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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. Based on massive tissue-specific RNA-seq data sets (10 553) found in The Cancer Genome Atlas (TCGA), 538 feature genes (biomarkers) were selected based on their gene expression ranks and used to predict tissue types. The top scoring pairs (TSPs) classifier of the tumor type was optimized by the TCGA training samples. To test the prediction accuracy of our TOD-CUP algorithm, we analyzed (1) two microarray data sets (1029 Agilent and 2277 Affymetrix/Illumina chips) and found 91% and 94% prediction accuracy, respectively, (2) RNA-seq data from five cancer types derived from 141 public metastatic cancer tumor samples and achieved 94% accuracy and (3) a total of 25 clinical cancer samples (including 14 metastatic cancer samples) were able to classify 24/25 samples correctly (96.0% accuracy). Taken together, the TOD-CUP algorithm provides a powerful and robust means to accurately identify the tissue origin of 24 cancer types across different data platforms. To make the TOD-CUP algorithm easily accessible for clinical application, we established a Web-based server for tumor tissue origin diagnosis (http://ibi. zju.edu.cn/todcup/)., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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. Here, we retrieved a diverse set of omics experimental results from 159 publications, including genome-wide association study, differentially expressed genes and differential methylation regions, and 2405 classified OA-related gene markers. Meanwhile, based on recent single-cell RNA-seq data from different joints, 5459 cell-type gene markers of joints were collected. The information has been integrated into an online database named OA o mics and molecular b iomarkers (OAOB). The database (http://ibi.zju.edu.cn/oaobdb/) provides a web server for OA marker genes, omics features and so on. To our knowledge, this is the first database of molecular biomarkers for OA. [ABSTRACT FROM AUTHOR]

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. Batch effects and different data platforms greatly decrease the predictive performance in inter-laboratory and different data type validation. Results Here, we present a new ensemble learning method named as 'scDetect' that combines gene expression rank-based analysis and a majority vote ensemble machine-learning probability-based prediction method capable of highly accurate classification of cells based on scRNA-seq data by different sequencing platforms. Because of tumor heterogeneity, in order to accurately predict tumor cells in the single-cell RNA-seq data, we have also incorporated cell copy number variation consensus clustering and epithelial score in the classification. We applied scDetect to scRNA-seq data from pancreatic tissue, mononuclear cells and tumor biopsies cells and show that scDetect classified individual cells with high accuracy and better than other publicly available tools. Availability and implementation scDetect is an open source software. Source code and test data is freely available from Github (https://github.com/IVDgenomicslab/scDetect/) and Zenodo (https://zenodo.org/record/4764132#.YKCOlrH5AYN). The examples and tutorial page is at https://ivdgenomicslab.github.io/scDetect-Introduction/. And scDetect will be available from Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

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. We have developed a bioinformatics pipeline (ePmiRNA_finder) that provides strident miRNA classification and applied it to analyze 421 small RNA sequencing data sets from 10 types of human body fluids and tissues and comparative samples from carnivores and herbivores. A total of 35 miRNAs were identified that map to plants typically found in the human diet and these miRNAs were found in at least one human blood sample and their abundance was significantly different when compared to samples from human microbiome or cow. The plant-derived miRNA profiles were body fluid/tissue-specific and highly abundant in the brain and the breast milk samples, indicating selective absorption and/or the ability to be transported across tissue/organ barriers. Our data provide conclusive evidence for the presence of plant-derived miRNAs as a consequence of dietary intake and their cross-kingdom regulatory function within human circulating system. [ABSTRACT FROM AUTHOR]

In this study, we characterized the miR482 family in cotton using existing small RNA datasets and the recently released draft genome sequence of Gossypium raimondii, a diploid cotton species whose progenitor is the putative contributor of the Dt (representing the D genome of tetraploid) genome of the cultivated tetraploid cotton species G. hirsutum and G. barbadense. Of the three ghr-miR482 members reported in G. hirsutum, ghr-miR482a has no homolog in G. raimondii, ghr-miR482b and ghr-miR482c each has a single homolog in G. raimondii. Gra-miR482d has five homologous loci (gra-miR482d, f-i) in G. raimondii and also exists in G. hirsutum (ghr-miR482d). A variant, miR482.2 that is a homolog of miR2118 in other species, is produced from several GHR-MIR482 loci in G. hirsutum. Approximately 12% of the G. raimondii NBS-LRR genes were predicted targets of various members of the gra-miR482 family. Based on the rationale that the regulatory relationship between miR482 and NBS-LRR genes will be conserved in G. raimondii and G. hirsutum, we investigated this relationship using G. hirsutum miR482 and G. raimondii NBS-LRR genes, which are not currently available in G. hirsutum. Ghr-miR482/miR482.2-mediated cleavage was confirmed for three of the four NBS-LRR genes analysed. As in tomato, miR482-mediated cleavage of NBS-LRR genes triggered production of phased secondary small RNAs in cotton. In seedlings of the susceptible cultivar Sicot71 (G. hirsutum) infected with the fungal pathogen Verticillium dahliae, the expression levels of ghr-miR482b/miR482b.2, ghr-miR482c and ghr-miR482d.2 were down-regulated, and several NBS-LRR targets of ghr-miR482c and ghr-miR482d were up-regulated. These results imply that, like tomato plants infected with viruses or bacteria, cotton plants are able to induce expression of NBS-LRR defence genes by suppression of the miRNA-mediated gene silencing pathway upon fungal pathogen attack. [ABSTRACT FROM AUTHOR]

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. The spatial transcriptomes of 6, 24, 36, and 48 h after imbibition unveiled both known and previously unreported embryo cell types, including two unreported scutellum cell types, corroborated by in situ hybridization and functional exploration of marker genes. Temporal transcriptomic profiling delineated gene expression dynamics in distinct embryonic cell types during seed germination, highlighting key genes involved in nutrient metabolism, biosynthesis, and signaling of phytohormones, reprogrammed in a cell-type-specific manner. Our study provides a detailed spatiotemporal transcriptome of rice embryo and presents a previously undescribed methodology for exploring the roles of different embryonic cells in seed germination. [Display omitted] • Establish a rice embryo germination spatiotemporal transcriptomic census • Develop AI-based plant cell wall segmentation for precise cell-type identification • Identify scutellum cell subtypes and cell-type-specific gene expression pattern • OsMFT2 specifically expressed in SCL2, with decreasing trends during seed germination Yao et al. establish the spatiotemporal transcriptomic cell census of rice seed embryo during germination. Employing cell bin and segmentation, they identify transcriptional disparities among embryo cells and delineate scutellum parenchyma cell developmental trajectory, as well as dynamics of pathways related to cell, hormones, and nutrition metabolism during germination. [ABSTRACT FROM AUTHOR]