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

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.
COVID-19 ; Humans ; RNA ; SARS-CoV-2/genetics* ; Single-Cell Analysis/methods* ; Transcriptome

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*

Gene expression labeling and conditional manipulation of gene function are important for elaborate dissection of gene function. However, contemporary generation of pairwise dual-function knockin alleles to achieve both conditional and geno-tagging effects with a single donor has not been reported. Here we first developed a strategy based on a flipping donor named FoRe to generate conditional knockout alleles coupled with fluorescent allele-labeling through NHEJ-mediated unidirectional targeted insertion in zebrafish facilitated by the CRISPR/Cas system. We demonstrated the feasibility of this strategy at sox10 and isl1 loci, and successfully achieved Cre-induced conditional knockout of target gene function and simultaneous switch of the fluorescent reporter, allowing generation of genetic mosaics for lineage tracing. We then improved the donor design enabling efficient one-step bidirectional knockin to generate paired positive and negative conditional alleles, both tagged with two different fluorescent reporters. By introducing Cre recombinase, these alleles could be used to achieve both conditional knockout and conditional gene restoration in parallel; furthermore, differential fluorescent labeling of the positive and negative alleles enables simple, early and efficient real-time discrimination of individual live embryos bearing different genotypes prior to the emergence of morphologically visible phenotypes. We named our improved donor as Bi-FoRe and demonstrated its feasibility at the sox10 locus. Furthermore, we eliminated the undesirable bacterial backbone in the donor using minicircle DNA technology. Our system could easily be expanded for other applications or to other organisms, and coupling fluorescent labeling of gene expression and conditional manipulation of gene function will provide unique opportunities to fully reveal the power of emerging single-cell sequencing technologies.
Alleles ; Animals ; CRISPR-Cas Systems ; DNA End-Joining Repair ; DNA, Circular/metabolism* ; Embryo, Nonmammalian ; Gene Editing/methods* ; Gene Knock-In Techniques ; Gene Knockout Techniques ; Genes, Reporter ; Genetic Loci ; Genotyping Techniques ; Green Fluorescent Proteins/metabolism* ; Integrases/metabolism* ; Luminescent Proteins/metabolism* ; Mutagenesis, Insertional ; Single-Cell Analysis ; Zebrafish/metabolism*

Ambystoma mexicanum/immunology* ; Amputation ; Animals ; Biomarkers/metabolism* ; Blastomeres/immunology* ; Cell Lineage/immunology* ; Connective Tissue Cells/immunology* ; Epithelial Cells/immunology* ; Forelimb ; Gene Expression ; High-Throughput Nucleotide Sequencing ; Humans ; Immunity ; Peroxiredoxins/immunology* ; Regeneration/immunology* ; Regenerative Medicine/methods* ; Single-Cell Analysis/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

The activation mechanism of chimeric antigen receptor (CAR)-engineered T cells may differ substantially from T cells carrying native T cell receptor, but this difference remains poorly understood. We present the first comprehensive portrait of single-cell level transcriptional and cytokine signatures of anti-CD19/4-1BB/CD28/CD3ζ CAR-T cells upon antigen-specific stimulation. Both CD4 helper T (T) cells and CD8 cytotoxic CAR-T cells are equally effective in directly killing target tumor cells and their cytotoxic activity is associated with the elevation of a range of T1 and T2 signature cytokines, e.g., interferon γ, tumor necrotic factor α, interleukin 5 (IL5), and IL13, as confirmed by the expression of master transcription factor genes TBX21 and GATA3. However, rather than conforming to stringent T1 or T2 subtypes, single-cell analysis reveals that the predominant response is a highly mixed T1/T2 function in the same cell. The regulatory T cell activity, although observed in a small fraction of activated cells, emerges from this hybrid T1/T2 population. Granulocyte-macrophage colony stimulating factor (GM-CSF) is produced from the majority of cells regardless of the polarization states, further contrasting CAR-T to classic T cells. Surprisingly, the cytokine response is minimally associated with differentiation status, although all major differentiation subsets such as naïve, central memory, effector memory, and effector are detected. All these suggest that the activation of CAR-engineered T cells is a canonical process that leads to a highly mixed response combining both type 1 and type 2 cytokines together with GM-CSF, supporting the notion that polyfunctional CAR-T cells correlate with objective response of patients in clinical trials. This work provides new insights into the mechanism of CAR activation and implies the necessity for cellular function assays to characterize the quality of CAR-T infusion products and monitor therapeutic responses in patients.
Antigens ; metabolism ; CTLA-4 Antigen ; metabolism ; Cell Differentiation ; drug effects ; Cell Line ; Cytokines ; metabolism ; Cytotoxicity, Immunologic ; drug effects ; Granulocyte-Macrophage Colony-Stimulating Factor ; pharmacology ; Humans ; Lymphocyte Activation ; drug effects ; immunology ; Lymphocyte Subsets ; drug effects ; metabolism ; Phenotype ; Proteomics ; Receptors, Chimeric Antigen ; metabolism ; Single-Cell Analysis ; methods ; T-Lymphocytes, Regulatory ; drug effects ; metabolism ; Th1 Cells ; cytology ; drug effects ; Th2 Cells ; cytology ; drug effects ; Transcription, Genetic ; drug effects ; Up-Regulation ; drug effects

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

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