PT  - JOURNAL ARTICLE
AU  - Maria Lorena Cordero-Maldonado
AU  - Simon Perathoner
AU  - Kees-Jan van der Kolk
AU  - Ralf Boland
AU  - Ursula Heins-Marroquin
AU  - Herman P. Spaink
AU  - Annemarie H. Meijer
AU  - Alexander D. Crawford
AU  - Jan de Sonneville
TI  - Deep learning image recognition enables efficient genome editing in zebrafish by automated injections
AID  - 10.1101/384735
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 384735
4099  - http://biorxiv.org/content/early/2018/08/03/384735.short
4100  - http://biorxiv.org/content/early/2018/08/03/384735.full
AB  - One of the most popular techniques in zebrafish research is microinjection, as it is a rapid and efficient way to genetically manipulate early developing embryos, and to introduce microbes or tracers at larval stages.Here we demonstrate the development of a machine learning software that allows for microinjection at a trained target site in zebrafish eggs at unprecedented speed. The software is based on the open-source deep-learning library Inception v3.In a first step, the software distinguishes wells containing embryos at one-cell stage from wells to be skipped with an accuracy of 93%. A second step was developed to pinpoint the injection site. Deep learning allows to predict this location on average within 42 µm to manually annotated sites. Using a Graphics Processing Unit (GPU), both steps together take less than 100 milliseconds. We first tested our system by injecting a morpholino into the middle of the yolk and found that the automated injection efficiency is as efficient as manual injection (~ 80%). Next, we tested both CRISPR/Cas9 and DNA construct injections into the zygote and obtained a comparable efficiency to that of an experienced experimentalist. Combined with a higher throughput, this results in a higher yield. Hence, the automated injection of CRISPR/Cas9 will allow high-throughput applications to knock out and knock in relevant genes to study their mechanisms or pathways of interest in diverse areas of biomedical research.