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Zhisong Tong, PhD | Scientist | Molecular Devices

Angeline Lim, PhD | Senior Applications Scientist | Molecular Devices

Introduction

Immunotherapy is increasingly popular as an additional form of cancer treatment in which the immune system is harnessed to specifically target cancer cells. These therapies include CAR T-cells (Chimeric Antigen Receptor engineered T-cells), tumor-infiltrating lymphocytes (TIL), and other genetically modified T-cells. Clinically, the use of immunotherapy has been successful in the treatment of various non-solid (hematological) cancers.1,2 However, this form of treatment has very limited response in patients with solid tumors which accounts for most cancers. One of the reasons for the low success rate is attributed to the immunosuppressive tumor microenvironment (TME).3 Thus, the ability to increase the recruitment and cytotoxic activities of T-cells is essential for successful cancer immunotherapy.

The benefits of using 3 dimensional (3D) cell models lie in the physical and chemical cues present within the TME that cannot be reproduced in conventional 2D monolayer cultures. Tumor cells frequently show altered metabolism resulting in an increase in lactic acid production which lowers the pH of the TME. In addition, 3D spheroids are characterized by a necrotic core which is a result of limited oxygen and nutrient penetration. Tumor lactic acid production has been shown to be detrimental to T-cell proliferation and anti-tumor function.4 Thus, 3D tumor spheroids serve as a more representative model for the study of T-cell-induce tumor cytotoxicity than 2D monolayer cultures.

Numerous studies have now established a general correlation between immune infiltrate and prognosis.5 Increasing infiltration and anti-tumor T-cells activity in the TME is used as a potential indicator of a successful therapy. Here we describe a method for high-throughput assessment of T-cell-induced cytotoxicity and infiltration into multi-cellular 3D tumor spheroids. Spheroids formed with Hela cells were co-cultured with stimulated prestained T-cells. Time-lapse imaging was carried out for 3 days using the ImageXpress® Micro Confocal HighContent Imaging System equipped with environmental control. To quantify T-cell penetration in the spheroid, we developed an analysis workflow that measures the distance (in 3D) of each T-cell from the edge of the spheroid. Our results show that the stimulated T-cells have much greater penetration distances compared to the non-stimulated control. Overall, our results show that 3D spheroid models and the 3D penetration analysis workflow can be used as a metric to evaluate the efficacy of immune T-cell therapy in vitro.

Materials and methods

Cell culture

The Hela cell line (ATCC) was used to generate spheroids. Cells were stained with MitoTracker Deep Red at 100nM (Thermo) before seeding (2000 cells per well) into 96-well U-bottom plate (Corning) and incubated at 37°C for 2 days to allow spheroid formation.

T-cell assay

Thawed PBMC/T-cells were stimulated with PMA/I for 6 hours and stained with CellTracker Green at 1µM (Thermo) before co-culturing with spheroids for 72 hours with 10 times more T cells than Hela cells, that is, E:T=10. Unstimulated T-cells were used as negative controls.

Live cell imaging

We performed the time-lapse live imaging using the ImageXpress Micro Confocal system equipped with sCMOS camera and MetaXpress® High-Content Image Acquisition and Analysis Software (v6.7) to capture the 3D structures of the whole spheroids. The 3D stacks and 2D projection images of transmitted light (TL) and corresponding fluorescent channels were acquired and saved using 10X objective (N.A.=0.5) with 10µm focus step every 2 hours (Figure 1).

Figure 1. T-Cell Penetration Assay Workflow and ImageXpress Micro Confocal High-Content Imaging System.

3D image analysis

To characterize T-cell penetration into spheroids, we used the 3D custom module editor (CME) to segment and detect the T-cell’s location within the spheroids (Figure 2). Briefly, whole spheroids (Cy5 channel, mask not shown) and individual T-cells (FITC channel, Figure 2B, 2D) were segmented. The distance to the edge of the spheroid mask was transformed into a 16-bit grayscale image where the intensity value indicates the 3D distance to the nearest edge of the mask (Figure 2C, 2E). The 3D penetration distance of T-cells within the spheroid was measured by aligning the T-cell mask with the transformed 16-bit grayscale image.

Figure 2. (A) The CME interface with TL channel (left), mitotracker (middle) and T-cell (right); (B) Find Round Objects module; (C) The 3D Distance module; (D) Masks of T-cells generated from the image in (A); (E) 16-bit grayscale image generated from the TL image.

Results

Visualization of T-cell penetration over time

To visualize T-cell penetration over time, we overlay the T-cell masks and TL images of the spheroids acquired from the same z-plane across all the time points. We clearly see that the T-cells are accumulated around the edge of the spheroids in the beginning and move towards the center of the spheroids across time (Figure 3).

Figure 3. T-cell penetration into Hela spheroids across time, shown in 6hr intervals. These are single z-planes of spheroids acquired in TL overlaid with T-cells in cyan.

Object-level and well-level statistics

Instead of 2D projection, we acquired and performed the analysis of the images in 3D stacks with the rationale that 2D projection may have T-cells outside the spheroids projected inside the spheroids. The resulting object-level and well-level (sum) T-cell climbing distance in z and penetration distance into the spheroids are outlined in Figure 4. The statistics clearly show that the activated T-cells not only penetrate deeper (Figure 4B, 4D) but also climb higher (Figure 4A, 4C) into the spheroids across time, while the non-activated T-cells stay at the superficial surface and lower height of spheroids. Thus, with the metrics of penetration distances and climbing distances, our results may serve as a tool to evaluate the efficacy of T-cells on cancer cells in vitro.

Figure 4. IA) Violin plot of object-level T-cell climbing height in Z; (B) Scatter plot of well-level T-cell climbing height in Z; (C) Violin plot of object-level T-cell penetration distance into spheroids; (D) Scatter plot of well-level T-cell penetration distance into spheroids.

Conclusions

• We used time-lapse, high-content imaging to monitor the growth and phenotypic changes of 3D spheroids.
• We successfully used 3D CME modules to analyze the T-cell penetration distance into the spheroids.
• Our results show clear effect of activated T-cells on cancer spheroids compared to non-activated T-cells, which may serve as a tool for T-cell evaluation in vitro.

References

1. Porter DL, Levine BL, Kalos M, Bagg A, June CH. Chimeric antigen receptor-modified T-cells in chronic lymphoid leukemia [published correction appears in N Engl J Med. 2016 Mar 10;374(10):998]. N Engl J Med. 2011;365(8):725–733. doi:10.1056/NEJMoa1103849
2. Wolchok JD, Kluger H, Callahan MK, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013;369(2):122–133. doi:10.1056/NEJMoa1302369
3. Beatty GL, Gladney WL. Immune escape mechanisms as a guide for cancer immunotherapy. Clin Cancer Res. 2015;21(4):687–692. doi:10.1158/1078-0432.CCR-14-1860
4. Fischer K, Hoffmann P, Voelkl S, et al. Inhibitory effect of tumor cell-derived lactic acid on human T-cells. Blood. 2007;109(9): 3812–3819. doi:10.1182/blood-2006-07-035972
5. Jochems C, Schlom J. Tumor-infiltrating immune cells and prognosis: the potential link between conventional cancer therapy and immunity. Exp Biol Med (Maywood). 2011;236(5):567–579. doi:10.1258/ebm.2011.011007

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