The tissue microenvironment is influenced by various factors, such as the extracellular matrix and cell-cell interactions, which play an essential role in tissue formation and function. However, it is difficult to replicate this complex in vivo environment when cells are cultured in 2D. Fortunately, 3D cell cultures can bridge this gap and create a more physiologically relevant model. These cultures grow in a 3D space using scaffold-based or scaffold-free techniques. This allows for more natural gene expression, morphology, and cell-cell and cell-environment interactions.
3D cultures can be used to grow both spheroids and organoids. Spheroids are simple spherical structures that typically contain one cell type. Organoids are more complex and consist of a variety of cellular phenotypes that mimic the structure and function of organs. This complexity exists because they are derived from stem cells or by isolating and cultivating small tissue fragments. However, they are not as complex as real organs since they lack components like blood vessels and nerves. Nonetheless, organoids hold immense potential for applications such as disease modeling, drug development, personalized medicine, and cancer research.
Incorporating deep learning, a branch of artificial intelligence, into 3D cell culture brings a transformative approach to data analysis and interpretation. These advanced algorithms offer sophisticated tools for analyzing the vast amount of complex data generated by 3D culture studies. By employing deep learning, researchers can better understand patterns in gene expression, cell morphology, and other critical parameters.
This eBook covers questions to ask when transitioning from 2D to 3D cell culture and articles on the role of 3D organoids in research, their importance in drug development, and an exploration of deep learning as a future tool in organoid work.
Download this eBook to learn more about:
- Transitioning from 2D to 3D cell culture
- How organoids can advance your research
- The benefits of the organ-on-a-chip model for drug discovery
- Insights into AI-powered organoid analysis