Abstract
Developmental and physiological processes depend on the transcriptional and translational activity of heterogeneous cell populations. A main challenge in gene expression studies is dealing with this intrinsic complexity while keeping sequencing efficiency. Translating ribosome affinity purification (TRAP) methods have allowed cell-specific recovery of polyribosome-associated RNAs by genetic tagging of ribosomes in selected cell populations. Here we combined the TRAP approach with adapted enhancer trap methods (trap-TRAP) to systematically generate zebrafish transgenic lines suitable for tissue-specific translatome interrogation. Through the random integration of a GFP-tagged version of the large subunit ribosomal protein L10a (EGFP-Rpl10a), we have generated stable lines driving expression in a variety of tissues, including the retina, skeletal muscle, lateral line primordia, rhombomeres, or jaws. To increase the range of applications, a UAS:TRAP transgenic line compatible with available Gal4 lines was also generated and tested. The resulting collection of lines and applications constitutes a resource for the zebrafish community in developmental genetics, organ physiology and disease modelling.
Highlights
The precise combination of repressed and activated genes determines the identity and transcriptional state of the cells, and controls their shape, mechanical properties, physiology, pathology, and survival
For the comparative expression analysis between whole-embryo (GEO accession number: GSM4521281) and vsx2:translating ribosome affinity purification (TRAP) transcripts, we examined RPKM values for a list of transcription factor (TF) encoding genes that have been shown as enriched at the neural retina domain vs the RPE in cell populations isolated by fluorescence-activated cell sorting (FACS) (Buono et al, 2021)
We have described two different approaches, Trap-TRAP and UAS-TRAP, which allow expanding the use of the TRAP methodology in zebrafish and can be adapted to other model organisms
Summary
The precise combination of repressed and activated genes determines the identity and transcriptional state of the cells, and controls their shape, mechanical properties, physiology, pathology, and survival. Among the numerous NGS applications, RNA-seq has become a method of choice in transcriptomics due to its high reproducibility, unbiased detection, single nucleotide resolution and quantitative estimation over a large dynamic range of gene expression (Wang et al, 2009). Using an “en masse” approach, gene expression profiles cannot be assigned to any specific cell type, but reflect averaged gene expression across the entire tissue. Many of these disadvantages have been overcome by single-cell RNA sequencing (scRNA-seq) technologies, which permit the characterization of heterogeneous cell populations, making possible to detect the signature of rare cell types or transient cellular states (Chen et al, 2019). The indepth transcriptomic characterization of a given cell type still depends on our ability to isolate them either through microdissection or using fluorescence-activated cell sorting (FACS) protocols
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