Efforts have been made to establish various human pluripotent stem cell lines. However, such methods have not yet been duplicated in non-human primate cells. Here, we introduce a multiplexed single-cell sequencing technique to profile the molecular features of monkey pluripotent stem cells in published culture conditions. The results demonstrate suboptimized maintenance of pluripotency and show that the selected signaling pathways for resetting human stem cells can also be interpreted for establishing monkey cell lines. Overall, this work legitimates the translation of novel human cell line culture conditions to monkey cells and provides guidance for exploring chemical cocktails for monkey stem cell line derivation.
Animals ; Haplorhini ; Single-Cell Gene Expression Analysis ; Pluripotent Stem Cells/metabolism* ; Cell Line ; Signal Transduction ; Cell Differentiation ; Transcriptome

Mammals exhibit limited heart regeneration ability, which can lead to heart failure after myocardial infarction. In contrast, zebrafish exhibit remarkable cardiac regeneration capacity. Several cell types and signaling pathways have been reported to participate in this process. However, a comprehensive analysis of how different cells and signals interact and coordinate to regulate cardiac regeneration is unavailable. We collected major cardiac cell types from zebrafish and performed high-precision single-cell transcriptome analyses during both development and post-injury regeneration. We revealed the cellular heterogeneity as well as the molecular progress of cardiomyocytes during these processes, and identified a subtype of atrial cardiomyocyte exhibiting a stem-like state which may transdifferentiate into ventricular cardiomyocytes during regeneration. Furthermore, we identified a regeneration-induced cell (RIC) population in the epicardium-derived cells (EPDC), and demonstrated Angiopoietin 4 (Angpt4) as a specific regulator of heart regeneration. angpt4 expression is specifically and transiently activated in RIC, which initiates a signaling cascade from EPDC to endocardium through the Tie2-MAPK pathway, and further induces activation of cathepsin K in cardiomyocytes through RA signaling. Loss of angpt4 leads to defects in scar tissue resolution and cardiomyocyte proliferation, while overexpression of angpt4 accelerates regeneration. Furthermore, we found that ANGPT4 could enhance proliferation of neonatal rat cardiomyocytes, and promote cardiac repair in mice after myocardial infarction, indicating that the function of Angpt4 is conserved in mammals. Our study provides a mechanistic understanding of heart regeneration at single-cell precision, identifies Angpt4 as a key regulator of cardiomyocyte proliferation and regeneration, and offers a novel therapeutic target for improved recovery after human heart injuries.
Humans ; Mice ; Rats ; Cell Proliferation ; Heart/physiology* ; Mammals ; Myocardial Infarction/metabolism* ; Myocytes, Cardiac/metabolism* ; Pericardium/metabolism* ; Single-Cell Analysis ; Zebrafish/metabolism*

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

Postnatal heart maturation is the basis of normal cardiac function and provides critical insights into heart repair and regenerative medicine. While static snapshots of the maturing heart have provided much insight into its molecular signatures, few key events during postnatal cardiomyocyte maturation have been uncovered. Here, we report that cardiomyocytes (CMs) experience epigenetic and transcriptional decline of cardiac gene expression immediately after birth, leading to a transition state of CMs at postnatal day 7 (P7) that was essential for CM subtype specification during heart maturation. Large-scale single-cell analysis and genetic lineage tracing confirm the presence of transition state CMs at P7 bridging immature state and mature states. Silencing of key transcription factor JUN in P1-hearts significantly repressed CM transition, resulting in perturbed CM subtype proportions and reduced cardiac function in mature hearts. In addition, transplantation of P7-CMs into infarcted hearts exhibited cardiac repair potential superior to P1-CMs. Collectively, our data uncover CM state transition as a key event in postnatal heart maturation, which not only provides insights into molecular foundations of heart maturation, but also opens an avenue for manipulation of cardiomyocyte fate in disease and regenerative medicine.
Gene Expression Regulation ; Heart ; Myocytes, Cardiac/metabolism* ; Single-Cell Analysis ; Transcription Factors/metabolism*

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*

Tumors are complex ecosystems in which heterogeneous cancer cells interact with their microenvironment composed of diverse immune, endothelial, and stromal cells. Cancer biology had been studied using bulk genomic and gene expression profiling, which however mask the cellular diversity and average the variability among individual molecular programs. Recent advances in single-cell transcriptomic sequencing have enabled a detailed dissection of tumor ecosystems and promoted our understanding of tumorigenesis at single-cell resolution. In the present review, we discuss the main topics of recent cancer studies that have implemented single-cell RNA sequencing (scRNA-seq). To study cancer cells, scRNA-seq has provided novel insights into the cancer stem-cell model, treatment resistance, and cancer metastasis. To study the tumor microenvironment, scRNA-seq has portrayed the diverse cell types and complex cellular states of both immune and non-immune cells interacting with cancer cells, with the promise to discover novel targets for future immunotherapy.
Ecosystem ; Gene Expression Profiling ; Genomics ; Humans ; Neoplasms/pathology* ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome ; Tumor Microenvironment/genetics*

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

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

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*