The rapid advancement of single-cell technologies has significantly enhanced our ability to investigate cellular heterogeneity within plant tissues. However, deciphering these intricate cellular landscapes requires processing high-dimensional gene expression matrices and integrating diverse datasets to enable accurate marker selection, cell identification, and other complex computational operations. These processes typically require broad programming expertise, posing a challenge for researchers with a limited computational background. To address this, we present integrated Plant single-cell Database (iPscDB), an integrated and multifunctional platform that facilitates the integration and analysis of plant single-cell data. iPscDB combines 4 688 428 cells and 288 139 curated cell markers derived from 946 experiments across 38 plant species. Wherever raw data were available, datasets were reprocessed through a single uniform pipeline, and both integration quality and automated cell-type annotation were benchmarked quantitatively. The platform introduces a Marker Confidence Level scheme that grades each cell-type marker by the strength and independence of its supporting evidence (from manually curated classic markers to database-derived associations), allowing users to judge marker reliability directly. The platform also provides a user-friendly online analysis pipeline and modules capable of processing raw FASTQ files or Cell Ranger-processed files. Users can configure parameters via an intuitive interface and utilize an integrated image editor to customize visualization outputs. Additionally, iPscDB supports various analyses, including cross-species gene expression, electronic Single-Cell Pictograph, and developmental trajectory. By streamlining the complex workflows of single-cell transcriptomics, iPscDB offers a practical and accessible resource for researchers with diverse technical backgrounds. iPscDB is accessible at https://www.tobaccodb.org/ipscdb/homePage.
Peng Lu, Jingjing Jin, Jie-Meng Tao et al.· Nucleic Acids Research· 0 citations
Abstract Tobacco (Nicotiana tabacum L.) is a major economic crop and a model for plant–pathogen interactions, yet the spatiotemporal dynamics of defense metabolism during infection remain poorly characterized. Here, we used MALDI-MSI-based spatial metabolomics to systematically profile tobacco leaves during Pseudomonas syringae infection. Multidimensional analysis of 1,399 annotated metabolites revealed distinct spatiotemporal regulation patterns. Temporally, early infection (12 h postinfection (hpi)) was characterized by increased organic acids and terpenoids, followed by a mid-stage shift toward phenolic acids and quinones (24 hpi) and a late-stage enrichment of alkaloids by 60 hpi. Spatially, constrained clustering produced anatomy-aligned segmentation maps and revealed cell type preferences across epidermal, mesophyll, and vascular regions, with directional redistribution of differentially expressed metabolites as infection progressed. Defense hormones, including salicylic acid (SA) and jasmonic acid (JA), preferentially accumulated in vascular bundles and varied dynamically over time. Functional validation through exogenous application of representative metabolites (eg calystegine C1 and L-phenylalanine) and hormones (JA and SA), together with genetic manipulation of JA-biosynthetic genes, confirmed their roles in reducing lesion development and suppressing bacterial proliferation. Notably, epidermal enrichment of alkaloids—especially nicotine—and amino acid derivatives showed a decrease-then-increase pattern consistent with early consumption and later replenishment; nicotine's defensive contribution was further supported using a low-nicotine mutant. Collectively, P. syringae infection orchestrates a coordinated, cell type-compartmentalized defense metabolic program in tobacco, providing a resource for mechanistic studies and metabolic engineering of disease resistance. This time- and tissue-resolved atlas links metabolite remodeling to hormone-associated signaling and chemical barrier formation during wildfire disease progression.
Xin-Hua Tian, Ze-Chao Qu, Jia-Qi Wang et al.· Plant Physiology· 0 citations
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