The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure.

Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high-throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non-redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image-based tool to dissect guard cell responses and outline a genetic framework of stomatal closure.

Incorporating prior knowledge improves detection of differences in bacterial growth rate.

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate.