Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d

In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA knockdown and Cas9-mediated DNA targeting in zebrafish embryos. They targeted the no tail (TBXTA) gene as a proof-of-principle, evaluating the induced embryonic phenotypes. Both Cas9 and CasRx systems caused loss of function phenotypes for TBXTA. Cas9 protein exhibited a higher percentage of severe phenotypes compared with mRNA, while CasRx protein and mRNA showed similar efficiency. Both robotic and manual injections demonstrated comparable phenotype percentages and mortality rates. The findings highlight the potential of RNA-targeting CRISPR effectors for precise gene knockdown and endorse automated microinjection at a speed of 1.0 s per embryo as a high-throughput alternative to manual methods.

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

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.

Analysis of RNAseq datasets from a comparative infectious disease zebrafish model using GeneTiles bioinformatics

We present a RNA deep sequencing (RNAseq) analysis of a comparison of the transcriptome responses to infection of zebrafish larvae with Staphylococcus epidermidis and Mycobacterium marinum bacteria. We show how our developed GeneTiles software can improve RNAseq analysis approaches by more confidently identifying a large set of markers upon infection with these bacteria. For analysis of RNAseq data currently, software programs such as Bowtie2 and Samtools are indispensable. However, these programs that are designed for a LINUX environment require some dedicated programming skills and have no options for visualisation of the resulting mapped sequence reads. Especially with large data sets, this makes the analysis time consuming and difficult for non-expert users. We have applied the GeneTiles software to the analysis of previously published and newly obtained RNAseq datasets of our zebrafish infection model, and we have shown the applicability of this approach also to published RNAseq datasets of other organisms by comparing our data with a published mammalian infection study. In addition, we have implemented the DEXSeq module in the GeneTiles software to identify genes, such as glucagon A, that are differentially spliced under infection conditions. In the analysis of our RNAseq data, this has led to the possibility to improve the size of data sets that could be efficiently compared without using problem-dedicated programs, leading to a quick identification of marker sets. Therefore, this approach will also be highly useful for transcriptome analyses of other organisms for which well-characterised genomes are available.