Single-cell analysis reveals an Angpt4-initiated EPDC-EC-CM cellular coordination cascade during heart regeneration

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.

Advances in methods and applications of single-cell Hi-C data analysis / 生物医学工程学杂志

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.

Application of single-cell transcriptome sequencing in mechanistic toxicology / 中华预防医学杂志

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.

Quantifying the state of cell differentiation based on the gene networks entropy / 生物工程学报

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.

PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19 / 医学前沿

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.

Cancer biology deciphered by single-cell transcriptomic sequencing

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.

Bi-FoRe: an efficient bidirectional knockin strategy to generate pairwise conditional alleles with fluorescent indicators

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.

A review on integration methods for single-cell data / 生物医学工程学杂志

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.

Application prospects of single-cell transcriptome sequencing in traditional Chinese medicine research / 中国中药杂志

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.

Single-cell analysis of angiotensin-converting enzyme II expression in human kidneys and bladders reveals a potential route of 2019 novel coronavirus infection / 中华医学杂志(英文版)

BACKGROUND@#Since 2019, a novel coronavirus named 2019 novel coronavirus (2019-nCoV) has emerged worldwide. Apart from fever and respiratory complications, acute kidney injury has been observed in a few patients with coronavirus disease 2019. Furthermore, according to recent findings, the virus has been detected in urine. Angiotensin-converting enzyme II (ACE2) has been proposed to serve as the receptor for the entry of 2019-nCoV, which is the same as that for the severe acute respiratory syndrome. This study aimed to investigate the possible cause of kidney damage and the potential route of 2019-nCoV infection in the urinary system.@*METHODS@#We used both published kidney and bladder cell atlas data and new independent kidney single-cell RNA sequencing data generated in-house to evaluate ACE2 gene expression in all cell types in healthy kidneys and bladders. The Pearson correlation coefficients between ACE2 and all other genes were first generated. Then, genes with r values larger than 0.1 and P values smaller than 0.01 were deemed significant co-expression genes with ACE2.@*RESULTS@#Our results showed the enriched expression of ACE2 in all subtypes of proximal tubule (PT) cells of the kidney. ACE2 expression was found in 5.12%, 5.80%, and 14.38% of the proximal convoluted tubule cells, PT cells, and proximal straight tubule cells, respectively, in three published kidney cell atlas datasets. In addition, ACE2 expression was also confirmed in 12.05%, 6.80%, and 10.20% of cells of the proximal convoluted tubule, PT, and proximal straight tubule, respectively, in our own two healthy kidney samples. For the analysis of public data from three bladder samples, ACE2 expression was low but detectable in bladder epithelial cells. Only 0.25% and 1.28% of intermediate cells and umbrella cells, respectively, had ACE2 expression.@*CONCLUSION@#This study has provided bioinformatics evidence of the potential route of 2019-nCoV infection in the urinary system.

Quantification of DNA Damage in Different Tissues in Rats with Heart Failure / Quantificação de Dano em DNA em Diferentes Tecidos em Ratos com Insuficiência Cardíaca

Abstract Background: Chronic heart failure (CHF) is a complex syndrome which comprises structural and functional alterations in the heart in maintaining the adequate blood demand to all tissues. Few investigations sought to evaluate oxidative DNA damage in CHF. Objective: To quantify the DNA damage using the comet assay in left ventricle (LV), lungs, diaphragm, gastrocnemius and soleus in rats with CHF. Methods: Twelve male Wistar rats (300 to 330 g) were selected for the study: Sham (n = 6) and CHF (n = 6). The animals underwent myocardial infarction by the ligation of the left coronary artery. After six weeks, the animals were euthanized. It was performed a cell suspension of the tissues. The comet assay was performed to evaluate single and double strand breaks in DNA. Significance level (p) considered < 0.05. Results: The CHF group showed higher values of left ventricle end-diastolic pressure (LVEDP), pulmonary congestion, cardiac hypertrophy and lower values of maximal positive and negative derivatives of LV pressure, LV systolic pressure (p < 0.05). CHF group showed higher DNA damage (% tail DNA, tail moment and Olive tail moment) compared to Sham (p < 0.001). The tissue with the highest damage was the soleus, compared to LV and gastrocnemius in CHF group (p < 0.05). Conclusion: Our results indicates that the CHF affects all tissues, both centrally and peripherically, being more affected in skeletal muscle (soleus) and is positively correlated with LV dysfunction.

Resumo Fundamento: A insuficiência cardíaca crônica (ICC) é uma síndrome complexa que compreende alterações estruturais e funcionais no coração, mantendo demanda sanguínea adequada a todos os tecidos. Poucas investigações procuraram avaliar o dano oxidativo ao DNA na ICC. Objetivo: Quantificar o dano ao DNA utilizando o ensaio cometa no ventrículo esquerdo (VE), pulmões, diafragma, gastrocnêmio e sóleo em ratos com ICC. Métodos: Doze ratos Wistar machos (300 a 330 g) foram selecionados para o estudo: placebo (n = 6) e ICC (n = 6). Os animais foram submetidos a infarto do miocárdio através de ligadura da artéria coronária esquerda. Após seis semanas, os animais foram sacrificados. Foi realizada uma suspensão celular dos tecidos. O ensaio cometa foi realizado para avaliar as quebras de fita simples e dupla no DNA. Nível de significância (p) < 0,05. Resultados: O grupo ICC apresentou maiores valores de pressão diastólica final do ventrículo esquerdo (PDFVE), congestão pulmonar, hipertrofia cardíaca e menores valores de derivados máximos positivos e negativos da pressão do VE, pressão sistólica do VE (p < 0,05). O grupo ICC apresentou maior dano ao DNA (% de DNA da cauda, momento da cauda e momento da cauda de Olive) em comparação ao placebo (p < 0,001). O tecido com maior dano foi o sóleo, comparado ao VE e ao gastrocnêmio no grupo ICC (p < 0,05). Conclusão: Nossos resultados indicam que a ICC afeta todos os tecidos, de maneira central e periférica, sendo mais afetada no músculo esquelético (sóleo) e está positivamente correlacionada com a disfunção do VE.

New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

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.

A human circulating immune cell landscape in aging and COVID-19

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.

A human circulating immune cell landscape in aging and COVID-19

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.

A human circulating immune cell landscape in aging and COVID-19

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.

SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data / 基因组蛋白质组与生物信息学报·英文版

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.

Single-cell Analysis of CAR-T Cell Activation Reveals A Mixed T1/T2 Response Independent of Differentiation / 基因组蛋白质组与生物信息学报·英文版

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.

Light-induced injury in mouse embryos revealed by single-cell RNA sequencing

BACKGROUND: Light exposure is a common stress factor in in vitro manipulation of embryos in the reproductive center. Many studies have shown the deleterious effects of high-intensity light exposure in different animal embryos. However, no transcriptomic studies have explored the light-induced injury and response in preimplantation embryos. RESULTS: Here, we adopt different time-courses and illumination intensities to treat mouse embryos at the 2-cell stage and evaluate their effects on blastulation. Meanwhile, single-cell transcriptomes from the 2-cell to blastocyst stage were analyzed after high-intensity light exposure. These data show that cells at each embryonic stage can be categorized into different light conditions. Further analyses of differentially expressed genes and GO terms revealed the light-induced injury as well as the potential repair response after high-intensity lighting. Maternal-to-zygote transition is also affected by the failure to remove maternal RNAs and deactivate zygotic genome expression. CONCLUSION: Our work revealed an integrated response to high-intensity lighting, involving morphological changes, long-lasting injury effects, and intracellular damage repair mechanisms.

Single Cell RNA-Sequencing for the Study of Atherosclerosis

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.