Optical Coherence Tomography Detects Necrotic Regions and Volumetrically Quantifies Multicellular Tumor Spheroids

More than 90% of cancer drugs in development fail because of lack of efficacy or unexpected toxicity in clinical trials (1–3). This failure rate in the late stages of clinical trials is in large part due to the use of overly simplistic in vitro cell assays and in vivo animal models with limited predictive value during various stages of drug discovery. Recently, three-dimensional (3D) tumor spheroid models have been explored to provide novel physiologic and pharmacologic data that are more predictive of drug efficacy and toxicity in the clinic (2–24). These studies show that 3D tumor spheroid models are expected to have an immediate and long-lasting impact in shortening drug discovery timelines, reducing costs of investment, and bringing new medicines to more patients. To characterize 3D tumor models in high-throughput screening, bright-field imaging, or phase-contrast imaging are routinely employed (7, 9, 22, 24). However, these modalities can only provide 2D projections of tumor spheroids and are unable to resolve their 3D structures. Likewise, fluorescence imaging, both confocal and nonconfocal, is often used to characterize 3D tumor spheroids (8, 9, 16, 18, 22); however, its low penetration of fluorescent reagents and limited light penetration in tumor spheroids over 50 μm in diameter significantly limits our understanding of the morphology and physiology inside the tumor spheroids. Light sheet fluorescence microscopy has been proposed as an alternative to create a high-resolution, 3D profile of an entire tumor spheroid (11, 25). However, this method requires extensive steps to prepare the samples, involving transfer, staining and mounting, and/or clearing of tumor spheroids, which dampens its strength for high-throughput screening and longitudinal imaging (11).

Optical coherence tomography (OCT; ref. 26), a rapidly developing optical imaging modality, is capable of obtaining label-free, nondestructive 3D images at millimeter depths in biological tissues (27–30). Unlike fluorescence imaging technologies, OCT uses low-coherence interferometry to detect back-scattered signals and provides reconstructed depth-resolved images at micron-level spatial resolutions. In the field of oncology, both standard and functional OCT have been used to analyze pathologic features of tumors and evaluate their margins ex vivo in tissues (31–35). OCT also facilitates in vivo studies of murine models harboring implanted tumors and detects their margins, microenvironment, and vascular dynamics (36, 37). Pilot studies have also used OCT to observe in vitro 3D tumor spheroids and evaluate their responses to photodynamic therapy based on morphologic information obtained from OCT (38, 39).

Quantitative assessment of tumor volume, the key factor predicting cancer progression and recurrence (40), has been challenging in both in vitro and in vivo settings. 2D bright field, phase contrast, and fluorescent imaging cannot resolve the entire depth structure of larger spheroids (>50 μm), so volumes are estimated using cross-sectional radii/diameters, a primitive method unsuitable for obtaining precise volume for irregularly shaped spheroids (7, 9, 41). To achieve precise volume calculation, a spheroid must be clearly resolved in 3D space so that its structure can be empirically measured. One can use the high-resolution, high-depth imaging capabilities of OCT to satisfy this need. However, very few studies have reported the utilization of OCT for quantitative assessment of tumor spheroids. Although Sharma and colleagues used OCT to measure heterogeneous geometries of 3D tumor spheroids (38), an oblate spheroid shape was still assumed to calculate the volume of 3D tumor models.

Necrotic core development in 3D tumor spheroids represents a unique feature mimicking tumor necrosis in vivo that is unlikely reproduced in 2D cell cultures. When a tumor spheroid grows larger than 500 μm in diameter, a three-layer concentric structure, including an outer layer of proliferating cells, a middle layer of quiescent cells, and a necrotic core, is typically observed in the spheroid (5, 6, 10, 15, 19). The complexity of this multilayer structure may be caused by lack of oxygen and nutrients, a feature not seen in 2D tumor cell cultures (6, 19, 25, 42). Live/dead fluorescence assay is commonly used to assess the viability of cells and distribution of the necrotic core in 3D tumor spheroids. However, its staining with fluorescent dyes prohibits the specimens from continuing development and from being used for longitudinal studies of spheroid growth kinetics.

The purpose of this study was to use OCT to accurately quantify the 3D structures of tumor spheroids and detect necrotic tissue using label-free intrinsic light attenuation contrast. We have recently developed high resolution OCT imaging platforms for various in vitro bioapplications, including examining angiogenic sprouting in microengineered devices (43) and evaluating progressive neuronal changes in in vitro rat hippocampal slice cultures over time (44). In this study, we employed a label-free OCT-based imaging platform to visualize 3D structures of multicellular tumor spheroids (MCTS) in 96-well plates and obtained high resolution structural and physiologic information from the spheroids over a period of 21 days. In particular, we quantified sizes, volumes and growth curves for each individual spheroid, and showed that OCT can provide accurate and robust quantification of these parameters, especially for irregular spheroids. Moreover, we demonstrated the ability of OCT to identify necrotic cores within tumor spheroids based solely on optical intrinsic attenuation contrast, offering the opportunity to track the change of necrotic regions over time. Our results demonstrated the value of this OCT-based imaging platform in MCTSs and laid the foundation for future high-throughput screening of tumor spheroids for drug testing.

Two tumor cell lines, glioblastoma (U-87 MG) and colorectal carcinoma (HCT116), were purchased from the ATCC in 2016 and 2014, respectively. Both cell lines were cultured following recommended protocols by suppliers. U-87 MG cells were maintained in EMEM (Lonza) supplemented with 10% FBS (Thermo Fisher Scientific), 1% penicillin–streptomycin (Pen-Strep; Lonza), and additionally, 1× nonessential amino acid (NEAA; Thermo Fisher Scientific) and 1% sodium pyruvate (Thermo Fisher Scientific). HCT116 cells were maintained in McCoy 5A medium (Thermo Fisher Scientific) supplemented with 10% FBS, 1% Pen-Strep, 1× NEAA, and 1% sodium pyruvate. Both cell lines were cultured under standard conditions (37°C, 5% CO₂, 95% humidity). Media were changed twice weekly and tumor cells were subcultured when they reached 90% confluence. A Mycoplasma test was performed on live HCT116 cell culture in May 2017, using a Mycoplasma Detection Kit (MycoFluor; Thermo Fisher Scientific). No mycoplasma contamination was observed. No mycoplasma test was done on U87 MG cell culture after the purchase.

MCTSs were prepared following protocols adapted from Vinci and colleagues (9). For growth media, 10% FBS was replaced by 1× B27, 40 ng/mL EGF recombinant human protein, and 40 ng/mL FGF-basic recombinant human protein (Thermo Fisher Scientific). U-87MG (P3–P7 after collection or thawing) and HCT116 (P12–P20 after thawing) cells were seeded in ultra-low-attachment 96-well U-bottom plates (Corning) with an initial concentration of 0.3 × 10⁴ cells/mL for both cell lines and 200 μL/well cell suspensions. After seeding, the whole plate was centrifuged at 1,200 rpm for 5 to 7 minutes to facilitate cell gathering at the bottom of each well. Tumor cells were incubated under standard condition and 50% of media were replaced twice weekly.

A customized spectral domain OCT (SD-OCT) system was developed (Fig. 1A) to acquire 3D images of tumor spheroids. A superluminescent diode (SLD; Thorlabs, SLD1325) was used to provide broadband light with a central wavelength of 1,320 nm and spectral range of 110 nm. Output of the SLD was split 50/50 with a fiber coupler and transmitted to the sample and reference arms of the SD-OCT system. A 2D galvanometer (Thorlabs, GVS002) was used to scan the optical beam in transverse directions on the sample. A 5× objective lens with an effective focal length of 40 mm was used in the sample arm. The beam diameter at the objective was measured to be approximately 5 mm, yielding an effective numerical aperture (NA) of approximately 0.062 in air. This leads to a calculated depth of field (DOF) of approximately 340 μm in air. The DOF of the OCT system was measured to be approximately 400 μm in air. During experiments, efforts were made to consistently maintain the focal position at approximately 100–200 μm below the top surface of each tissue sample. As a result, the effect of nonuniform depth-wise focal profile would be relatively minimum in our experiments. The transverse resolution was measured to be approximately 7.8 μm using a USAF target. Interference signals between the reference arm and the sample arm were detected by a spectrometer, which consists of a diffraction grating, a F-theta lens (Thorlabs, FTH100-1064) and a 1,024-pixel InGaAs linescan camera (Sensors Unlimited, SU1024LDH2) operating at 20-kHz A-scan rate. The maximum depth detection range of the SD-OCT system was approximately 3.4 mm in air or approximately 2.5 mm in tissue. The axial resolution of the system was measured to be approximately 4.9 μm in tissue (refractive index of tumor spheroids was assumed to be 1.37; refs. 45, 46). Sensitivity of the OCT system was measured as approximately 104 dB. In addition, a 2D motorized stage (Aerotech) was used to provide horizontal translation of the 96-well plate to image tumor spheroids in different wells (Fig. 1B and C).

OCT images of tumor spheroids were acquired at 4, 7, 11, 14, 18, and 21 days after spheroid initiation for both cell lines. Numbers of tumor spheroids from U-87MG and HCT116 cell lines imaged at each time point are shown in Table 1. OCT scanning range was adjusted to cover the whole tumor spheroid as it developed. Actual scanning range varied from 0.46 × 0.42 mm² to 2.0 × 1.7 mm². Each 3D dataset of a tumor spheroid was acquired in 10 to 15 seconds. Obtained OCT datasets of tumor spheroids were first processed to generate OCT structural images (in 8-bit) with corrected scales. OCT intensity was normalized by dividing the OCT image value by 255, the maximum value of 8-bit data prior to next steps of image analysis.

After OCT imaging of all tumor spheroids at each time point, one to two tumor spheroids were randomly selected for viability tests to evaluate the distribution of live and dead cells. These spheroids were treated with calcein AM (Thermo Fisher Scientific) and propidium iodide (Biotium) for live and dead staining, respectively. The whole plate was incubated for 30 minutes prior to fluorescence imaging. Fluorescent images were obtained using an Olympus IX51 inverted microscope with a 10× objective for small tumor spheroids and a 4× objective for large tumor spheroids. A manual image registration was performed to correlate fluorescent images with 3D OCT images based on landmark features of tumor spheroids, such as surface irregularity or cell sprouting. Because of the destructive effect of fluorescent dyes, these one to two selected tumor spheroids were no longer used in later stages.

At selected time points, one to three tumor spheroids were processed for histology and IHC to compare with corresponding OCT results. Each tumor spheroid was collected in a single 1.5-mL microcentrifuge tube filled with 10% formaldehyde and fixed for 48 hours. Histology and IHC processes of each tumor spheroid were performed by HistoWiz Inc. using standard paraffin embedding techniques. Sections of tumor spheroids (5 μm in thickness) were stained for hematoxylin and eosin (H&E) and TUNEL apoptosis detection (Promega), respectively. A counter staining of hematoxylin was applied to TUNEL. Images were obtained using a Leica AT2 scanner.

To calculate the tumor volume based on measured diameter of the tumor spheroid, three cross-sectional images in the XY, XZ, and YZ imaging planes across the centroid of the spheroid were selected. Diameter of the spheroid was measured in XY plane and its height was measured in XZ plane. Diameter-based tumor volume was calculated as:

where d was the average diameter of the spheroid measured in the X and Y directions.

To quantify actual tumor volume, we used a voxel-based method: first, a 3D average filter was performed to remove the speckles within tumor spheroids in OCT images. Then, the tumor spheroid was segmented using a canny edge detection filter frame by frame. Connective voxels were grouped and the mean distance of grouped connective voxels to the manually chosen spheroid centroid was calculated. The group with the minimum mean distance was identified as the spheroid region. The voxels within this region were summed and then multiplied by the actual volume of an individual voxel (volume/voxel), yielding the total volume of the spheroid.

Necrotic region in 3D tumor spheroids can be identified with intrinsic optical attenuation measurement. Because OCT signals originate from back-scattering at different depths, OCT intensity detected as a function of depth in a homogeneous medium can be described by the Beer–Lambert Law (47):

where z represents the depth and μ is the optical attenuation coefficient, and I₀ is the incident intensity to the sample. OCT images are typically presented in logarithmic scale to increase image contrast. Therefore, the optical attenuation coefficient can be obtained by finding the derivative of the logarithmic scale OCT intensity:

To calculate the optical attenuation from the logarithmic scale OCT images, we first plotted the OCT intensity profile along each axial scan from cross-sectional OCT images. Next we obtained local optical attenuation coefficients by linear fitting the logarithmic scale OCT intensity profile over a certain depth range, extracting its slope, and multiply the slope by −1/2. The aforementioned method was further applied to each axial scan across the entire spheroid. Note that we applied a 3D average filter over OCT data to reduce speckle noise in OCT images before optical attenuation calculation. The attenuation coefficient at each voxel within the segmented spheroid region was calculated based on the slope of OCT intensity profile in a 10-voxel depth window (∼40 μm in depth), with the voxel located in the middle of the window.

Student paired t tests were performed to compare diameter and height measurements, as well as diameter-based and voxel-based volumes at each time point. A P value of <0.05 was used to indicate statistically significant differences between the paired measurements (diameter vs. height, diameter-based volume vs. voxel-based volume).

OCT was used to perform volumetric imaging of tumor spheroids for both U-87MG and HCT116 cell lines at six different time points over 21 days (Fig. 2A). Figure 2B–D and Supplementary Materials (Video S1) show the development of a U-87MG tumor spheroid over 21 days. En face (Fig. 2B), cross-sectional (Fig. 2C), and 3D rendered images (Fig. 2D) were shown to illustrate the growth dynamics of the same sample. We observed that U-87 MG cells gathered to form a spheroid with a diameter of approximately 250 μm on day 4. The spheroid continued to grow and reached approximately 550 μm in diameter on day 14, after which growth slowed down and no significant changes in diameter were observed. The U-87MG tumor spheroid remained tightly packed spherical shape throughout the 21-day development.

The growth dynamics of another cell line (HCT116) over the same duration were shown in Fig. 2E–G and Supplementary Material (Video S2). We found that HCT 116 cells formed a spheroid with a size of approximately 350 μm on day 4, and reached approximately 500 μm on day 7. As shown in en face images (Fig. 2E), the HCT116 cell cluster maintained a smooth and near-spherical shape and continued to grow, albeit rather slowly, before day 18. However, from cross-section images (Fig. 2F), the HCT116 cluster was observed as a spheroid only on days 4 and 7. On day 11, an irregular “two-peak” shape of the HCT 116 cluster was seen. In addition, tumor height was observed to reach its maximum on day 11 and reduced after that day. In 3D rendered images (Fig. 2G), we clearly observed that the shape of the HCT 116 tumor spheroid was disrupted and flattened after day 11. Similar results were observed in all HCT 116 tumor spheroids investigated in this study. Therefore, the morphology of HCT 116 underwent distinctive evolution in their geometric shapes at late growth stage when compared with U-87 MG tumor spheroids.

The growth kinetics of U-87 MG and HCT 116 tumor spheroids were quantitatively analyzed and compared in terms of diameter, height, and volume (Fig. 3). Figure 3A to F exhibits two tumor spheroids (day 18) used in our determination of average diameter and height based on en face XY and cross-sectional XZ OCT images. The average diameter was calculated from cross-sectional measurements in the X and Y directions in the en face OCT image (Fig. 3B and E). Similarly, the average height was measured from XZ cross-sectional images (Fig. 3C and F).

As shown in Fig. 3G, U-87MG tumor spheroids showed a linear increase in the average diameter from day 4 (diameter 220 ± 20 μm) to day 14 (diameter 530 ± 30 μm), and then exhibited a slower growth rate until day 21 (diameter 580 ± 20 μm). This growth trend for spheroid diameter was consistent with previous studies (9, 22). Similarly, the height of U-87MG tumor spheroids linearly increased from day 4 (height 230 ± 10 μm) to day 14 (height 550 ± 40 μm), and then continued to increase at a slower rate until day 21 (height 640 ± 20 μm). The average height of U-87MG tumor spheroids was slightly but significantly larger than the average diameter of the spheroids only after day 14, indicating nonperfect sphericity of the spheroids at a late stage of culture. However, the average diameter of HCT116 tumor spheroids exhibited a linear increase from day 4 (diameter 350 ± 30 μm) to day 11 (diameter 670 ± 90 μm), but then only slightly increased from day 11 to day 21 (Fig. 3H). A different growth curve pattern was observed for the average height of the HCT116 tumor spheroids. The spheroid height reached the maximum value on day 11 (height 560 ± 70 μm) and then rapidly dropped until day 21 (height 270 ± 100 μm). The diameter and height of HCT116 increased at the same pace only on day 4 and day 7. Significant differences between height and diameter were observed at all other measured time points (P < 0.001 on days 11, 14; P < 0.01 on day 18, and P < 0.05 on day 21; Fig. 3H). The discrepancy between average diameter and height of HCT 116 tumor spheroids was consistent with our observation of the heterogeneous geometry development and collapse of the spheroids at a late stage shown in reconstructed OCT 3D images (Fig. 3D).

The volume growth curves of U-87 MG and HCT 116 tumor spheroids were measured and compared using a voxel-based method (Fig. 3I and J). The actual average spheroid volume of U-87 MG tumor spheroids steadily increased by over 20 times from day 4 to day 21 (Fig. 3I). The U-87MG volumes derived from the traditional diameter-based measurements were comparable but slightly smaller than the volume obtained using voxel-based method. In comparison, Fig. 3J shows the voxel-based volume measurements of HCT116 spheroids. The average volume of HCT116 tumor spheroids first increased from day 4 to day 11, but gradually decreased at subsequent time points. In contrast, the growth curve for diameter-based volumes of HCT116 tumor spheroids displayed a steadily increasing trend throughout the entire period, showing a large deviation from the growth curve of the voxel-based measurements. Therefore, the diameter-based volumes of HCT116 tumor spheroids were significantly overestimated compared with the volume measured from the voxel-based method after day 11. The diameter-based volume measurements were as high as approximately 276% compared with the voxel-based measurements on day 21, which could be attributed to the significant reduction of the height of the HCT116 spheroid (Fig. 3H). Our results suggest that voxel-based volume measurements from 3D OCT data yield a more robust and accurate quantification of the 3D tumor spheroids compared to diameter-based volume measurements. OCT provides complimentary depth information of the samples, which is not readily observable with traditional imaging modalities but critical for the actual volume quantification of 3D tumor spheroid models.

We detected necrotic regions in 3D tumor spheroids based on intrinsic optical attenuation contrast. Figure 4A–L showed representative analyses of optical attenuation of tumor spheroids on days 4, 14, and 18, respectively. The backscattered signals within tumor spheroids (Fig. 4A, E, and I) were used to derive intensity profiles along each axial scan line (Fig. 4B, F, and J). We found that the intensity profile of the HCT116 tumor spheroid on day 4 (Fig. 4B), although noisy, showed no significant drops (fitted gray line). On days 14 and 18 (Fig. 4F and J), two distinct slopes in the intensity profile were clearly observed: a slow decay of intensity at the top portion of the spheroid marked with a gray line and a fast decay marked with a red line. Therefore, high attenuation regions (indicated in red lines) could be observed from the intensity profiles of the tumor spheroids on days 14 and 18 (Fig. 4F and J), but not from the tumor spheroid on day 4 (Fig. 4B).

The quantitative determination of necrotic region is based on attenuation coefficients instead of the intensity profiles. To determine the threshold to separate low and high attenuation regions, we first plotted histograms of attenuation coefficients from OCT images of specimens from different time point (Fig. 4C, G, and K). It can be clearly seen that live and necrotic tissues have distinct peak locations (Fig. 4C and G). The histograms were then fit as the combination of two Gaussian curves, where the peak locations (P₁, P₂) were determined from the curve-fitting. The best fitting results were observed at Peak 1 (P₁ = 0.36/mm) and Peak 2 (P₂ = 0.60/mm) for all tumor spheroids tested in this study with R² > 0.95. The threshold of high attenuation region was determined as the mean of the two peak values (i.e., 0.48 mm⁻¹). Binarized maps were generated by highlighting high attenuation regions above the threshold (indicated in red; Fig. 4D, H, and L). Because the OCT imaging technology was completely nondestructive, we further monitored the progression of tumor necrotic regions from the same sample throughout the growing period (Supplementary Video S3). Increase of high-attenuation regions was clearly visible as the spheroid developed. Similar results were also found in the progression of necrotic regions of U-87 MG tumor spheroids.

This nondestructive necrotic region detection technique was confirmed by comparing spheroid images obtained using fluorescence, histology, and IHC with corresponding OCT optical attenuation images (Fig. 5). Figure 5A and B shows the correlation between en face OCT attenuation image of an HCT116 tumor spheroid (Fig. 5A) at Day 14 and the corresponding fluorescent image (Fig. 5B). The necrotic region within the tumor spheroid indicated by the viability test (PI stained, red region in Fig. 5B) was consistent with the distribution of the high-attenuation region derived from the OCT image (red region, Fig. 5A). Figure 5C–L further showed the correlation between OCT attenuation image of two HCT116 tumor spheroids at Days 4 and 14 and the corresponding histology and IHC spheroid slices stained with H&E and TUNEL, respectively. On Day 4, no necrotic region was observed in OCT attenuation image, H&E, and TUNEL-stained slices (Fig. 5C–G). On day 14, OCT attenuation image (Fig. 5H) showed a good match with H&E and TUNEL slices (Fig. 5I–L), as indicated by analyzing the features within the regions in H&E and TUNEL slices marked by dash lines derived from the contour of OCT high attenuation regions. In H&E slices, the necrotic regions were indicated by less dense and aggregated structure located within the dashed line region (Fig. 5I and K). In TUNEL slices, a good match was observed between high attenuation region and TUNEL-labeled apoptotic cellular region (Fig. 5J and L).

Quantitative assessment of 3D tumor spheroids, including the size, shape, and growth curve is of great importance because tumor activity is highly relevant to its morphologic structures. Currently, imaging platforms using bright field, phase contrast, or fluorescent imaging have been established for high-throughput, routine imaging, and analysis of morphology or functions of the MCTS (8, 9, 18, 22). However, they are unable to resolve the entire, large tumor structure due to limited depth penetration. In this study, we demonstrated the capability of OCT to acquire 3D label-free images of the entire MCTS as they develop over time. We observed different growth patterns between U-87 MG and HCT 116 cell lines: U-87 MG spheroids remained tightly packed over time, whereas some HCT116 spheroids became flattened and disrupted after day 11. Nevertheless, from 2D images, both U-87MG and HCT116 tumor spheroids were observed to maintain their spherical shapes up to 18 days with diameters increasing gradually, yielding a false-positive observation of spheroid growth. In addition, quantitative analyses of HCT116 spheroids showed that volume estimation based on 2D cross-sections led to large quantification errors when irregular shapes of tumor spheroids developed, whereas volume quantification based on 3D OCT data yielded an accurate result of the actual volume. Furthermore, 3D OCT images provide a unique view of MCTS, and offer complimentary depth information that is not available in 2D screening.

Viability tests remain a routine procedure used in functional analysis of the MCTS, especially for the purpose of drug screening (18). These tests require that spheroids be stained with fluorescent dyes, indicating live and dead cells, and be imaged with a fluorescent microscope. However, the destructive nature of fluorescent dyes staining indicates that these tests are only suitable for endpoint studies. In this study, we demonstrated an alternative method that can characterize cell viability within the entire MCTS. Our results have indicated that OCT can clearly differentiate the necrotic region from the viable region in the spheroid based on intrinsic optical attenuation contrast. In addition, OCT's 3D imaging capability and nondestructive nature allowed us to quantitatively evaluate the distribution of necrotic tissues and monitor the progression of the necrotic core within the MCTS in situ over a period of 21 days, which is not feasible with traditional fluorescence viability tests.

One interesting observation is that the necrotic tissue within the tumor spheroid is not symmetrically distributed as expected from the concentric model for in vivo large tumor spheroids (4, 48), but shifted towards the bottom of the tumor spheroid. This observation was clearly represented by OCT attenuation image (Fig. 5H) and further confirmed by histology and IHC results (Fig. 5I–L). We suspect, though, that this asymmetric distribution is related to the surrounding environment of the MCTSs. In our experiment, the bottom surface of the MCTS is in close contact with the bottom of cell culture well. The heterogeneous distribution of oxygen and nutrients around the spheroid may partially explain the nonuniform distribution of necrotic tissue within the tumor spheroid. Early experimental evidence suggests that nonuniform distribution of oxygen tension exists within a stationary tumor spheroid (48). Our observations also indicated a thin layer of live tumor cells between the necrotic region and the bottom of the well (Fig. 5I–L). In extra-large tumor spheroids (diameter > 600 μm), we noticed that their bottom regions not highlighted in red in the OCT attenuation image. We suspected that it may be either due to insufficient penetration of light into these regions (Fig. 4E and I, where intensity of these regions was close to the background intensity) or due to strong light reflection on top of tumor spheroid blocking incident lights.

The current OCT setup for this experiment takes 10–15 seconds to acquire one 3D dataset of a single tumor spheroid. Imaging speed can be further improved for high-throughput screening either by using state-of-the-art OCT systems equipped with a high speed tunable laser source (49), or by using parallel OCT imaging with a space-division multiplexing setup (50). For example, a column of 16 wells in a 384-well plate could be scanned simultaneously with a 16-channel parallel OCT system, to achieve a 16× speed improvement for high-throughput screening.

In summary, we have demonstrated that OCT is capable of visualizing and characterizing 3D structure of multicellular tumor spheroids over time. In addition to diameter measurements, OCT provides complementary depth information to better characterize tumor morphology, yielding an accurate and robust quantification of volumes and heights of MCTSs. Moreover, OCT can be reliably utilized to identify necrotic tissues within tumor spheroids based on intrinsic optical contrast, suggesting a promising alternative of viability tests in tumor spheroids. Further development of this OCT-based imaging platform with a parallel imaging scheme will facilitate high-throughput screening of MCTS for cancer drug discovery as well as other 3D tissue models for various biomedical applications.

No potential conflicts of interest were disclosed.

Conception and design: Y. Huang, J. Qiu, C. Zhou

Development of methodology: Y. Huang, Q. Guo, S. Kessel, L.L.-Y. Chan, P. Li, Y. Liu, C. Zhou

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Y. Huang, S. Wang, S. Kessel, I. Rubinoff

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Y. Huang, S. Wang, Q. Guo, I. Rubinoff, C. Zhou

Writing, review, and/or revision of the manuscript: Y. Huang, S. Wang, Q. Guo, S. Kessel, I. Rubinoff, L.L.-Y. Chan, Y. Liu, C. Zhou

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): C. Zhou

Study supervision: Y. Liu, C. Zhou

The authors would like to acknowledge Mr. Wentao Shi for his assistance in cell culture and histologic characterizations.

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

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.