Stochastic de-repression of rhodopsins in single photoreceptors of the fly retina

Pranidhi Sood, Robert J. Johnston, Edo Kussell

Research output: Contribution to journalArticlepeer-review


The photoreceptors of the Drosophila compound eye are a classical model for studying cell fate specification. Photoreceptors (PRs) are organized in bundles of eight cells with two major types - inner PRs involved in color vision and outer PRs involved in motion detection. In wild type flies, most PRs express a single type of Rhodopsin (Rh): inner PRs express either Rh3, Rh4, Rh5 or Rh6 and outer PRs express Rh1. In outer PRs, the K50 homeodomain protein Dve is a key repressor that acts to ensure exclusive Rh expression. Loss of Dve results in de-repression of Rhodopsins in outer PRs, and leads to a wide distribution of expression levels. To quantify these effects, we introduce an automated image analysis method to measure Rhodopsin levels at the single cell level in 3D confocal stacks. Our sensitive methodology reveals cell-specific differences in Rhodopsin distributions among the outer PRs, observed over a developmental time course. We show that Rhodopsin distributions are consistent with a two-state model of gene expression, in which cells can be in either high or basal states of Rhodopsin production. Our model identifies a significant role of post-transcriptional regulation in establishing the two distinct states. The timescale for interconversion between basal and high states is shown to be on the order of days. Our results indicate that even in the absence of Dve, the Rhodopsin regulatory network can maintain highly stable states. We propose that the role of Dve in outer PRs is to buffer against rare fluctuations in this network.

Original languageEnglish (US)
Article numbere1002357
JournalPLoS computational biology
Issue number2
StatePublished - Feb 2012

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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