Active Ribosome Profiling

Unveiling the Actively Translated Population

How it compares to standard Ribo-Seq

  • A population of messenger RNA molecules exists in a "stalled" state. In this scenario, ribosomes are loaded onto the mRNA and ready for translation, but actual protein production does not occur. 
  • Standard Ribo-Seq captures all ribosomes on mRNA, including those paused and not actively translating proteins. This underestimates true translation efficiency and weakens the connection to protein levels measured by proteomics methodologies. 
  • Active Ribo-Seq only targets actively translating ribosomes. This allows the discrimination of the active and the stalled ribosome populations and offers a more accurate picture of translation efficiency.  

How it works

  • Conventional Ribo-Seq techniques rely on separating ribosomes based on their association with mRNA (polysomes) using fractionation on a density gradient. Since actively translating and stalled ribosomes have the same molecular weight, this method can't differentiate between them.  
  • RiboLaceTM is Immagina's proprietary technique that selects ribosomes based on their conformational state. By relying on a probe that is specific for ribosomes in the open state, it is possible to specifically isolate and analyze ribosomes that are actively translating mRNA.   


  • Bypasses the issue of stalled ribosomes
  • Provides a more accurate picture of the "active" translation population
  • Ensures better correlation with proteomics
  • Allows for more robust and affordable experimental workflows 

Delivery time: 10 to 12 weeks

Service description and outcomes

  • Pre-service consultation: experimental design and BioIT analyses
  • Input material: flash-frozen cell pallets or tissues 
  • RPF pulldown and library preparation
  • NGS sequencing
  • Bioinformatic analysis 

Technical applications

  • Studying translational control
  • Identification of new mechanisms of translational regulation
  • Locating translational start sites and alternative reading frames 
  • Quantifying translational efficiency