Computational design and interpretation of single-RNA translation experiments.

Oleg A Igoshin,Luis U Aguilera,Zachary R Fox,Elliot Djokic,Michael May,Brian Munsky,Tatsuya Morisaki,Timothy J Stasevich,William Raymond

doi:10.1371/journal.pcbi.1007425

Abstract

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, β-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.

Highlights

The central dogma of molecular biology (i.e., DNA codes are transcribed into messenger RNA, which are translated to build proteins) has been a foundation of biological understanding since it was stated by Francis Crick in 1958
Translation is an essential step in which ribosomes decipher mRNA sequences to manufacture proteins
The first design is related to Fluorescence Correlation Spectroscopy (FCS), in that the nascent protein fluorescence signal is monitored over time and used to compute the auto-covariance function (G(τ), Fig 1C, bottom)

Summary

Introduction

The central dogma of molecular biology (i.e., DNA codes are transcribed into messenger RNA, which are translated to build proteins) has been a foundation of biological understanding since it was stated by Francis Crick in 1958 Despite their overwhelming importance to biological and biomedical science, many of the fundamental steps in the gene expression process are only becoming observable in living cells through the application of real time single-molecule fluorescence imaging approaches. A second approach to measure translation rate is to chemically block translation initiation (e.g., through the application of a drug such as Harringtonine, as depicted, top) In this Run-Off Assay (ROA) approach, the time, τROA, at which the fluorescence signal disappears corresponds to the time for a single ribosome to translate the entire coding region, including the tag region itself [10]. As for the FCS approach, the time of total recovery, τFRAP, relates to the time required for a single ribosome to complete translation from the tag region to the termination codon [8, 9]

Methods

Results

Discussion

Conclusion