The lab seeks to understand the relationship between molecular and cellular complexity using multicellular plants as a model system of study.
Seeds provide a tractable system to investigate signal integration and decision-making in plants (Bassel, Trends in Plant Science 2016). When a seed is shed from its mother, it is endowed with a set of instructions as to when to terminate dormancy and commence germination. Environmental information is perceived and integrated by the seed leading to a single binary decision to terminate dormancy and commence germination. We are interested in understanding the molecular components and interactions which underlying this decision-making process.
Using publicly available microarray data we performed a genome-wide gene correlation (co-expression) analysis to uncover the global co-regulation of transcripts underlying the transition from seed dormancy to germination (Bassel et al. 2014 PNAS). This led to the identification of 11 previously uncharacterized genetic regulators of this decision-making process, and interactions between them. We also identified conservation in the molecular components that mediate the decision to terminate dormancy also regulate the decision to flower, the other key life decision plants are faced with making in response to the environment.
In a second approach we used the rule-based machine learning algorithm BioHEL in collaboration with Jaume Bacardit to functionally associate genes based on their collective ability to predict the developmental outcome of dormancy or germination (Bassel et al. 2011 Plant Cell). The resulting network model was used to uncover 4 previously uncharacterized genetic regulators of the seed decision-making process.
Whole mount 3D imaging of plant organs using confocal microscopy (Truernit et al. 2008; Bassel et al. 2014 PNAS) enables the shape of each and every cell to be digitally captured. The image analysis software MorphoGraphX developed by collaborator Richard Smith (Barbier de Reuille et al. 2015 eLife) can describe the surfaces of all cells in discrete terms using polygonal meshes. This facilitates the quantitative analysis of changes in cell shape.
Like gene expression data, 3D cellular resolution datasets require annotation to identify cells types and positions. We have developed a geometry and topology-based method to annotate the cell types and positions within radially symmetric plant organs (Montenegro-Johnson et al. 2015 Plant Cell).
Computational analysis of 3D cellular resolution datasets enables the global connectivity of cells within entire organs to be identified. These can be used to enhance the accuracy with which the annotation of cells is performed. They also represent a way to quantify cellular patterning in plants and provide a means to analyse their properties.
A role for cellular complexity in plant organ growth has also been uncovered. Using cellular resolution 3D mechanical models of plant organs (FEM), a role for cell size, shape and arrangement has been uncovered (Bassel et al. 2014 PNAS).