Patterned proliferation orients tissue-wide stress to control root vascular symmetry in Arabidopsis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

The transient gene expression system using Arabidopsis mesophyll protoplasts has proven an important and versatile tool for conducting cell-based experiments using molecular, cellular, biochemical, genetic, genomic and proteomic approaches to analyze the functions of diverse signaling pathways and cellular machineries. A well-established protocol that has been extensively tested and applied in numerous experiments is presented here. The method includes protoplast isolation, PEG-calcium transfection of plasmid DNA and protoplast culture. Physiological responses and high-throughput capability enable facile and cost-effective explorations as well as hypothesis-driven tests. The protoplast isolation and DNA transfection procedures take 6-8 h, and the results can be obtained in 2-24 h. The cell system offers reliable guidelines for further comprehensive analysis of complex regulatory mechanisms in whole-plant physiology, immunity, growth and development.

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.