Back

Cryopreservation of Human Cortical Organoids Using Vitrification.

Mojica-Perez, S.; Stokes, K.; Jacobs, S.; Huang, J.; Vaid, S.; Yuan, y.; Pearson, C. A.; Montes, D.; Tidball, A.; VanHeyningen, D.; Gaillard, J.; Dang, L. T.; Ross, M. E.; Isom, L. L.; Uhler, M.; Niu, W.; Parent, J. M.

2025-04-11 neuroscience
10.1101/2025.04.08.647634 bioRxiv
Show abstract

Cryopreservation at ultra-low temperatures is a valuable tool for preserving cells and tissues used in research. However, few protocols exist for the preservation of brain organoid models. Current methods for preserving human cortical organoids (hCOs) rely on conventional slow cooling approaches with organoids suspended in a medium containing a cocktail of cryoprotectants. In contrast, we have optimized a vitrification technique previously used to cryopreserve human embryos and oocytes for application to hCOs. We have successfully cryopreserved hCOs that were generated by two different protocols. The vitrified organoids demonstrate a growth rate, cytoarchitecture, cell type composition and electrical activity comparable to non-vitrified controls. Our hCO cryopreservation method provides a useful alternative approach for bio-banking and cross-institutional collaboration using cortical organoids as their model system. Highlights- Human pluripotent stem cell (hPSC) derived hCOs can be cryopreserved using vitrification. - Vitrified hCOs develop similarly to non-vitrified controls. - Vitrified hCOs can be stored long-term and shipped to other institutions. MotivationMany methods of cryopreservation have been developed for the maintenance of cell lines, organoid models and tissues. Most organoid models have been successfully preserved using conventional slow-cooling methods; however, human cortical organoids have been difficult to preserve. Our findings demonstrate that the vitrification of cortical organoids preserves their structure, cell type diversity and function upon rewarming and continued culture.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.