Cellular stress leads to reprogramming of mRNA translation and formation of stress granules (SGs), membraneless organelles consisting of mRNA and RNA-binding proteins. Although the function of SGs remains largely unknown, it is widely assumed they contain exclusively nontranslating mRNA. Here we re-examine this hypothesis using single-molecule imaging of mRNA translation in living cells. While our data confirms that non-translating mRNAs are preferentially recruited to SGs, we find unequivocal evidence for translation of mRNA localized to SGs. Our data indicate that SG-associated translation is not rare and that the entire translation cycle (initiation, elongation and termination) can occur for these transcripts. Furthermore, translating mRNAs can be observed transitioning between the cytosol and SGs without changing their translational status. Together, these results argue against a direct role for SGs in inhibition of mRNA translation.

Detection of diffraction-limited spots in single-molecule microscopy images is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network-based method to detect and localize spots automatically. We demonstrate that deepBlink outperforms other state-of-the-art methods across six publicly available datasets containing synthetic and experimental data.

Detection of diffraction-limited spots is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network- based method to detect and localize spots automatically and demonstrate that deepBlink outperforms state-of-the-art methods across six publicly available datasets. deepBlink is open-sourced on PyPI and GitHub (https://github.com/BBQuercus/deepBlink) as a ready-to- use command-line interface.