An Efficient Organoid Cutting Method for Long-Term Culture and High-Throughput Analyses

Background: Human organoid models are invaluable for developmental studies, disease modeling, and personalized medicine research. However, long-term maintenance is challenging due to hypoxia and nutrient limitations as organoids grow. Cutting organoids improves viability, but current methods have low throughput and are prone to causing culture contamination. This study introduces an efficient organoid cutting method to enhance long-term culture and enable high-throughput analyses. Methods: We employed three-dimensional (3D) printing to fabricate four classes of organoid cutting jigs with blade guides that were compared and optimized for consistent sectioning of human pluripotent stem cell (hPSC)-derived organoids. Organoids were cultured in mini-spin bioreactors and cut every three weeks, beginning on day 35. Organoid health and growth were evaluated by size increase and proliferative marker expression. Additionally, we utilized 3D printed molds to create GelMA or Geltrex-embedded organoid arrays and silicone molds for optimal cutting temperature compound (OCT)-embedding of organoid arrays. Results: All 3D printed jigs enabled rapid and uniform organoid cutting under sterile conditions. We determined that a flat-bottom cutting jig design had superior cutting efficiency. Cutting improved nutrient diffusion, increased cell proliferation, and enhanced organoid growth during long-term culture. The mold-based approaches enabled the creation of densely packed organoid arrays and cryosections with evenly distributed organoids. Conclusion: This novel organoid cutting and arraying method overcomes limitations in long-term organoid culture and high-throughput processing. The simplicity of the cutter design and handling make it a versatile tool for diverse types of organoids. By enhancing organoid viability and enabling consistent sample preparation, this approach facilitates improved organ development and disease modeling, drug screening, and high-throughput analyses, including single-cell spatial transcriptomics applications.