BUHO: a MATLAB script for the study of stress granules and processing bodies by high-throughput image analysis

PLoS One. 2012;7(12):e51495. doi: 10.1371/journal.pone.0051495. Epub 2012 Dec 20.

Abstract

The spontaneous and reversible formation of foci and filaments that contain proteins involved in different metabolic processes is common in both the nucleus and the cytoplasm. Stress granules (SGs) and processing bodies (PBs) belong to a novel family of cellular structures collectively known as mRNA silencing foci that harbour repressed mRNAs and their associated proteins. SGs and PBs are highly dynamic and they form upon stress and dissolve thus releasing the repressed mRNAs according to changes in cell physiology. In addition, aggregates containing abnormal proteins are frequent in neurodegenerative disorders. In spite of the growing relevance of these supramolecular aggregates to diverse cellular functions a reliable automated tool for their systematic analysis is lacking. Here we report a MATLAB Script termed BUHO for the high-throughput image analysis of cellular foci. We used BUHO to assess the number, size and distribution of distinct objects with minimal deviation from manually obtained parameters. BUHO successfully addressed the induction of both SGs and PBs in mammalian and insect cells exposed to different stress stimuli. We also used BUHO to assess the dynamics of specific mRNA-silencing foci termed Smaug 1 foci (S-foci) in primary neurons upon synaptic stimulation. Finally, we used BUHO to analyze the role of candidate genes on SG formation in an RNAi-based experiment. We found that FAK56D, GCN2 and PP1 govern SG formation. The role of PP1 is conserved in mammalian cells as judged by the effect of the PP1 inhibitor salubrinal, and involves dephosphorylation of the translation factor eIF2α. All these experiments were analyzed manually and by BUHO and the results differed in less than 5% of the average value. The automated analysis by this user-friendly method will allow high-throughput image processing in short times by providing a robust, flexible and reliable alternative to the laborious and sometimes unfeasible visual scrutiny.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Drosophila melanogaster / cytology*
  • Drosophila melanogaster / genetics
  • Image Processing, Computer-Assisted / methods*
  • Molecular Imaging / methods*
  • Organelles / metabolism*
  • Oxidative Stress
  • RNA Interference
  • RNA, Messenger / genetics
  • RNA, Messenger / isolation & purification
  • Software*
  • Synapses / metabolism
  • Time Factors

Substances

  • RNA, Messenger

Grants and funding

This work was supported by the following grants UBACyT X311 from University of Buenos Aires, Argentina; PIP 6173 from Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET); PICT 38006 and PICT 1965 from Agencia Nacional de Promoción Científica y Tecnológica, (ANPCyT), Argentina. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.