Identification of cell-type specificity, trans- and cis-acting functions of plant lincRNAs from single-cell transcriptomes.

Long noncoding RNAs, including intergenic lncRNAs (lincRNAs), play a key role in various biological processes throughout the plant life cycle, and the advent of single-cell RNA sequencing (scRNA-seq) technology has opened up a valuable avenue for scrutinizing the intricate roles of lincRNAs in cellular processes. Here, we identified a new batch of lincRNAs using scRNA-seq data from diverse tissues of plants (rice, Arabidopsis, tomato, and maize). Based on well-annotated single-cell transcriptome atlases, plant lincRNAs were found to possess the same level of cell-type specificity as mRNAs and to be involved in the differentiation of certain cell types based on pseudo-time analysis.

Single-cell landscape of long and short glandular trichomes in leaves.

Glandular trichomes (GTs) play a crucial role in plant defenses and the synthesis of secondary metabolites. Understanding the developmental trajectory of GTs is essential for unraveling their functional significance and potential applications. Here we established a comprehensive single-cell atlas of leaves, a model plant for GT studies.

Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germination.

Characterizing cellular features during seed germination is crucial for understanding the complex biological functions of different embryonic cells in regulating seed vigor and seedling establishment. We performed spatially enhanced resolution omics sequencing (Stereo-seq) and single-cell RNA sequencing (scRNA-seq) to capture spatially resolved single-cell transcriptomes of germinating rice embryos. An automated cell-segmentation model, employing deep learning, was developed to accommodate the analysis requirements.

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC.

Multi-omics molecular biomarkers and database of osteoarthritis.

Osteoarthritis (OA) is the most common form of arthritis in the adult population and is a leading cause of disability. OA-related genetic loci may play an important role in clinical diagnosis and disease progression. With the rapid development of diverse technologies and omics methods, many OA-related public data sets have been accumulated.

The whole chloroplast genome of subsp a wild and perennial kinship to cultivated rye.

subsp. is a perennial, hermaphrodite wild rye species and a progenitor of the modern cultivated rye, . With high adaptive capacity in stress conditions, it is valuable for enriching the germplasm resources of rye.

Effects of Sample Size on Plant Single-Cell RNA Profiling.

Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e.

Recent origination of circular RNAs in plants.

Circular RNA (circRNA) is a kind of new regulatory RNA with diverse biological functions. Numerous circRNAs have been identified in many plant species; however, evolution of plant circRNAs remains largely unknown. In this study, we assembled full-length sequences of 6519 rice (Oryza sativa) circRNAs and analyzed their conservation in another 46 plant species based on comparison of sequences and expression patterns.

Large-scale identification of extracellular plant miRNAs in mammals implicates their dietary intake.

Extracellular microRNAs (miRNAs) have been proposed to function in cross-kingdom gene regulation. Among these, plant-derived miRNAs of dietary origin have been reported to survive the harsh conditions of the human digestive system, enter the circulatory system, and regulate gene expression and metabolic function. However, definitive evidence supporting the presence of plant-derived miRNAs of dietary origin in mammals has been difficult to obtain due to limited sample sizes.

Signaling pathway perturbation analysis for assessment of biological impact of cigarette smoke on lung cells.

Exposure to cigarette smoke (CS) results in injury to the epithelial cells of the human respiratory tract and has been implicated as a causative factor in the development of chronic obstructive pulmonary disease and lung cancers. The application of omics-scale methodologies has improved the capacity to understand cellular signaling processes underlying response to CS exposure. We report here the development of an algorithm based on quantitative assessment of transcriptomic profiles and signaling pathway perturbation analysis (SPPA) of human bronchial epithelial cells (HBEC) exposed to the toxic components present in CS.

scDetect: a rank-based ensemble learning algorithm for cell type identification of single-cell RNA sequencing in cancer.

Motivation: Single-cell RNA sequencing (scRNA-seq) has enabled the characterization of different cell types in many tissues and tumor samples. Cell type identification is essential for single-cell RNA profiling, currently transforming the life sciences. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other scRNA-seq studies.

TOD-CUP: a gene expression rank-based majority vote algorithm for tissue origin diagnosis of cancers of unknown primary.

Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types.

RiceRelativesGD: a genomic database of rice relatives for rice research.

Rice (Oryza sativa L.) is one of the most important crops worldwide. Its relatives, including phylogenetically related species of rice and paddy weeds with a similar ecological niche, can provide crucial genetic resources (such as resistance to biotic and abiotic stresses and high photosynthetic efficiency) for rice research.

Characteristics of plant circular RNAs.

Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs.

The Circular RNA Profiles of Colorectal Tumor Metastatic Cells.

Circular RNAs (circRNAs) have been reported that can be used as biomarkers for colorectal cancers (CRC) and other types of tumors. However, a limited number of studies have been performed investigating the potential role of circRNAs in tumor metastasis. Here, we examined the circRNAs in two CRC cell lines (a primary tumor cell SW480 and its metastasis cell SW620), and found a large set of circRNA (2,919 ncDECs) with significantly differential expression patterns relative to normal cells (NCM460).

Echinochloa crus-galli genome analysis provides insight into its adaptation and invasiveness as a weed.

Barnyardgrass (Echinochloa crus-galli) is a pernicious weed in agricultural fields worldwide. The molecular mechanisms underlying its success in the absence of human intervention are presently unknown. Here we report a draft genome sequence of the hexaploid species E.

Full-length sequence assembly reveals circular RNAs with diverse non-GT/AG splicing signals in rice.

Circular RNAs (circRNAs) have been identified in diverse eukaryotic species and are characterized by RNA backsplicing events. Current available methods for circRNA identification are able to determine the start and end locations of circRNAs in the genome but not their full-length sequences. In this study, we developed a method to assemble the full-length sequences of circRNAs using the backsplicing RNA-Seq reads and their corresponding paired-end reads.