OBJECTIVE: To depict the cell landscape and molecular biological characteristics of human intrauterine adhesion (IUA) so as to better understand its immune microenvironment and provide new inspirations for clinical treatment. METHODS: Four patients with IUA who underwent hysteroscopic treatment at Dongguan Maternal and Child Health Care Hospital from February 2022 to April 2022 were selected as the study subjects. Hysteroscopy was used to collect the tissues of IUA, which were graded based on the patient's medical history, menstrual history and status of IUA. Library construction, sequencing, single cell data comparison and gene expression matrix construction were carried out in strict accordance with the single cell RNA sequencing process. Thereafter, the UMAP dimension reduction analysis of cell population and genetic analysis were carried out based on the cell types. RESULTS: A total of 27 511 cell transcripts were obtained from four moderately graded IUA tissue samples and assigned to six cell lineages including T cells, mononuclear phagocytes, epithelial cells, fibroblasts, endothelial cells and erythrocytes. Compared with normal uterine tissue cells, the four samples showed different cell distribution, and the proportions of mononuclear phagocytes and T cells in sample IUA0202204 were significantly increased, suggesting a strong cellular immune response. CONCLUSION The cell diversity and heterogeneity of moderate IUA tissues have been described. Each cell subgroup has unique molecular characteristics, which may provide new clues for further study of the pathogenesis of IUA and heterogeneity among the patients.
Pregnancy ; Female ; Child ; Humans ; Endothelial Cells ; Uterine Diseases/complications* ; Hysteroscopy/methods* ; Tissue Adhesions/etiology* ; Sequence Analysis, RNA

The recent discovery of circular RNAs (circRNAs) and characterization of their functional roles have opened a new avenue for understanding the biology of genomes. circRNAs have been implicated to play important roles in a variety of biological processes, but their precise functions remain largely elusive. Currently, a few approaches are available for novel circRNA prediction, but almost all these methods are intended for animal genomes. Considering that the major differences between the organization of plant and mammal genomes cannot be neglected, a plant-specific method is needed to enhance the validity of plant circRNA identification. In this study, we present CircPlant, an integrated tool for the exploration of plant circRNAs, potentially acting as competing endogenous RNAs (ceRNAs), and their potential functions. With the incorporation of several unique plant-specific criteria, CircPlant can accurately detect plant circRNAs from high-throughput RNA-seq data. Based on comparison tests on simulated and real RNA-seq datasets from Arabidopsis thaliana and Oryza sativa, we show that CircPlant outperforms all evaluated competing tools in both accuracy and efficiency. CircPlant is freely available at http://bis.zju.edu.cn/circplant.
Arabidopsis/metabolism* ; Oryza/metabolism* ; RNA, Circular/metabolism* ; RNA, Plant/metabolism* ; Sequence Analysis, RNA/methods*

Acute Kidney Injury ; etiology ; Betacoronavirus ; Coronavirus Infections ; complications ; Humans ; Kidney ; enzymology ; Pandemics ; Peptidyl-Dipeptidase A ; physiology ; Pneumonia, Viral ; complications ; Sequence Analysis, RNA ; methods ; Serine Endopeptidases ; physiology ; Single-Cell Analysis ; methods

Age-associated changes in immune cells have been linked to an increased risk for infection. However, a global and detailed characterization of the changes that human circulating immune cells undergo with age is lacking. Here, we combined scRNA-seq, mass cytometry and scATAC-seq to compare immune cell types in peripheral blood collected from young and old subjects and patients with COVID-19. We found that the immune cell landscape was reprogrammed with age and was characterized by T cell polarization from naive and memory cells to effector, cytotoxic, exhausted and regulatory cells, along with increased late natural killer cells, age-associated B cells, inflammatory monocytes and age-associated dendritic cells. In addition, the expression of genes, which were implicated in coronavirus susceptibility, was upregulated in a cell subtype-specific manner with age. Notably, COVID-19 promoted age-induced immune cell polarization and gene expression related to inflammation and cellular senescence. Therefore, these findings suggest that a dysregulated immune system and increased gene expression associated with SARS-CoV-2 susceptibility may at least partially account for COVID-19 vulnerability in the elderly.
Adult ; Aged ; Aged, 80 and over ; Aging ; genetics ; immunology ; Betacoronavirus ; CD4-Positive T-Lymphocytes ; metabolism ; Cell Lineage ; Chromatin Assembly and Disassembly ; Coronavirus Infections ; immunology ; Cytokine Release Syndrome ; etiology ; immunology ; Cytokines ; biosynthesis ; genetics ; Disease Susceptibility ; Flow Cytometry ; methods ; Gene Expression Profiling ; Gene Expression Regulation, Developmental ; Gene Rearrangement ; Humans ; Immune System ; cytology ; growth & development ; immunology ; Immunocompetence ; genetics ; Inflammation ; genetics ; immunology ; Mass Spectrometry ; methods ; Middle Aged ; Pandemics ; Pneumonia, Viral ; immunology ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome ; Young Adult

Adult ; Female ; Genome-Wide Association Study ; Humans ; Leukocytes, Mononuclear ; metabolism ; Lupus Erythematosus, Systemic ; genetics ; Male ; Principal Component Analysis ; Sequence Analysis, RNA ; methods

Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC), a new clustering framework based on random projection and feature construction, for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy, robustness, and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, SSCC achieved 20% improvement for clustering accuracy and 50-fold acceleration, but only consumed 66% memory usage, compared to the widelyused software package SC3. Compared to k-means, the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.
Algorithms ; Animals ; Cluster Analysis ; Computational Biology ; methods ; Databases as Topic ; Gene Expression Profiling ; methods ; Humans ; Mice ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Statistics, Nonparametric

Zika virus (ZIKV) is one of the pathogens which is transmitted world widely, but there are no effective drugs and vaccines. Whole genome sequencing (WGS) of viruses could be applied to viral pathogen characterization, diagnosis, molecular surveillance, and even finding novel pathogens. We established an improved method using direct RNA sequencing with Nanopore technology to obtain WGS of ZIKV, after adding poly (A) tails to viral RNA. This established method does not require specific primers, complimentary DNA (cDNA) synthesis, and polymerase chain reaction (PCR)-based enrichment, resulting in the reduction of biases as well as of the ability to find novel RNA viruses. Nanopore technology also allows to read long sequences. It makes WGS easier and faster with long-read assembly. In this study, we obtained WGS of two strains of ZIKV following the established protocol. The sequenced reads resulted in 99% and 100% genome coverage with 63.5X and 21,136X, for the ZIKV PRVABC59 and MR 766 strains, respectively. The sequence identities of the ZIKV PRVABC59 and MR 766 strains for each reference genomes were 98.76% and 99.72%, respectively. We also found that the maximum length of reads was 10,311 bp which is almost the whole genome size of ZIKV. These long-reads could make overall structure of whole genome easily, and WGS faster and easier. The protocol in this study could provide rapid and efficient WGS that could be applied to study the biology of RNA viruses including identification, characterization, and global surveillance.
Bias (Epidemiology) ; Biology ; Diagnosis ; DNA ; Genome ; Genome Size ; Methods ; Nanopores ; Polymerase Chain Reaction ; RNA Viruses ; RNA ; RNA, Viral ; Sequence Analysis, RNA ; Tail ; Vaccines ; Zika Virus

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.
Gene Expression ; Methods ; Phenotype ; Sequence Analysis, RNA

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.
Computer Graphics ; Gene Expression Profiling ; methods ; Humans ; Sequence Analysis, RNA ; methods ; Single-Cell Analysis

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users' input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.
Animals ; Gene Expression Profiling ; High-Throughput Nucleotide Sequencing ; Mice ; MicroRNAs ; chemistry ; metabolism ; Molecular Sequence Annotation ; RNA, Ribosomal ; chemistry ; metabolism ; RNA, Small Interfering ; chemistry ; metabolism ; RNA, Small Untranslated ; chemistry ; metabolism ; RNA, Transfer ; chemistry ; metabolism ; Sequence Analysis, RNA ; methods ; Software