A recent Arabidopsis Research Roundup included a paper from Adrienne Roeder’s lab in Cornell that includes James Locke and Henrik Jonsson from SLCU. The research focuses on the Arabidopsis sepal, which has been the central theme of the Roeder lab since it was set up a few years ago. On a personal level I recall seeing a talk on this topic maybe 10 years ago and it’s always struck me as a fantastically simplistic system that can be used to answer some fundamental questions about the processes that control cell patterning.
This latest paper is focused on the important question of how adjacent cells are set on different development paths, using the giant sepal cells as an excellent model system. This type of cell type specificity is thought to develop following mild stochastic fluctuations in gene expression that lead into feedback loops that accentuate these initial differences. However this has not yet been visualized in vivo until this new manuscript in Elife
The sepal is the outermost organ of the Arabidopsis flower and its correct shape relies on the formation of giant epidermal cells that can grow up to 20% of the entire organ length. These are necessary for the correct function of the organ (to facilitate flower opening) and they form in approximately equal numbers to non-giant cells. Prior to this paper the mechanism of this patterning remained opaque as giant cells can form either adjacent to or apart from each other. The ATML1 transcription factor plays an important role in general Arabidopsis epidermal patterning and has been shown to be required for the generation of giant sepal cells. Importantly the increased size of these giant cells is facilitated by rounds of endoreduplication that can result in 64C nuclei.
ATML1 is expressed in all sepal cells yet only a subset of these will become giant. By using ATML1-overexpression lines together with a simple genetic analysis, the authors show that gene dosage of the ATML1 gene determines the number of giant cells that form (constitutive ATML1 expression have all giant cells). The mechanism by which this dosage results in a mixed cell fate was unclear until they found (using a line containing a fluorescent ATML1-Citrine protein) that ATML1 expression fluctuated far more in the sepal cells than did other genes expressed in the same tissue.
The authors used some fantastic live imaging to show that there are high levels of ATML1 expression in cells destined for giant fate. Although this was not an absolute relationship (as some smaller cells also showed high ATML1 expression), they mathematically demonstrate that obtaining a high threshold of ATML1 correlated about 70% of the time with uptake of giant cell fate.
Finer detail was added to this picture when it became clear that obtaining this threshold at a particular phase of the cell cycle was much more strongly correlated with giant-cell fate. If this threshold is obtained when DNA content was 4C (occurring after DNA replication in G2 phase of the cell cycle) then in 80% of the time the cell became giant. As the authors state ‘a cell is competent to respond to high levels of ATML1 mainly during G2 to induce giant cell formation’.
Finally the authors used plants with a mutation in the LGO gene (LOSS OF GIANT CELLS FROM ORGANS), which have sepals with no giant cells, to determine whether there was feedback control of ATML1 once giant cell fate had been determined. The lgo mutant is epistatic to atml1 and consistent with this observation they show that ATML1 fluctuates normally in the lgo mutant but that this signal does not lead to endoreduplication and giant cell formation. Therefore there is no feedback loop that features endoreduplication and ATML1; rather there is a linear mechanism in which ATML1 fluctuations set in motion endoreduplication, which then continues independent of those ongoing fluctuations.
This data was then used to develop a model that could precisely predict the location of giant cell formation based on this information about rapid yet relatively small fluctuations in ATML1 levels.
Overall this study is an outstanding example of using technological advances in live imaging in a simple experimental system to help develop an understanding of a complex regulatory system. It remains to be seen whether this type of threshold-fluctuation model is important for patterning in other tissues. However this case is an scientific tour of force, demonstrating what is possible when technical advances are put together with careful measurements and inspired experimental planning!