Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.
Chromatin ; Genome ; Single-Cell Analysis/methods* ; Cell Differentiation ; Data Analysis

For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.
Animals ; Cell Communication ; Humans ; RNA-Seq ; Single-Cell Analysis ; Transcriptome

Betacoronavirus ; Coronavirus Infections ; Humans ; Pandemics ; Pneumonia, Viral ; Single-Cell Analysis

Single-cell transcriptome sequencing(scRNA-seq) can be used to analyze the expression characteristics of the transcriptome at the level of individual cell, and discover the heterogeneity of gene expression in individual cell that is "diluted" or averaged in study of group organization. The scRNA-seq, with the characteristics of standardization, high-throughput, and high integration, can greatly simplify the experimental operation and significantly reduce the consumption of reagents. At the same time, a variety of cells are screened and the gene expression patterns are analyzed at the single-cell level to provide a more efficient detection technique and more rich and accurate information for drug research. In the field of traditional Chinese medicine(TCM), the scRNA-seq is still a new technology, but the individual and precision concepts embodied by scRNA-seq and the theory of TCM syndrome differentiation and treatment have reached the same effect between the micro and macro aspects. This study tried to broaden the thinking for the modernization of TCM by introducing the development of scRNA-seq technology and its application in modern drug research and discussing the application prospects of scRNA-seq in TCM research.
Gene Expression Profiling ; Medicine, Chinese Traditional ; Reference Standards ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome

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

Atherosclerosis is a major cause of coronary artery disease and stroke. A massive and new type of data has finally arrived in the field of atherosclerosis: single cell RNA sequencing (scRNAseq). Recently, scRNAseq has been successfully applied to the study of atherosclerosis to identify previously uncharacterized cell populations. scRNAseq is an effective approach to evaluate heterogeneous cell populations by measuring the transcriptomic profiles at the single cell level. Besides the studies of atherosclerosis, scRNAseq is being employed in various areas of biology, including cancer research and organ development. In order to analyze these new massive datasets, various analytic approaches have been developed. This review aims to enhance the understanding of this new technology by exploring how the single cell transcriptome has been applied to the study of atherosclerosis and further discuss potential analysis of using scRNAseq.
Atherosclerosis ; Biology ; Coronary Artery Disease ; Dataset ; Sequence Analysis, RNA ; Single-Cell Analysis ; Stroke ; Transcriptome

Traditional bulk RNA sequencing assesses the average expression level of genes in tissues rather than the differences in cellular responses. Accordingly, it is hard to differentiate sensitive responding cells, leading to inaccurate identification of toxicity pathways. Single-cell RNA sequencing (scRNA-seq) isolated single cells from tissue and subjected them to cell subtypes-specific transcriptome analysis. This technique in toxicological studies realizes the heterogeneous cellular responses in the tissue microenvironment upon chemical exposure. Thus it helps to identify sensitive responding cells and key molecular events, providing a powerful tool and a new perspective for exploring the mechanisms of toxicity and the modes of action. This review summarizes the development, principle, method, application and limitations of scRNA-seq in mechanistic toxicological researches, and discusses the prospect of multi-directional applications.
Base Sequence ; Gene Expression Profiling ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome

The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
Base Sequence ; Gene Expression Profiling ; Gene Expression Regulation ; Humans ; Sequence Analysis, RNA ; Single-Cell Analysis

Basic research in life science and medicine has dug into single cell level in recent years. Single-cell analysis offers to understand life from diverse perspectives and is used to profile cell heterogeneity to investigate mechanism of diseases. Single cell technologies have also found applications in forensic medicine and clinical reproductive medicine, while the techniques are rapidly evolving and have become more and more sophisticated. In this article, we reviewed various single cell isolation techniques and their pros and cons, including manual cell picking, laser capture microdissection and microfluidics, as well as analysis methods for DNA, RNA and protein in single cell. In addition, we summarized major up-to-date single cell research achievements and their potential applications.
Animals ; Cell Separation ; DNA ; Laser Capture Microdissection ; RNA ; Single-Cell Analysis

Studies of cellular dynamic processes have shown that cells undergo state changes during dynamic processes, controlled mainly by the expression of genes within the cell. With the development of high-throughput sequencing technologies, the availability of large amounts of gene expression data enables the acquisition of true gene expression information of cells at the single-cell level. However, most existing research methods require the use of information beyond gene expression, thus introducing additional complexity and uncertainty. In addition, the prevalence of dropout events hampers the study of cellular dynamics. To this end, we propose an approach named gene interaction network entropy (GINE) to quantify the state of cell differentiation as a means of studying cellular dynamics. Specifically, by constructing a cell-specific network based on the association between genes through the stability of the network, and defining the GINE, the unstable gene expression data is converted into a relatively stable GINE. This method has no additional complexity or uncertainty, and at the same time circumvents the effects of dropout events to a certain extent, allowing for a more reliable characterization of biological processes such as cell fate. This method was applied to study two single-cell RNA-seq datasets, head and neck squamous cell carcinoma and chronic myeloid leukaemia. The GINE method not only effectively distinguishes malignant cells from benign cells and differentiates between different periods of differentiation, but also effectively reflects the disease efficacy process, demonstrating the potential of using GINE to study cellular dynamics. The method aims to explore the dynamic information at the level of single cell disorganization and thus to study the dynamics of biological system processes. The results of this study may provide scientific recommendations for research on cell differentiation, tracking cancer development, and the process of disease response to drugs.
Cell Differentiation/genetics* ; Entropy ; Gene Regulatory Networks ; High-Throughput Nucleotide Sequencing ; Single-Cell Analysis/methods*