Automated microinjection for zebrafish xenograft models
Yi Ding, Kees-Jan van der Kolk, Wietske van der Ent, Michele Scotto di Mase, Saskia Kowald, Jenny Huizing, Jana M. Vidal Teuton, Gunja Mishra, Maxime Kempers, Rusul Almter, Sandra Kunz, Laurine Munier, Carl Koschmann, Sebastian M. Waszak, Vincenzo Di Donato, Sylvia Dyballa, Peter ten Dijke, Camila V. Esguerra, Lasse D. Jensen, Jan de Sonneville
- 发表年份
- 2024
- 引用次数
- 2
- 访问权限
- 开放获取
摘要
Abstract Zebrafish xenograft models have been increasingly recognized for their ability to predict patient responses to cancer therapeutics, suggesting their potential as diagnostic tools in clinical settings. However, these models require the precise microinjection of cancer cell suspensions in many small and fragile zebrafish larvae. Manual injections are so challenging that, even after months of training, variability in experimental results persists among researchers. This limits the uptake and deployment of zebrafish xenograft models for clinical use and drug discovery. To address this challenge, we have designed, built, and validated an automated microinjection robot. Combined results of injections into the vasculature, perivitelline space, and hindbrain ventricle demonstrated an average injection success rate of approximately 60%, with a larvae survival rate exceeding 70%, comparable to manual injections using a traditional micromanipulator. Notably, the full automated mode was twice as fast as manual injections. This automation of the microinjection process significantly reduces the need for extensive personnel training while it enhances reproducibility, efficiency, and accuracy, paving the way for more extensive use of zebrafish xenograft models in drug discovery and patient diagnostics.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002