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Zhisong Tong | Research Scientist | Molecular Devices
Angeline Lim | Senior Application Scientist | Molecular Devices

Introduction

Immunotherapy is increasingly popular as a type of cancer treatment. These therapies include the use of Chimeric Antigen Receptor-engineered T-cells (CAR T-cells), tumor-infiltrating lymphocytes (TIL), and other genetically modified T-cells to specifically target cancer cells. Although much success has been achieved with immunotherapy for the treatment of blood cancers, its efficacy remains limited in solid tumors. One of the reasons for this low success rate is attributed to the solid tumor microenvironment (TME) where suppressive cytokines limit the tumor-killing ability of T-cells. Therefore, understanding the role the TME plays in T-cell responses is essential for the development of effective cancer therapies.

The benefits of using 3-dimensional (3D) patient-derive organoids (PDOs) lie in the physical and chemical cues present within the TME that cannot be mimicked in traditional 2D monolayer cultures. Studies show that PDOs show similar responses to drugs as original tumors, suggesting the value of using PDOs to improve therapeutic outcomes. This means that PDOs are more relevant physiological and pathological cancer models that recapitulate the basic features of primary tumors and are more suited for assessing the effectiveness of T-cell killing than 2D cell models.

Despite the benefits associated with the use of PDOs, there are significant barriers to their widespread adoption in drug discovery. Organoid production is a costly and highly labor-intensive process. Moreover, organoid culture is a skilled manual process, that can have significant variability between operators. To address the challenges associated with the use of PDOs in large scale applications, a semiautomated bioprocess has been developed for the large-scale expansion of assay-ready organoids. Here, we developed a method to assess the effectiveness of T-cell invasion in solid tumors using PDOs. Using bioreactor expanded patient-derived colorectal cancer organoids (CRCs), activated human peripheral blood mononuclear cells (PBMCs) were stained with CellTracker and added to CRCs (stained with MitoTracker) in a 96-well microtiter plate and monitored every 4 hours for 3 days using a highcontent imager. To quantify T-cell invasion, we developed an image analysis method to measure the distance of each T-cell to the nearest organoid. We found that stimulated T-cells resulted in smaller interaction distance than non-stimulated T-cells. The results demonstrate the utility of the bioreactor-expanded organoids in largescale T-cell-based screens.

Instruments and methods

Workflow

Bioreactor-expanded patient-derived CRCs were patient-derived CRCs were thawed and cultured in 80% Matrigel® (Growth factor reduced, Corning). After 48 hours, organoids were collected and stained with MitoTracker Red (Thermo Fisher Scientific, manufacturer recommended concentration) and resuspended in 3% Matrigel before seeding into a 384-well flat-bottom plate with ultralow attachment (Corning). The thawed PBMC/T-cells were stimulated in PMA/i for 6 hours and stained with CellTracker Green (Thermo Fisher Scientific, manufacturer recommended concentration) before adding to the CRC organoids for co-culture. We used the ImageXpress® Confocal HT.ai High-Content Imaging System (Molecular Devices) equipped with spinning disk confocal and sCMOS camera to perform timelapse live imaging every 4 hours.

Figure 1. T-cell and CRC PDO interaction workflow (nonstimulated T-cells were used as control).

Automation setup

An automated workcell consisting of an incubator and a high-content imager (orange box in Figure 2) was used for monitoring the co-culture of organoids and T-cells. The Genera scheduling software (RETISOFT) was used to execute routine monitoring of the organoids in culture. The protocol involved the retrieval of the plate from the incubator, transport of plate to the ImageXpress Confocal HT.ai to image the organoids every 4 hours (z-stack acquisition, 10X), and placement of plate back in the incubator using PreciseFlex400 robotic arm (Brooks).

Figure 2. Layout of the automated workcell. This standardized workflow included a liquid handler (Hamilton), a robotic arm (Brooks), incubator (LiCONiC), the ImageXpress Confocal HT.ai system (Molecular Devices), ImageXpress Pico Automated Imaging System (Molecular Devices), SpectraMax® iD5 MultiMode Microplate Reader (Molecular Devices), AquaMax® Microplate Washer (Molecular Devices), a plate hotel, a centrifuge, and a barcode scanner. The curved arrows show an example of the process to monitor cells in culture where plates are moved from the incubator to the ImageXpress Confocal HT.ai system for imaging and then back to the incubator.

Assay optimization

Studies have shown that T-cells migrate poorly in dense collagen matrix². Because organoids are cultured in a high concentration of Matrigel (80%) which could impede T-cell movement, we first optimized the assay by first determining the minimal amount of Matrigel required to maintain organoid in culture. Ideally, the amount of Matrigel used should be sufficient to maintain the CRC structure while allowing T-cells to migrate freely towards the organoids. CRC organoids in 80% Matrigel continued to increase in size while those embedded in 3%, 5% or 10% Matrigel showed no significant change in size. Interestingly, CRC organoids in media only showed slight increase in size. This could be due to the effect of the biophysical properties of Matrigel (such as stiffness) on organoid morphology¹. From the results, we chose to use 3% Matrigel to maximize T-cell penetration efficacy while maintaining the overall organoid structure.

Figure 3. Mix of organoids with (A) 0% (media only), (B) 3%, (C) 5%, (D) 10%, and (E) 80% Matrigel at the start of culture 0hr; growth of organoids with (F) 0%, (G) 3%, (H) 5%, (I) 10%, and (J) 80% Matrigel at 52hr; (K) IN Carta® Image Analysis Software was used to measure the area of the organoids; (L) Average organoid growth rate over 52 hours for different concentrations of Matrigel. Standard deviation represented by error bars.

Results

T-cell reciprocal interaction with PDOs

We observed that T-cells and organoids displayed varying degrees of motility in culture. Figure 4 shows the progressive reciprocal interaction between T-cells and CRC organoids. Stimulated T-cells tended to accumulate around the organoids (Figure 4A), while non-stimulated T-cells did not. Compared to the non-stimulated condition, where the relative position of organoids remained unchanged, stimulated T-cells appeared to displace organoids in culture (see white arrows in Figure 4A).

T-cell accumulation analysis

To quantify the number of T-cells surrounding the organoids, we first measured the distance from each T-cell to the nearest organoid (where T-cells inside the organoids were not considered) (Figure 5A–5F), using the custom module editor (CME) in the MetaXpress® High-Content Image Acquisition and Analysis Software. Only T-cells within 50 µm of an organoid were analyzed. Our analysis show that T-cells localize to the edge of the organoid over time. The maximum number of T-cells at the organoid edge occurs 24hour post treatment (Timepoint 6, Figure 5). In contrast, non-stimulated T-cells do not show any preferential accumulation around the organoids but instead, are accumulate the end of the wells.

Figure 4. (A) Stimulated T-cells accumulated around the edge of the organoids and attracted the organoids together through the time; (B) non-stimulated T-cells accumulated around the edge of the wells without affecting the organoids.

Figure 5. (A) Mito-tracker Red staining mitochondria in the organoids; (B) the organoid masks generated from mito-tracker staining, indicating the boundary of each organoid; (C) 16-bit grayscale image with pixel value representing the distance to the nearest whole pixel in (B); (D) cell-tracker Green staining T-cells; (E) the T-cell masks generated from cell-tracker staining; (F) the distance of T-cell to the nearest organoid is measured by overlaying (C) and (E); (G) the average distance of all T-cells from all wells with the same treatment (10 wells for stimulated, 5 wells for non-stimulated) across all timepoints.

Conclusion

• We used time-lapse high-content imaging to monitor the interaction of T-cells and organoids using the ImageXpress Confocal HT.ai High-Content Imaging System.
• We used a custom module editor to analyze the T-cell accumulation on the organoid edges.
• We propose a novel workflow to measure the interaction between T-cells and the organoid.

References

1. Quantification of Visco-Elastic Properties of a Matrigel for Organoid Development as a Function of Polymer Concentration
2. Xiangming Liu, Yuemei Qiao, JianFeng Chen, Gaoxiang Ge, Basement membrane promotes tumor development by attenuating T cell activation, Journal of Molecular Cell Biology, Volume 14, Issue 2, February 2022

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