Deep learning image recognition enables efficient genome editing in zebrafish by automated injections

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Deep learning image recognition enables efficient genome editing in zebrafish by automated injections

Type: Article / Letter to editor
Title: Deep learning image recognition enables efficient genome editing in zebrafish by automated injections
Author: Cordero-Maldonado, M.L.Perathoner, S.Kolk, K.J. van derBoland, R.Heins-Marroquin, U.Spaink, H.P.Meijer, A.H.Crawford, A.D.Sonneville, J. de
Journal Title: PLOS One
Issue: 1
Volume: 14
Issue Date: 2019
Abstract: One of the most popular techniques in zebrafish research is microinjection. This is a rapid and efficient way to genetically manipulate early developing embryos, and to introduce microbes, chemical compounds, nanoparticles 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.
Handle: http://hdl.handle.net/1887/69907
 

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