Danny Y. T. Liu

Cell-to-cell transport via the lumen of the endoplasmic reticulum

Plasmodesmata are plasma membrane-lined channels through which cytoplasmic molecules move from cell-to-cell in plants. Most plasmodesmata contain a desmotubule, a central tube of endoplasmic reticulum (ER), that connects the ER of adjacent cells. Here we demonstrate that molecules of up to 10.4 kDa in size can move between the ER lumen of neighbouring leaf trichome or epidermal cells via the desmotubule lumen. Fluorescent molecules of up to 10 kDa, microinjected into the ER of Nicotiana trichome cells, consistently moved into the ER and nuclei of neighbouring trichome cells. This movement occurred more rapidly than movement via the cytoplasmic pathway. A fluorescent 3-kDa dextran microinjected into the ER of a basal trichome cell moved into the ER and nuclei of epidermal cells across a barrier to cytoplasmic movement. We constructed a 10.4-kDa recombinant ER-lumenal reporter protein (LRP) from a fragment of the endogenous ER-lumenal binding protein AtBIP1. Following transient expression of the LRP in the ER of Tradescantia leaf epidermal cells, it often moved into the nuclear envelopes of neighbouring cells. However, green fluorescent protein targeted to the ER lumen (ER-GFP) did not move from cell to cell. We propose that the ER lumen of plant cells is continuous with that of their neighbours, and allows movement of small ER-lumenal molecules between cells.

Reflection across plant cell boundaries in confocal laser scanning microscopy

The fluorescence patterns of proteins tagged with the green fluorescent protein (GFP) and its derivatives are routinely used in conjunction with confocal laser scanning microscopy to identify their sub‐cellular localization in plant cells. GFP‐tagged proteins localized to plasmodesmata, the intercellular junctions of plants, are often identified by single or paired punctate labelling across the cell wall. The observation of paired puncta, or ‘doublets’, across cell boundaries in tissues that have been transformed through biolistic bombardment is unexpected if there is no intercellular movement of the GFP‐tagged protein, since bombardment usually leads to the transformation of single, isolated cells. We expressed a putative plasmodesmal protein tagged with GFP by bombarding Allium porrum epidermal cells and assessed the nature of the doublets observed at the cell boundaries. Doublets were formed when fluorescent spots were abutting a cell boundary and were only observable at certain focal planes. Fluorescence emitted from the half of a doublet lying outside the transformed cells was polarized. Optical simulations performed using finite‐difference time‐domain computations showed a dramatic distortion of the confocal microscopes point spread function when imaging voxels close to the plant cell wall due to refractive index differences between the wall and the cytosol. Consequently, axially and radially out‐of‐focus light could be detected. A model of this phenomenon suggests how a doublet may form when imaging only a single real fluorescent body in the vicinity of a plant cell wall using confocal microscopy. We suggest, therefore, that the appearance of doublets across cell boundaries is insufficient evidence for plasmodesmal localization due to the effects of the cell wall on the reflection and scattering of light.

Plasmodesmata: New Perspectives on Old Questions

The progress so far and challenges remaining in developing a functional model of the macromolecular architecture of plasmodesmata are discussed. Details of the macromolecular components identified within plasmodesmata are summarised. Electron tomography and correlative microscopy techniques are explored as potential avenues to overcome the challenges in developing an accurate three-dimensional model of the macromolecular architecture of plasmodesmata. In recent years, some areas of plasmodesmatal biology have been left ignored, largely because the technologies required to advance them have been considered too difficult. For example, there have been no electrophysiological studies of plasmodesmata in the last decade and consequently no advances in our understanding of the rapid regulation of the permeability of plasmodesmata. There has also been no advance on the question of heterogeneity of function of the plasmodesmata within a wall and potential avenues to address this question are considered.

Characterisation of Arabidopsis calnexin 1 and calnexin 2 in the endoplasmic reticulum and at plasmodesmata

Calnexin (CNX) is a highly conserved endoplasmic reticulum (ER) chaperone protein. Both calnexin and the homologous ER-lumenal protein, calreticulin, bind calcium ions and participate in protein folding. There are two calnexins in Arabidopsis thaliana, CNX1 and CNX2. GUS expression demonstrated that these are expressed in most Arabidopsis tissues throughout development. Calnexin transfer DNA (T-DNA) mutant lines exhibited increased transcript abundances of a number of other ER chaperones, including calreticulins, suggesting a degree of redundancy. CNX1 and CNX2 localised to the ER membrane including that within plasmodesmata, the intercellular channels connecting plant cells. This is comparable with the previous localisations of calreticulin in the ER lumen and at plasmodesmata. However, from green fluorescent protein (GFP) diffusion studies in single and double T-DNA insertion mutant lines, as well as overexpression lines, we found no evidence that CNX1 or CNX2 play a role in intercellular transport through plasmodesmata. In addition, calnexin T-DNA mutant lines showed no change in transcript abundance of a number of plasmodesmata-related proteins. CNX1 and CNX2 do not appear to have a specific localisation or function at plasmodesmata—rather the association of calnexin with the ER is simply maintained as the ER passes through plasmodesmata.

Data-Driven Personalization of Student Learning Support in Higher Education

Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data—one that puts the relationship between teacher and student at the center. Our approach means that teachers’ pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.

Knowledge Acquisition for Learning Analytics: Comparing Teacher-Derived, Algorithm-Derived, and Hybrid Models in the Moodle Engagement Analytics Plugin

One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher- and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model- and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.