The lab seeks to understand how complexity across scales shapes and constrains biological systems, namely:

1) Molecular complexity – how interactions between molecular components give rise to cell behaviours.

2) Cellular complexity – how interactions between cells influence organ function.

Molecular complexity

Seeds provide a useful experimental system to study signal integration, decision-making and information processing in plants (Bassel, Trends in Plant Science 2016). When a seed is shed from its mother, it is given a set of instructions telling it under which conditions it should start to germinate. Signals from the environment are perceived and processed by molecular networks within a seed in order to time this decision to terminate dormancy. Uncovering the molecular circuitry that underpins this decision-making process is our goal.

Using publicly available microarray data we previously 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. 2011 PNAS). This led to the identification of 11 previously uncharacterized genetic regulators of this decision-making process, and interactions between them. We also identified molecular components that both mediate the decision to terminate dormancy and regulate the decision to flower. These represent two of the most important decisions plants make during their life cycle.

Cellular complexity

We next investigated where the molecules that help a seed decide when to germinate are located in a dormant seed. Using 3D digital single cell analysis, we found these components to be enriched within a small group of cells. This “decision-making centre” is located within the cells of the embryonic root (radicle, red on image below) in dormant Arabidopsis seeds (Topham et al. 2017). A spatial separation between cells that act to each promote and inihibit seed dormancy within this subgroup of cells was shown to be important in processing alternating temperatures to promote seed germination. This provides an example of complex information processing in plants.

These analyses are aided by whole mount 3D imaging of plant organs using confocal microscopy (Truernit et al. 2008; Bassel et al. 2014 PNAS). The shape of each and every cell can be digitally captured. The image analysis software MorphoGraphX (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 of3D cellular geometry and organization.

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.

The topological analysis of cellular connectivity networks using network science also provides a way to understand how the arrangement of cells impacts what organs do, bridging structure and function in organ design. Our analysis of the Arabidopsis hypocotyl identified a division of labour between different cell types of the epidermis of this organ.

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 on grwoth has been uncovered (Bassel et al. 2014 PNAS).