Streamlined spatial and environmental expression signatures characterize the minimalist duckweed Wolffia australiana .
Tom DenyerPin-Jou WuKelly ColtBradley W AbramsonZhili PangPavel SolanskyAllen MamertoTatsuya NoboriJoseph R EckerEric LamTodd P MichaelMarja C P TimmermansPublished in: Genome research (2024)
Single-cell genomics permits a new resolution in the examination of molecular and cellular dynamics, allowing global, parallel assessments of cell types and cellular behaviors through development and in response to environmental circumstances, such as interaction with water and the light-dark cycle of the Earth. Here, we leverage the smallest, and possibly most structurally reduced, plant, the semiaquatic Wolffia australiana , to understand dynamics of cell expression in these contexts at the whole-plant level. We examined single-cell-resolution RNA-sequencing data and found Wolffia cells divide into four principal clusters representing the above- and below-water-situated parenchyma and epidermis. Although these tissues share transcriptomic similarity with model plants, they display distinct adaptations that Wolffia has made for the aquatic environment. Within this broad classification, discrete subspecializations are evident, with select cells showing unique transcriptomic signatures associated with developmental maturation and specialized physiologies. Assessing this simplified biological system temporally at two key time-of-day (TOD) transitions, we identify additional TOD-responsive genes previously overlooked in whole-plant transcriptomic approaches and demonstrate that the core circadian clock machinery and its downstream responses can vary in cell-specific manners, even in this simplified system. Distinctions between cell types and their responses to submergence and/or TOD are driven by expression changes of unexpectedly few genes, characterizing Wolffia as a highly streamlined organism with the majority of genes dedicated to fundamental cellular processes. Wolffia provides a unique opportunity to apply reductionist biology to elucidate signaling functions at the organismal level, for which this work provides a powerful resource.
Keyphrases
- single cell
- rna seq
- high throughput
- poor prognosis
- genome wide
- induced apoptosis
- cell therapy
- machine learning
- risk assessment
- gene expression
- deep learning
- signaling pathway
- mesenchymal stem cells
- cell cycle arrest
- binding protein
- high intensity
- oxidative stress
- bioinformatics analysis
- climate change
- bone marrow
- genome wide identification