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Real-time Imaging of Single Engineered RNA Transcripts in Living Cells Using Ratiometric Bimolecular Beacons
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A Cas6-based RNA tracking platform functioning in a fluorescence-activation mode.

Feng Gao1, Ke Zheng1, You-Bo Li2

  • 1Gene Editing Research Center, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China.

Nucleic Acids Research
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

We developed Cas6FC, a novel RNA tracking platform using a mutated Cas6 enzyme. This system detects target RNAs in vivo with high sensitivity and specificity, overcoming background noise issues common in other methods.

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Area of Science:

  • Molecular Biology
  • RNA Biology
  • Biotechnology

Background:

  • RNA localization is crucial for cellular function, driving the need for live-cell RNA tracking techniques.
  • Existing methods like MS2-MCP and CRISPR/Cas9/Cas13 struggle with high background noise.
  • CRISPR/Cas6 exhibits allosteric changes upon binding its RNA target site.

Purpose of the Study:

  • To develop a new RNA tracking platform with high sensitivity and specificity.
  • To overcome the limitations of high background noise in current RNA detection methods.
  • To create a tool for in vivo RNA detection with minimal perturbation to the target RNA.

Main Methods:

  • Engineered a Cas6-based Fluorescence Complementation (Cas6FC) platform.
  • Utilized a mutated Escherichia coli Cas6 (dEcCas6) enzyme.
  • Conjugated split-Venus fragments to dEcCas6 for ligand-activated complementation upon binding the Cas6 Binding Site (CBS) on target RNAs.

Main Results:

  • Cas6FC successfully detected target RNAs in living cells with near-zero background noise.
  • The platform demonstrated high sensitivity, detecting RNAs with as little as one copy of the CBS tag.
  • Minimal tagging (29nt CBS) reduced the likelihood of altering target RNA conformation and localization.

Conclusions:

  • Cas6FC is a novel, highly sensitive, and specific RNA tracking platform for live-cell applications.
  • This method effectively addresses the challenge of background noise in RNA detection.
  • The platform's minimal tagging approach preserves the native state of target RNAs, enabling more accurate biological studies.