XY</td>
			<td></td>
		</tr>
		<tr>
			<td>2D Tilting Stage</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Rotation Stage</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Plate Holder</td>
			<td></td>
			<td></td>
			<td>3D printed</td>
		</tr>
		<tr>
			<td>Spectrometer</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Lens Tube</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Adapter</td>
			<td>Thorlabs</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Collimating Lens</td>
			<td>Thorlabs</td>
			<td>AC080-020-C</td>
			<td></td>
		</tr>
		<tr>
			<td>Grating</td>
			<td>Wasatch</td>
			<td></td>
			<td>G = 1145 lpmm</td>
		</tr>
		<tr>
			<td>F-theta Lens</td>
			<td>Thorlabs</td>
			<td>FTH-1064-100</td>
			<td></td>
		</tr>
		<tr>
			<td>InGaAs Line-scan Camera</td>
			<td>Sensor Unlimited</td>
			<td>SU1024-LDH2</td>
			<td></td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Cell Culture Component</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>HCT 116 Cell line</td>
			<td>ATCC</td>
			<td>CCL-247</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Culture Flask</td>
			<td>SPL Life Sciences</td>
			<td>70025</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette</td>
			<td>Fisherbrand</td>
			<td>14388100</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette tips</td>
			<td>Sorenson Bioscience</td>
			<td>10340</td>
			<td></td>
		</tr>
		<tr>
			<td>Gibco GlutaMax DMEM</td>
			<td>Thermo Fisher Scientific</td>
			<td>10569044</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum, certified, US origin</td>
			<td>Thermo Fisher Scientific</td>
			<td>16000044</td>
			<td></td>
		</tr>
		<tr>
			<td>Antibiotic-Antimycotic (100X)</td>
			<td>Thermo Fisher Scientific</td>
			<td>15240062</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning 96-well Clear Round Bottom Ultra-Low Attachment Microplate</td>
			<td>Corning</td>
			<td>7007</td>
			<td></td>
		</tr>
		<tr>
			<td>Gibco PBS, pH 7.4</td>
			<td>Thermo Fisher Scientific</td>
			<td>10010023</td>
			<td></td>
		</tr>
		<tr>
			<td>Gibco Trypsin-EDTA (0.5%)</td>
			<td>Thermo Fisher Scientific</td>
			<td>15400054</td>
			<td></td>
		</tr>
		<tr>
			<td>Forma Series II 3110 Water-Jacketed CO2 Incubators</td>
			<td>Thermo Fisher Scientific</td>
			<td>3120</td>
			<td></td>
		</tr>
		<tr>
			<td>Gloves</td>
			<td>VWR</td>
			<td>89428-750</td>
			<td></td>
		</tr>
		<tr>
			<td>Parafilm</td>
			<td>Sigma-Aldrich</td>
			<td>P7793</td>
			<td></td>
		</tr>
		<tr>
			<td>Transfer pipets</td>
			<td>Globe Scientific</td>
			<td>138080</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>5702 R</td>
			<td>To centrifuge the 15 mL tube</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>NUAIRE</td>
			<td>AWEL CF 48-R</td>
			<td>To centrifuge the 96-well plate</td>
		</tr>
		<tr>
			<td>Microscope</td>
			<td>Olympus</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Histology &#38; IHC</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Digital slide scanner</td>
			<td>Leica</td>
			<td>Aperio AT2</td>
			<td>Obtain high-resolution histological images</td>
		</tr>
		<tr>
			<td>Histology Service</td>
			<td>Histowiz</td>
			<td></td>
			<td>Request service for histological and immunohistological staining of tumor spheroid</td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>List of Commerical OCTs</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SD-OCT system</td>
			<td>Thorlabs</td>
			<td>Telesto Series</td>
			<td></td>
		</tr>
		<tr>
			<td>SD-OCT system</td>
			<td>Wasatch Photonics</td>
			<td>WP OCT 1300 nm</td>
			<td></td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Software for Data Analyses</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Basic Image Analysis</td>
			<td>NIH</td>
			<td>ImageJ</td>
			<td>Fiji also works.</td>
		</tr>
		<tr>
			<td>3D Rendering</td>
			<td>Thermo Fisher Scientific</td>
			<td>Amira</td>
			<td>Commercial software. Option 1</td>
		</tr>
		<tr>
			<td>3D Rendering</td>
			<td>Bitplane</td>
			<td>Imaris</td>
			<td>Commercial software. Option 2. Used in the protocol</td>
		</tr>
		<tr>
			<td>OCT acquisition software</td>
			<td></td>
			<td></td>
			<td>custom developed in C++.</td>
		</tr>
		<tr>
			<td>Stage Control</td>
			<td>Beijing Mao Feng Optoelectronics Technology Co., Ltd.</td>
			<td>MRC_3</td>
			<td>Incorporated into the custom OCT acquisition code</td>
		</tr>
		<tr>
			<td>OCT processing software</td>
			<td></td>
			<td></td>
			<td>custom developed in C++. Utilize GPU. Incorporated into the custom OCT acquisition code.</td>
		</tr>
		<tr>
			<td>Morphological and Physiological Analysis</td>
			<td></td>
			<td></td>
			<td>custom developed in MATLAB</td>
		</tr>
	</tbody>
</table>

longitudinal morphological and physiological monitoring of three-dimensional tumor spheroids using optical coherence tomography

Tumor spheroids have been developed as a three-dimensional (3D) cell culture model in cancer research and anti-cancer drug discovery. However, currently, high-throughput imaging modalities utilizing bright field or fluorescence detection, are unable to resolve the overall 3D structure of the tumor spheroid due to limited light penetration, diffusion of fluorescent dyes and depth-resolvability. Recently, our lab demonstrated the use of optical coherence tomography (OCT), a label-free and non-destructive 3D imaging modality, to perform longitudinal characterization of multicellular tumor spheroids in a 96-well plate. OCT was capable of obtaining 3D morphological and physiological information of tumor spheroids growing up to about 600 &#181;m in height. In this article, we demonstrate a high-throughput OCT (HT-OCT) imaging system that scans the whole multi-well plate and obtains 3D OCT data of tumor spheroids automatically. We describe the details of the HT-OCT system and construction guidelines in the protocol. From the 3D OCT data, one can visualize the overall structure of the spheroid with 3D rendered and orthogonal slices, characterize the longitudinal growth curve of the tumor spheroid based on the morphological information of size and volume, and monitor the growth of the dead-cell regions in the tumor spheroid based on optical intrinsic attenuation contrast. We show that HT-OCT can be used as a high-throughput imaging modality for drug screening as well as characterizing biofabricated samples.

High Throughput Optical Coherence Tomography Imaging of Spheroids in a 96-well Plate
Figure 3 exhibits the result of HT-OCT scanning of a 96-well plate with HCT 116 tumor spheroids on Day 3. The sequential scan of the whole plate starts from the bottom-right well (H12). Figure 3B shows the flow chart of the software implementation of the HT-OCT system. After one spheroid d...

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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Optical coherence tomography (OCT), a three-dimensional imaging technology, was used to monitor and characterize the growth kinetics of multicellular tumor spheroids. Precise volumetric quantification of tumor spheroids using a voxel counting approach, and label-free dead tissue detection in the spheroids based on intrinsic optical attenuation contrast, were demonstrated.

Tumor spheroids have been developed as a three-dimensional (3D) cell culture model in cancer research and anti-cancer drug discovery. However, currently, ...

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Cancer Research

7.5K Views.  Lehigh University. Driedimensionale tumorsferoïden kunnen weefselspecifieke eigenschappen van tumoren in vivo nabootsen en meer klinisch relevante gegevens verstrekken voor de ontdekking van anti-kankermedicijn dan eenvoudige 2D-celkweek. Onze beeldvormingstechniek, optische coherentietomografie, kan gemakkelijk de 3D-structuur van een enkele tumoreuze zenuwen visualiseren, met een grootte van enkele honderden micron, en zorgen voor een nauwkeurigere karakterisering van de morfologie en baanbesmetting, vaak bi...

Video: Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is exposed to in vivo. Solid tumors grow not as a sheet attached to plastic, but instead as a collection of clonal cells in a three-dimensional (3D) space interacting with their neighbors, and with distinct spatial properties such as the disruption of normal cellular polarity. These interactions cause 3D-cultured cells to acquire morphological and cellular characteristics which are more relevant to in vivo tumors. Additionally, a tumor mass is in direct contact with other cell types such as stromal and immune cells, as well as the extracellular matrix from all other cell types. The matrix deposited is comprised of macromolecules such as collagen and fibronectin.
In an attempt to increase the translation of research findings in oncology from bench to bedside, many groups have started to investigate the use of 3D model systems in their drug development strategies. These systems are thought to be more physiologically relevant because they attempt to recapitulate the complex and heterogeneous environment of a tumor. These systems, however, can be quite complex, and, although amenable to growth in 96-well formats, and some now even in 384, they offer few choices for large-scale growth and screening. This observed gap has led to the development of the methods described here in detail to culture tumor spheroids in a high-throughput capacity in 1536-well plates. These methods represent a compromise to the highly complex matrix-based systems, which are difficult to screen, and conventional 2D assays. A variety of cancer cell lines harboring different genetic mutations are successfully screened, examining compound efficacy by using a curated library of compounds targeting the Mitogen-Activated Protein Kinase or MAPK pathway. The spheroid culture responses are then compared to the response of cells grown in 2D, and differential activities are reported. These methods provide a unique protocol for testing compound activity in a high-throughput 3D setting.

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma

Osteosarcoma (OS) is the most common and a highly aggressive primary bone tumor. It is characterized with anatomic and histologic variations along with diagnostic or prognostic difficulties. OS comprises genotypically and phenotypically heterogeneous cancer cells. Bone microenvironment elements are proved to account for tumor heterogeneity and disease progression. Bone extracellular matrix (BEM) retains the microstructural matrices and biochemical components of native extracellular matrix. This tissue-specific niche provides a favorable and long-term scaffold for OS cell seeding and proliferation. This article provides a protocol for the preparation of BEM model and its further experimental application. OS cells can grow and differentiate into multiple phenotypes consistent with the histopathological complexity of OS clinical specimens. The model also allows visualization of diverse morphologies and their association with genetic alterations and underlying regulatory mechanisms. As homologous to human OS, this BEM-OS model can be developed and applied to the pathology and clinical research of OS.

The bone extracellular matrix (BEM) model for osteosarcoma (OS) is well established and shown here. It can be used as a suitable scaffold for mimicking primary tumor growth in vitro and providing an ideal model for studying the histologic and cytogenic heterogeneity of OS.

Osteosarcoma (OS) is the most common and a highly aggressive primary bone tumor. It is characterized with anatomic and histologic variations along with ...

Fully Human Tumor-based Matrix in Three-dimensional Spheroid Invasion Assay

Two-dimensional cell culture-based assays are commonly used in in vitro cancer research. However, they lack several basic elements that form the tumor microenvironment. To obtain more reliable in vitro results, several three-dimensional (3D) cell culture assays have been introduced. These assays allow cancer cells to interact with the extracellular matrix. This interaction affects cell behavior, such as proliferation and invasion, as well as cell morphology. Additionally, this interaction could induce or suppress the expression of several pro- and anti-tumorigenic molecules. Spheroid invasion assay was developed to provide a suitable 3D in vitro method to study cancer cell invasion. Currently, animal-derived matrices, such as mouse sarcoma-derived matrix (MSDM) and rat tail type I collagen, are mainly used in the spheroid invasion assays. Taking into consideration the differences between the human tumor microenvironment and animal-derived matrices, a human myoma-derived matrix (HMDM) was developed from benign uterus leiomyoma tissue. It has been shown that HMDM induces migration and invasion of carcinoma cells better than MSDM. This protocol provided a simple, reproducible, and reliable 3D human tumor-based spheroid invasion assay using the HMDM/fibrin matrix. It also includes detailed instructions on imaging and analysis. The spheroids grow in a U-shaped ultra-low attachment plate within the HMDM/fibrin matrix and invade through it. The invasion is daily imaged, measured, and analyzed using ilastik and Fiji ImageJ software. The assay platform was demonstrated using human laryngeal primary and metastatic squamous cell carcinoma cell lines. However, the protocol is suitable also for other solid cancer cell lines.

Tumor microenvironment is an essential part of cancer growth and invasion. To mimic carcinoma progression, a biologically relevant human matrix is needed. This protocol introduces an improvement for the in vitro three-dimensional spheroid invasion assay by applying a human leiomyoma-based matrix. The protocol also introduces a computer-based cell invasion analysis.

Two-dimensional cell culture-based assays are commonly used in in vitro cancer research. However, they lack several basic elements that form the tumor ...

Establishment and Characterization of Small Bowel Neuroendocrine Tumor Spheroids

Small bowel neuroendocrine tumors (SBNETs) are rare cancers originating from enterochromaffin cells of the gut. Research in this field has been limited because very few patient derived SBNET cell lines have been generated. Well-differentiated SBNET cells are slow growing and are hard to propagate. The few cell lines that have been established are not readily available, and after time in culture may not continue to express characteristics of NET cells. Generating new cell lines could take many years since SBNET cells have a long doubling time and many enrichment steps are needed in order to eliminate the rapidly dividing cancer-associated fibroblasts. To overcome these limitations, we have developed a protocol to culture SBNET cells from surgically removed tumors as spheroids in extracellular matrix (ECM). The ECM forms a 3-dimensional matrix that encapsulates SBNET cells and mimics the tumor micro-environment for allowing SBNET cells to grow. Here, we characterized the growth rate of SBNET spheroids and described methods to identify SBNET markers using immunofluorescence microscopy and immunohistochemistry to confirm that the spheroids are neuroendocrine tumor cells. In addition, we used SBNET spheroids for testing the cytotoxicity of rapamycin.

Neuroendocrine tumors (NETs) originate from neuroendocrine cells of the neural crest. They are slow growing and challenging to culture. We present an alternative strategy to grow NETs from the small bowel by culturing them as spheroids. These spheroids have small bowel NET markers and can be used for drug testing.

Small bowel neuroendocrine tumors (SBNETs) are rare cancers originating from enterochromaffin cells of the gut. Research in this field has been limited ...

A Three-dimensional Model of Spheroids to Study Colon Cancer Stem Cells

Colorectal cancers are characterized by heterogeneity and a hierarchical organization comprising a population of cancer stem cells (CSCs) responsible for tumor development, maintenance, and resistance to drugs. A better understanding of CSC properties for their specific targeting is, therefore, a pre-requisite for effective therapy. However, there is a paucity of suitable preclinical models for in-depth investigations. Although in vitro two-dimensional (2D) cancer cell lines provide valuable insights into tumor biology, they do not replicate the phenotypic and genetic tumor heterogeneity. In contrast, three-dimensional (3D) models address and reproduce near-physiological cancer complexity and cell heterogeneity. The aim of this work was to design a robust and reproducible 3D culture system to study CSC biology. The present methodology describes the development and optimization of conditions to generate 3D spheroids, which are homogenous in size, from Caco2 colon adenocarcinoma cells, a model that can be used for long-term culture. Importantly, within the spheroids, the cells which were organized around lumen-like structures, were characterized by differential cell proliferation patterns and by the presence of CSCs expressing a panel of markers. These results provide the first proof-of-concept for the appropriateness of this 3D approach to study cell heterogeneity and CSC biology, including the response to chemotherapy.

This protocol presents a novel, robust, and reproducible culture system to generate and grow three-dimensional spheroids from Caco2 colon adenocarcinoma cells. The results provide the first proof-of-concept for the appropriateness of this approach to study cancer stem cell biology, including the response to chemotherapy.

Colorectal cancers are characterized by heterogeneity and a hierarchical organization comprising a population of cancer stem cells (CSCs) responsible for ...

Three-Dimensional In Vitro Biomimetic Model of Neuroblastoma Using Collagen-Based Scaffolds

Neuroblastoma is the most common extracranial solid tumor in children, accounting for 15% of overall pediatric cancer deaths. The native tumor tissue is a complex three-dimensional (3D) microenvironment involving layers of cancerous and non-cancerous cells surrounded by an extracellular matrix (ECM). The ECM provides physical and biological support and contributes to disease progression, patient prognosis, and therapeutic response.
This paper describes a protocol for assembling a 3D scaffold-based system to mimic the neuroblastoma microenvironment using neuroblastoma cell lines and collagen-based scaffolds. The scaffolds are supplemented with either nanohydroxyapatite (nHA) or glycosaminoglycans (GAGs), naturally found at high concentrations in the bone and bone marrow, the most common metastatic sites of neuroblastoma. The 3D porous structure of these scaffolds allows neuroblastoma cell attachment, proliferation and migration, and the formation of cell clusters. In this 3D matrix, the cell response to therapeutics is more reflective of the in vivo situation.
The scaffold-based culture system can maintain higher cell densities than conventional two-dimensional (2D) cell culture. Therefore, optimization protocols for initial seeding cell numbers are dependent on the desired experimental timeframes. The model is monitored by assessing cell growth via DNA quantification, cell viability via metabolic assays, and cell distribution within the scaffolds via histological staining.
This model&#39;s applications include the assessment of gene and protein expression profiles as well as cytotoxicity testing using conventional drugs and miRNAs. The 3D culture system allows for the precise manipulation of cell and ECM components, creating an environment more physiologically similar to native tumor tissue. Therefore, this 3D in vitro model will advance the understanding of the disease pathogenesis and improve the correlation between results obtained in vitro, in vivo in&#160;animal models, and human subjects.

Three-Dimensional In Vitro Biomimetic Model of Neuroblastoma Using Collagen-Based Scaffolds

This paper lists the steps required to seed neuroblastoma cell lines on previously described three-dimensional collagen-based scaffolds, maintain cell growth for a predetermined timeframe, and retrieve scaffolds for several cell growth and cell behavior analyses and downstream applications, adaptable to satisfy a range of experimental aims.

Neuroblastoma is the most common extracranial solid tumor in children, accounting for 15% of overall pediatric cancer deaths. The native tumor tissue is a ...

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver cancer, rationalize its rank as the second most common cause of cancer-related death. Preclinical models need to be adapted to recapitulate the human conditions to select the best therapeutic candidates for clinical development and aid the delivery of personalized medicine. Three-dimensional (3D) cellular spheroid models show promise as an emerging in vitro alternative to two-dimensional (2D) monolayer cultures. Here, we describe a 3D tumor spheroid&#160;model which exploits the ability of individual cells to aggregate when maintained in hanging droplets, and is more representative of an in vivo environment than standard monolayers. Furthermore, 3D spheroids can be produced by combining homotypic or heterotypic cells, more reflective of the cellular heterogeneity in vivo, potentially enabling the study of environmental interactions that can influence progression and treatment responses. The current research optimized the cell density to form 3D homotypic and heterotypic tumor spheroids by immobilizing cell suspensions on the lids of standard 10 cm3 Petri dishes. Longitudinal analysis was performed to generate growth curves for homotypic versus heterotypic tumor/fibroblasts spheroids. Finally, the proliferative impact of fibroblasts (COS7 cells) and liver myofibroblasts (LX2) on homotypic tumor (Hep3B) spheroids was investigated. A seeding density of 3,000 cells (in 20 &#181;L media) successfully yielded Huh7/COS7 heterotypic spheroids, which displayed a steady increase in size up to culture day 8, followed by growth retardation. This finding was corroborated using Hep3B homotypic spheroids cultured in LX2 (human hepatic stellate cell line) conditioned medium (CM). LX2 CM triggered the proliferation of Hep3B spheroids compared to control tumor spheroids. In conclusion, this protocol has shown that 3D tumor spheroids can be used as a simple, economical, and prescreen in vitro tool to study tumor-stromal interactions more comprehensively.

Comprehensive in vitro models that faithfully recapitulate the relevant human disease are lacking. The current study presents three-dimensional (3D) tumor spheroid creation and culture, a reliable in vitro tool to study the tumor-stromal interaction in human hepatocellular carcinoma.

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver ...

Maintaining Human Glioblastoma Cellular Diversity Ex vivo using Three-Dimensional Organoid Culture

Glioblastoma (GBM) is the most commonly occurring primary malignant brain cancer with an extremely poor prognosis. Intra-tumoral cellular and molecular diversity, as well as complex interactions between tumor microenvironments, can make finding effective treatments a challenge. Traditional adherent or sphere culture methods can mask such complexities, whereas three-dimensional organoid culture can recapitulate regional microenvironmental gradients. Organoids are a method of three-dimensional GBM culture that better mimics patient tumor architecture, contains phenotypically diverse cell populations, and can be used for medium-throughput experiments. Although three-dimensional organoid culture is more laborious and time-consuming compared to traditional culture, it offers unique benefits and can serve to bridge the gap between current in vitro and in vivo systems. Organoids have established themselves as invaluable tools in the arsenal of cancer biologists to better understand tumor behavior and mechanisms of resistance, and their applications only continue to grow. Here, details are provided about methods for generating and maintaining GBM organoids. Instructions of how to perform organoid sample embedding and sectioning using both frozen and paraffin-embedding techniques, as well as recommendations for immunohistochemistry and immunofluorescence protocols on organoid sections, and measurement of total organoid cell viability, are all also described.

Maintaining Human Glioblastoma Cellular Diversity Ex vivo using Three-Dimensional Organoid Culture

Here, we describe a method of generating glioblastoma (GBM) organoids from primary patient specimens or patient-derived cell cultures and maintaining them to maturity. These GBM organoids contain phenotypically diverse cell populations and recreate tumor microenvironments ex vivo.

Glioblastoma (GBM) is the most commonly occurring primary malignant brain cancer with an extremely poor prognosis. Intra-tumoral cellular and molecular ...

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Skin cancer is one of the most common cancers worldwide. Diagnosis relies on visual inspection and dermoscopy followed by biopsy for histopathological confirmation. While the sensitivity of dermoscopy is high, the lower specificity results in 70%-80% of the biopsies being diagnosed as benign lesions on histopathology (false positives on dermoscopy).
Reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) imaging can noninvasively guide the diagnosis of skin cancers. RCM visualizes cellular morphology in en-face layers. It has doubled the diagnostic specificity for melanoma and pigmented keratinocytic skin cancers over dermoscopy, halving the number of biopsies of benign lesions. RCM acquired billing codes in the USA and is now being integrated into clinics.
However, limitations such as the shallow depth (~200 &#181;m) of imaging, poor contrast for nonpigmented skin lesions, and imaging in en-face layers result in relatively lower specificity for the detection of nonpigmented basal cell carcinoma (BCCs) &#8212; superficial BCCs contiguous with the basal cell layer and deeper infiltrative BCCs. In contrast, OCT lacks cellular resolution but images tissue in vertical planes down to a depth of ~1 mm, which allows the detection of both superficial and deeper subtypes of BCCs. Thus, both techniques are essentially complementary.
A &#34;multi-modal,&#34; combined RCM-OCT device simultaneously images skin lesions in both en-face and vertical modes. It is useful for the diagnosis and management of BCCs (nonsurgical treatment for superficial BCCs vs. surgical treatment for deeper lesions). A marked improvement in specificity is obtained for detecting small, nonpigmented BCCs over RCM alone. RCM and RCM-OCT devices are bringing a major paradigm shift in the diagnosis and management of skin cancers; however, their use is currently limited to academic tertiary care centers and some private clinics. This paper familiarizes clinicians with these devices and their applications, addressing translational barriers into routine clinical workflow.

Here, we describe protocols for acquiring good-quality images using novel, noninvasive imaging devices of reflectance confocal microscopy (RCM) and combined RCM and optical coherence tomography (OCT). We also familiarize clinicians with their clinical applications so that they can integrate the techniques into regular clinical workflows to improve patient care.

Skin cancer is one of the most common cancers worldwide. Diagnosis relies on visual inspection and dermoscopy followed by biopsy for histopathological ...

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

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