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Traditionally, cell-based assays have been performed in monolayers growing on a solid substrate, which effectively can be considered as a two-dimensional (2D) environment. However, it is becoming increasingly recognized that 2D cell culture models lack physiological relevance in some contexts and cannot replicate many of the complex interactions that occur between cells1. Three-dimensional (3D) cell culture methods are rapidly becoming popular among researchers, and 3D cell models show high potential to better mimic the physiological conditions encountered by cells in the tissue environment2. There are several different types of 3D cell assemblies that have been employed, but the two most common types are spheroids and organoids. Spheroids can be grown from many different cell lines, and they can adopt various shapes and sizes depending on the cell type used and their method of assembly3. Moreover, spheroids can also be referred to as multicellular tumor spheroids (MCTS) when they are grown from cancer cell lines, and these models have found particular use for preclinical in vitro drug delivery and toxicity studies4,5. Organoids, on the other hand, aim to better mimic the tissues and organs in our body and can adopt more complex morphological arrangements. The production of organoids involves the use of adult stem cells or pluripotent stem cells, which can be reprogrammed into the appropriate cells to resemble the tissue or organ of interest. They are primarily used to investigate the development of organs and to model diseases and host-pathogen interactions6.
There is a range of different methods used to generate 3D cell assemblies. Scaffold-based methods provide a substrate or support to which cells can either attach or grow within. These scaffolds can have various shapes and can be made from a variety of different materials. The most common are extracellular matrix (ECM) components and hydrogels, and they are designed to resemble the natural extracellular environment of cells and thereby facilitate physiological interactions4,7. ECM basement material has been extracted from Engelbreth-Holm-Swarm mouse sarcoma tumor and shown to contain a rich mixture of ECM components, including laminin, type IV collagen, and perlecan8. However, despite its advantageous composition, there are two main challenges with its use, namely its batch-to-batch variability and that it has two different aggregate states below and above 10 °C8,9. In contrast, hydrogels have the advantage of being flexible with respect to their components and rigidity, and they can be customized to suit the specific 3D cell assembly desired7,10. Scaffold-based methods are essential for organoid growth but are also widely used for spheroids. Scaffold-free methods, which work by preventing cells from attaching to the surface they are growing on, are usually only compatible with spheroid assembly. Examples include ultra-low attachment (ULA) plates, with either a flat bottom or U-bottom, which allow aggregation of the cells into spheroids, or the use of continuous agitation of the cells in spinner/rotation flasks10.
The use of 3D cell assemblies to study a wide variety of biological events is rapidly gaining popularity; however, it is essential that the method chosen for their culture is appropriate and compatible with the plans for their downstream analysis. For example, the use of ULA plates generates spheroids of high consistency; however, this method is restricted to the production of a single spheroid per well, thereby limiting throughput. Particular consideration is needed when fluorescence imaging of the 3D structure is planned. The substrate or plate on which the assembly is grown needs to be optically compatible, and care must be taken to minimize the effects of light scattering caused by any scaffolds that may have been used11. This particular problem becomes more acute as the numerical aperture of the microscope objective lenses increases.
Arguably one of the major reasons for selecting to work with a 3D cell model is to extract volumetric imaging data about not only the entire assembly but also the individual cells within it. MCTS models, in particular, are beginning to prove very powerful for deepening our understanding of how therapeutics transit from the outside to central cells (as they would need to in a tumor)12, and so gaining knowledge from individual cells at different layers is essential. The imaging technology that extracts quantitative information from individual cells is termed high-content analysis (HCA) and is a powerful approach in the context of screening13. To date, HCA has almost exclusively been applied to monolayer cultures, but there is an increasing realization that this approach has the power to be applied to 3D cultures enabling a wide range of cellular functions and processes to be studied14. It would have the clear advantage that large numbers of 3D assemblies could be analyzed, potentially providing cell-level data from across each structure. However, challenges associated with imaging of potentially thick cell assemblies, as well as the large data sets generated, need to be overcome.
In this article, a robust scaffold-based method for the large-scale production of MCTS in a 96-well format is presented. The method facilitates the production of several hundred 3D cell assemblies in each well. Examples are shown for three different cell types, representing solid tumor models of the liver, lung, and colon. The spheroids that form can be of a variety of sizes, and so HCA is used to select structures of a particular size and/or morphology. This feature provides the additional advantage that any phenotypes observed can be compared across spheroids of different sizes, but all treated in the same way in the same well. This approach is compatible with high-resolution imaging, importantly providing both cell-level and subcellular level quantitative data from the same cellular assemblies. This method of spheroid production has the additional advantage over methods that generate a single spheroid per well, that the large numbers of spheroids produced in each well potentially provide sufficient biomass for other downstream analyses, such as transcriptome and proteome profiling.