(610e) Incorporation of Different Macrophage Phenotypes in Spatially-Defined in Vitro Tumor Microenvironment Models

The tumor microenvironment (TME) has a decisive role in the proliferation, metastasis, and response of cancer cells to treatment [1]. In addition to the extracellular matrix, the TME comprises numerous cell types, including fibroblasts, endothelial cells, and immune cells [1]. Macrophages are considered a major inflammatory component of the stroma, which could significantly impact cancer progression [2]. Macrophages may be polarized into M1-like and M2-like phenotypes, generally corresponding to pro-inflammatory and immunosuppressive characteristics, respectively [3]. Previous studies on different cancers such as prostate [5] and pancreatic cancer [2] have shown that the ratio of M1-like to M2-like macrophages impacts the phenotype of cancer and the survival of the patients. Additionally, there are studies suggesting that the spatial composition of these two phenotypes could vary in different regions of TME [6]. This spatial and compositional dependence is of great interest to clinicians and researchers; however, if is challenging to study in a controlled and systematic fashion. In vivo models often do not follow the same progression and trajectory as human disease. While current in vitro models can benefit from the incorporation of human cells, they fall short in presenting the required throughput and/or control over the contributing factors to the TME. Here, we propose a DNA-directed patterning approach to develop in vitro models of the TME [7]. Using this approach, we benefit from the high-throughput nature of DNA-directed patterning while introducing macrophages to the TME with the predefined ratio and spatial configuration of M1-like and M2-like macrophages. We demonstrate two applications of this approach in the study of pancreatic and prostate cancer microenvironments.

EXPERIMENTAL

DNA-directed patterning was used to pattern the cells to recapitulate different TMEs, allowing us to precisely define the concentration and spatial configuration of macrophages and the ratio of M1-like and M2-like macrophages. Briefly, we first patterned single-stranded oligonucleotides on the surface of a glass slide. An aldehyde-functionalized glass slide was spin-coated with S1813, a positive photoresist, then exposed to UV light to photopattern select regions. Then, a developer solution, MF321, was used to remove the exposed areas of the photoresist. Next, amine-terminated single-stranded oligonucleotides (20 bp in length) were immobilized on the glass substrate through reductive amination. The remaining photoresist was then removed with acetone. This process could be repeated to pattern different oligonucleotide sequences in different regions of the slide.

The different cell types of interest were then labeled with the complementary oligonucleotide as described previously [8]. Cells suspended in PBS were first incubated with 141 bp single-stranded oligonucleotide sequences attached to a cholesterol molecule. This oligonucleotide sequence contains both the complementary sequence to that patterned on the class slide as well as a 20 bp sequence that allows further anchoring of the cholesterol-oligonucleotide to a 20 bp oligo-cholesterol conjugate subsequently incubated with the cells. Thus, labeled with the appropriate oligonucleotide sequence, we were able to pattern the cells in the selected regions based on Watson-Crick base pairing (Fig. 1a, b). This enabled us to pattern cells in predefined configurations so we can recapitulate different TMEs. As the final step, we also incorporated ECM to create a “2.5D” system

To show the general applicability of this approach, we have designed two different TMEs. First, we have recapitulated the pancreatic TME using pancreatic cancer cells, cancer-associated fibroblasts, and M1-like and M2-like macrophages (Fig. 1c). We have also used this approach to mimic the spatial configuration of cells in the endosteal niche in the bone marrow TME, a common site of prostate cancer metastasis (Fig. 1d) [9]. In this design, we have patterned osteoblasts, osteoclasts, and macrophages. To culture several cell types simultaneously, it is important to determine a mutually supportive media that enables the viability and functionality of all the cell types. For each of these systems, we tested different ratios of different base media to find the optimum media. Finally, to assess the phenotype of the cells, we used immunofluorescence to evaluate relevant markers for each cell type. Functional assays (e.g., Alizarin Red S staining, tartrate-resistant acid phosphatase staining) were used to assess the behavior of the cells in the endosteal niche. Enzyme-linked immunosorbent assays (ELISAs) were performed to evaluate the polarization of macrophages.

RESULTS AND DISCUSSION

In this study, we have used representative cells from two different TMEs to show the robustness of DNA-directed patterning for developing in vitro models with a predefined spatial configuration. Patterning was confirmed by labeling cells with CellTracker dye, showing that each cell type was immobilized in the specified region.

To find the optimum media, cells were cultured in different media compositions for 1 week followed by live/dead staining using Calcein AM and propidium iodide. The media with the highest viability amongst all cell types was chosen for further use. Cultured cells were then analyzed for relevant markers to ensure the maintenance of phenotype (Table 1). Both immunofluorescence staining and ELISAs were used to validate macrophage phenotypes and evaluate how they changed over time. Since such systems provide both throughput and precision, they could be used to investigate the interaction of different cells in TME and eventually for drug screening for cancer therapy.

CONCLUSION

Using DNA-directed patterning, we were able to develop a high-throughput method for precisely incorporating different cell types present in the TME, including macrophages. Future directions include further analysis of the interactions between these different cell types and evaluation of long-term cultures, with an interest in modeling progression and response to treatment.

REFERENCES

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