Positional information transfer by sub-micron transcription factor clusters

ORAL

Abstract

Rapid and reproducible cell fate decisions require accurate interpretation of input signals. In the early fly embryo, cells gain information about their physical location by the reading of transcription factor (TF) molecules that are distributed in a long-range gradient spanning the major axis. Interpretation of information in the nucleus results from interaction of these diffuse transcription factor molecules with gene regulatory enhancer elements on the DNA. Recently clustering of the molecules inside the nucleus has been observed, but whether these clusters report directly on the extra-nuclear information is unclear. By imaging fluorescently-labelled TF molecules in live embryos, we show that clustering only occurs at target genes and this gives rise to the observed heterogeneity in nuclear TF distribution. The number of clusters detected per nucleus at various embryo positions is surprisingly reproducible across embryos. An exponential fit of the average intensity of the TF clusters shows that the cluster intensity decays twice as slowly as the nuclear intensity. However the sum of all cluster intensities within a nucleus, weighted by the cluster sizes has the same exponential decay constant as that of the nuclear intensity. Thus, we show that while clustering enables differential interpretation of the nuclear TF concentration at the gene loci, the clustered molecules cumulatively reflect the information in the protein concentration gradient and hence the positional identity of a cell.

*This work was supported in part by the US National Science Foundation, through the Center for the Physics of Biological Function (PHY– 1734030); by National Institutes of Health Grants R01GM097275, U01DA047730 and U01DK127429.

Presenters

  • Rahul Munshi

    • Princeton University

Authors

  • Rahul Munshi

    • Princeton University
  • Sergey Ryabichko

    • Princeton University
  • Eric F Wieschaus

    • Princeton University
  • Thomas Gregor

    • Princeton University