ZFIN ID: ZDB-PUB-170414-11
Biotagging of Specific Cell Populations in Zebrafish Reveals Gene Regulatory Logic Encoded in the Nuclear Transcriptome
Trinh, L.A., Chong-Morrison, V., Gavriouchkina, D., Hochgreb-Hägele, T., Senanayake, U., Fraser, S.E., Sauka-Spengler, T.
Date: 2017
Source: Cell Reports   19: 425-440 (Journal)
Registered Authors: Fraser, Scott E., Sauka-Spengler, Tatjana, Trinh, Le
Keywords: bi-directional transcription, cis-regulation, enhancers, in vivo biotinylation, myocardium, neural crest, nuclear transcriptome
Microarrays: GEO:GSE89670
MeSH Terms:
  • Animals
  • Cell Compartmentation/genetics
  • Cell Lineage/genetics
  • Conserved Sequence/genetics
  • Gene Expression Regulation, Developmental
  • Gene Regulatory Networks/genetics
  • Neural Crest/growth & development*
  • Organ Specificity/genetics
  • Transcription Factors/biosynthesis*
  • Transcription Factors/genetics
  • Transcriptome/genetics*
  • Zebrafish/genetics*
  • Zebrafish/growth & development
PubMed: 28402863 Full text @ Cell Rep.
Interrogation of gene regulatory circuits in complex organisms requires precise tools for the selection of individual cell types and robust methods for biochemical profiling of target proteins. We have developed a versatile, tissue-specific binary in vivo biotinylation system in zebrafish termed biotagging that uses genetically encoded components to biotinylate target proteins, enabling in-depth genome-wide analyses of their molecular interactions. Using tissue-specific drivers and cell-compartment-specific effector lines, we demonstrate the specificity of the biotagging toolkit at the biochemical, cellular, and transcriptional levels. We use biotagging to characterize the in vivo transcriptional landscape of migratory neural crest and myocardial cells in different cellular compartments (ribosomes and nucleus). These analyses reveal a comprehensive network of coding and non-coding RNAs and cis-regulatory modules, demonstrating that tissue-specific identity is embedded in the nuclear transcriptomes. By eliminating background inherent to complex embryonic environments, biotagging allows analyses of molecular interactions at high resolution.