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ZIRC
ZFIN ID: ZDB-PUB-190108-13
Deep learning image recognition enables efficient genome editing in zebrafish by automated injections
Cordero-Maldonado, M.L., Perathoner, S., van der Kolk, K.J., Boland, R., Heins-Marroquin, U., Spaink, H.P., Meijer, A.H., Crawford, A.D., de Sonneville, J.
Date: 2019
Source: PLoS One   14: e0202377 (Journal)
Registered Authors: Boland, Ralf, Crawford, Alexander, de Sonneville, Jan, Meijer, Annemarie H., Spaink, Herman P.
Keywords: none
MeSH Terms:
  • Animals
  • Deep Learning*
  • Embryo, Nonmammalian/embryology*
  • Embryonic Development/genetics*
  • Gene Editing/methods*
  • Gene Knock-In Techniques/methods*
  • Microinjections/methods
  • Zebrafish*/embryology
  • Zebrafish*/genetics
PubMed: 30615627 Full text @ PLoS One
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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.
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