PUBLICATION

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

Authors
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.
ID
ZDB-PUB-190108-13
Date
2019
Source
PLoS One   14: e0202377 (Journal)
Registered Authors
Boland, Ralf, Cordero-Maldonado, Maria Lorena, Crawford, Alexander, de Sonneville, Jan, Heins-Marroquin, Ursula, Meijer, Annemarie H., Spaink, Herman P.
Keywords
none
MeSH Terms
  • Gene Editing/methods*
  • Embryo, Nonmammalian/embryology*
  • Animals
  • Zebrafish*/embryology
  • Zebrafish*/genetics
  • Embryonic Development/genetics*
  • Deep Learning*
  • Microinjections/methods
  • Gene Knock-In Techniques/methods*
(all 9)
PubMed
30615627 Full text @ PLoS One
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.
Genes / Markers
Figures
Figure Gallery (1 images)
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Expression
Phenotype
No data available
Mutations / Transgenics
No data available
Human Disease / Model
No data available
Sequence Targeting Reagents
Fish
Antibodies
No data available
Orthology
No data available
Engineered Foreign Genes
No data available
Mapping
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Citation
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. (2019) Deep learning image recognition enables efficient genome editing in zebrafish by automated injections. PLoS One. 14:e0202377.