Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines.

The limited correlation between mRNA and protein levels within cells highlighted the need to study mechanisms of translational control. To decipher the factors that determine the rates of individual steps in mRNA translation, machine learning approaches are currently applied to large libraries of synthetic constructs, whose properties are generally different from those of endogenous mRNAs. To fill this gap and thus enable the discovery of elements driving the translation of individual endogenous mRNAs, we here report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically (i) ribosome profiling data from unperturbed cells as well as following the block of translation initiation (ribosome run-off, to trace translation elongation), (ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and (iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). These data will enable improved predictions of mRNA sequence-dependent protein output, which is crucial for engineering protein expression and for the design of mRNA vaccines.