bioRxivPlant BiologyPreprint
Tissue Context Shapes the Circadian Transcriptome of the Arabidopsis Leaf
Alveena ZulfiqariD, Zachary MyersiD, Danielle SchoeneckeriD, Ananda MenoniD, Kathleen GreenhamiD
Abstract
Circadian regulation enables plants to coordinate cellular processes with daily environmental cycles, yet how clocks operate across cell and tissue types remains poorly understood. To resolve circadian regulation in the mature Arabidopsis thaliana leaf, we generated a 24-hour single nucleus RNA-sequencing (snRNA-seq) circadian time course spanning seven circadian time points and ~30,000 nuclei. We recovered all major leaf cell types and identified >7,400 genes with cluster-resolved rhythmic expression. Coexpression analyses defined five major temporal expression clades, revealing cell-type-specific phase shifts, while cluster-specific gene regulatory networks identified shared and unique transcription factor targets. These analyses suggest that core clock components broadly coordinate processes such as photosynthesis and light signaling while fine-tuning hormone, redox, and stress pathways in cell-type-specific manners. To restore the spatial information lost during nuclei isolation, we designed a 100-gene Xenium spatial transcriptomics panel and profiled intact mature leaves at relative dawn and dusk. Spatial transcriptomics validated cell-type-associated expression patterns from the single-nucleus time course and further revealed spatially restricted domains that were not apparent from cluster averages alone, including vascular subdomains and guard cell-associated differences in clock transcript accumulation. Several core clock components had unequal spatial distribution across similar cell types, suggesting potential fine-tuning of the clock in discrete spatial regions. Together, these findings show that the circadian transcriptional programs in the mature leaf are not simply cell-type-specific but spatially organized within the architecture of the organ.
- organism: Arabidopsis
- assay: snRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 3
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