scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases.

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis.

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

The ChIP-Hub Resource: Toward plantEncode.

Recent advances in sequencing technologies lead to the generation of an enormous amount of regulome and epigenome data in a variety of plant species. However, a comprehensive standardized resource is so far not available. In this chapter, we present ChIP-Hub, an integrative platform that has been developed based on the ENCODE standards by collecting and reanalyzing regulatory genomic datasets from 41 plant species. The ChIP-hub website is introduced in this chapter, including information on detailed steps of searching, data download, and online analyses, which facilitates users to explore ChIP-Hub. We also provide a cross-species comparison of chromatin accessibility information that gives a thorough view of evolutionary regulatory networks in plants.

Exploring long non-coding RNA networks from single cell omics data.

Single-cell omics technologies provide an unprecedented opportunity to decipher molecular mechanisms underlying various biological processes in a cellular heterogeneity manner. The emergence of such techniques promotes the exploration of lncRNAs, which are known to be tissue- and cell-specific noncoding transcripts involving the regulation of multiple important cellular processes. In this review, we introduce the advancement of lncRNA studies which benefit from single-cell omics data analysis. We discuss the expression heterogeneity of lncRNAs, their cell-type specificity and associated gene regulatory networks (GRNs) from a single-cell perspective. We also summarized the state-of-the-art single-cell omics resources and tools for the construction of single-cell GRNs (scGRNs) that could be potentially used for lncRNA functional study. Finally, we highlight the challenges and prospective for scGRN exploration in lncRNA biology.

The First Complete Mitochondrial Genomes of Two Sibling Species from Nitidulid Beetles Pests.

Carpophilus dimidiatus (Fabricius, 1792) and Carpophilus pilosellus Motschulsky are two sibling species and economically important storage pests worldwide. The first complete mitochondrial (mt) genomes of both were sequenced using next-generation sequencing. The mt genomes of C. dimidiatus and C. pilosellus are circular, with total lengths of 15,717 bp and 15,686 bp, respectively. Gene order and content for both species are similar to what has been observed in ancestral insects and consist of 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and a control region. Comparing the mt genome data of C. dimidiatus and C. pilosellus, they are similar in organization, arrangement patterns, GC contents, transfer RNA (tRNA) secondary structures, and codon usage patterns. Small differences were noted with regards to the nucleotide similarity of coding regions and the control region. This is the first publication of the complete mitochondrial genomes of two sibling species. The mt genome sequences can supplement the nuclear markers of the Carpophilus genus in research species identification, system evolution, and population genetic structure, and also will be valuable molecular marker for further genetic studies.