Throughout plant evolution the circadian clock has expanded into a complex signaling network, coordinating physiological and metabolic processes with the environment. Early land plants faced new environmental pressures that required energy-demanding stress responses. Integrating abiotic stress response into the circadian system provides control over daily energy expenditure. Here, we describe the evolution of the circadian clock in plants and the limited, yet compelling evidence for conserved regulation of abiotic stress. The need to introduce abiotic stress tolerance into current crops has expanded research into wild accessions and revealed extensive variation in circadian clock parameters across monocot and eudicot species. We argue that research into the ancestral links between the clock and abiotic stress will benefit crop improvement efforts.

Motivation Clustering spatial-resolved gene expressions is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this paper, we present a Convolutional Neural Network (CNN) regularized by the graph of Protein-Protein Interaction (PPI) network to cluster spatially-resolved gene expressions. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization. Results In the experiments, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10x Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the-state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights. Availability Source code is available at https://github.com/kuanglab/CNN-PReg.

Background Non-invasive reporter systems are powerful tools to query physiological and transcriptional responses in organisms. For example, fluorescent and bioluminescent reporters have revolutionized cellular and organismal assays and have been used to study plant responses to abiotic and biotic stressors. Integrated, cooled charge-coupled device (CCD) camera systems have been developed to image bioluminescent and fluorescent signals in a variety of organisms; however, these integrated long-term imaging systems are expensive. Results We have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions. This system combines environmental growth chambers with high-sensitivity CCD cameras, multi-wavelength LEDs, open-source software, and several options for coordinating lights with imaging. This easy-to-assemble system can be used for short and long-term imaging of bioluminescent reporters, acute light-response, circadian rhythms, delayed fluorescence, and fluorescent-protein-based assays in vivo. Conclusions We have developed two self-assembled imaging systems that will be useful to researchers interested in continuously monitoring in vivo reporter systems in various plant species.