*Result*: PscOA: a plant scRNA-seq marker gene database for enhanced cellular transcriptome understanding.

Title:
PscOA: a plant scRNA-seq marker gene database for enhanced cellular transcriptome understanding.
Authors:
Xu H; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China., Li B; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China.; The Key Laboratory of Plant Development and Environmental Adaptation Biology, Ministry of Education, School of Life Sciences, Shandong University, Qingdao 266237, China., Zhang X; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China., Li H; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China., Song K; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China., Rajendran M; Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China., Cao H; Henan Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450002, China., Yang L; Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China.
Source:
Plant & cell physiology [Plant Cell Physiol] 2026 Feb 27; Vol. 67 (2), pp. 213-223.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: Japan NLM ID: 9430925 Publication Model: Print Cited Medium: Internet ISSN: 1471-9053 (Electronic) Linking ISSN: 00320781 NLM ISO Abbreviation: Plant Cell Physiol Subsets: MEDLINE
Imprint Name(s):
Publication: Tokyo : Oxford University Press
Original Publication: Kyoto, Japan : Japanese Society of Plant Physiologists
MeSH Terms:
Transcriptome*/genetics , Single-Cell Analysis*/methods , Databases, Genetic*, Arabidopsis/genetics ; Nicotiana/genetics ; Sequence Analysis, RNA/methods ; Populus/genetics ; Genetic Markers/genetics ; Genes, Plant/genetics ; Gene Expression Profiling ; Molecular Sequence Annotation ; Gene Expression Regulation, Plant ; Single-Cell Gene Expression Analysis
Grant Information:
SDAIT-25-01 Foundation of Shandong Province Modern Agricultural Technology System Innovation Team
Contributed Indexing:
Keywords: cellular transcriptome; marker gene database; plant; scRNA-seq; stress
Substance Nomenclature:
0 (Genetic Markers)
Entry Date(s):
Date Created: 20251117 Date Completed: 20260307 Latest Revision: 20260307
Update Code:
20260308
DOI:
10.1093/pcp/pcaf151
PMID:
41247320
Database:
MEDLINE

*Further Information*

*Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptome analysis, enabling the study of cellular heterogeneity beyond bulk transcriptomics. However, plant science lags behind animal science in this field largely due to limited marker genes. In this study, we developed Plant single-cell Original Analyses (PscOA) (http://sdau.biodb.com.cn/pscoa/), a plant scRNA-seq marker gene database with 39 347 marker genes. PscOA integrates Basic Local Alignment Search Tool (BLAST) for homology-based marker gene mining, and A Cell Type Annotation Tool for Single-Cell RNA-seq Data (SCSA) for cell type annotation, complemented by visualization tools. Case studies in Arabidopsis thaliana and Populus alba L. demonstrate the potential of cell type annotation in PscOA. Leveraging A. thaliana marker genes from PscOA, we predict 258 potential markers in Nicotiana tabacum, showcasing its marker gene discovery potential for specific species. Differential expression analysis under stress reveals common and diverse strategies at the single-cell level, offering insights into plant cell type diversity and function. Altogether, PscOA serves as a valuable repository for original scRNA-seq analysis in plant science, deepening our understanding of the plant cellular transcriptome.
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