Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.     

单细胞转录组测序数据分析算法在干细胞研究中的应用

细胞的转录组决定其生理状态,每个细胞的转录组都是唯一的。借助单细胞转录组测序可分析单个干细胞的转录组特征,通过进一步的运算方法可以根据转录组特征对细胞进行细胞状态测定以及系谱分化特征的重建,在干细胞及组织发育研究中发挥了强大的作用,推动了其快速发展,加速了对干细胞分化及组织发育的相关过程及调控路径的认识。尤其是在干细胞领域的应用,得益于新算法的发展,单细胞转录组测序分析可用来阐述干细胞的起源、异质性,尤其是对干细胞的分化过程进行连续观察。本文主要对应用于干细胞分化相关研究的单细胞转录组测序数据新的算法及其应用进行了综述。     

Defining endogenous barcoding sites for CRISPR/Cas9-based cell lineage tracing in zebrafish

There is a growing interest in developing experimental methods for tracking the developmental cell lineages of a complex organism.The recently developed CRISPR/Cas9-based barcoding method is,although highly promising,difficult to scale up because it relies on exogenous barcoding sequences that are engineered into the genome.In this study,we characterized 78 high-quality endogenous sites in the zebrafish genome that can be used as CRISPR/Cas9-based barcoding sites.The 78 sites are all highly expressed in most of the cell types according to single-cell RNA sequencing(scRNA-seq)data.Hence,the barcoding information of the 78 endogenous sites is recovered by the available scRNA-seq platforms,enabling simultaneous characterization of cell type and cell lineage information.     

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Accurate identification of cell types from single-cell RNA sequencing(scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed,the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity.The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.     

Cell type diversity in scallop adductor muscles revealed by single-cell RNA-Seq

Studies on cell atlas in marine invertebrates provide a better understanding of cell types, stem cell maintenance, and lineages of cell differentiation. To investigate the molecular features of various cell types in molluscan muscles, we performed single-cell RNA sequencing (scRNA-seq) to map cell types in scallop adductor muscles. We uncovered the cell type-specific features of 20 cell clusters defined by the expression of multiple specific molecular markers. These cell clusters are mainly classified into four broad classes, including mesenchymal stem cells, muscle cells, neurons, and haemolymph cells. In particular, we identified a diverse repertoire of neurons in the striated adductor muscle, but not in the smooth muscle. We further reconstructed the cell differentiation events using all the cell clusters by single-cell pseudotemporal trajectories. By integrating dual BrdU-PCNA immunodetection, neuron-specific staining and electron microscopy observation, we showed the spatial distribution of mesenchymal stem cells and neurons in striated adductor muscle of scallops. The present findings will not only be useful to address the cell type-specific gene expression profiles in scallop muscles, but also provide valuable resources for cross-species comparison of marine organisms.     

单细胞转录组测序技术在心脏发育、疾病以及医学中的应用

单细胞转录组测序(Single-cell RNA sequencing,scRNA-seq)可以在单细胞水平描绘出每个细胞同一基因的表达量在不同细胞间的表达水平差异,使得在单细胞水平重新认识各种组织器官成为可能。目前对心脏的测序研究正从传统的普通转录组水平过渡到单细胞水平,对小鼠和人的心脏的测序陆续地发表出来。概述了scRNA-seq在心脏发育、疾病以及医学中的应用,讨论了scRNA-seq技术在胚胎心脏发育、心脏细胞的异质性以及在心脏血管方面、多能干细胞分化心血管细胞模型和先天性心脏畸形的进展和存在问题,对scRNA-seq技术对心脏发育、心脏再生、心脏病和单细胞个性化医疗等方面作出展望。     

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or"tipping point"of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regu-latory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the"dark genes"that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes. The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.     

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.     

Approximate distance correlation for selecting highly interrelated genes across datasets

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.     

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

Annotating cell types is a critical step in single-cell RNA sequencing(scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection(CP) and robust partial correlations(RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other,compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.     

Applications of single cell RNA sequencing to research of stem cells

Stem cells(SCs) with their self-renewal and pluripotent differentiation potential,show great promise for therapeutic applications to some refractory diseases such as stroke, Parkinsonism, myocardial infarction, and diabetes. Furthermore, as seed cells in tissue engineering, SCs have been applied widely to tissue and organ regeneration. However, previous studies have shown that SCs are heterogeneous and consist of many cell subpopulations. Owing to this heterogeneity of cell states, gene expression is highly diverse between cells even within a single tissue,making precise identification and analysis of biological properties difficult, which hinders their further research and applications. Therefore, a defined understanding of the heterogeneity is a key to research of SCs. Traditional ensemble-based sequencing approaches, such as microarrays, reflect an average of expression levels across a large population, which overlook unique biological behaviors of individual cells, conceal cell-to-cell variations, and cannot understand the heterogeneity of SCs radically. The development of high throughput single cell RNA sequencing(scRNA-seq) has provided a new research tool in biology, ranging from identification of novel cell types and exploration of cell markers to the analysis of gene expression and predicating developmental trajectories. scRNA-seq has profoundly changed our understanding of a series of biological phenomena. Currently, it has been used in research of SCs in many fields, particularly for the research of heterogeneity and cell subpopulations in early embryonic development. In this review, we focus on the scRNA-seq technique and its applications to research of SCs.     

Comparative analysis of cell lineage differentiation during hepatogenesis in humans and mice at the single-cell transcriptome level

During embryogenesis, the liver is the site of hepatogenesis and hematopoiesis and contains many cell lineages derived from the endoderm and mesoderm. However, the characteristics and developmental programs of many of these cell lineages remain unclear, especially in humans. Here, we performed single-cell RNA sequencing of whole human and mouse fetal livers throughout development. We identified four cell lineage families of endoderm-derived, erythroid, non-erythroid hematopoietic, and mesoderm-derived non-hematopoietic cells, and defined the developmental pathways of the major cell lineage families. In both humans and mice, we identified novel markers of hepatic lineages and an ID3⁺ subpopulation of hepatoblasts as well as verified that hepatoblast differentiation follows the “default-directed” model. Additionally, we found that human but not mouse fetal hepatocytes display heterogeneity associated with expression of metabolism-related genes. We described the developmental process of erythroid progenitor cells during human and mouse hematopoiesis. Moreover, despite the general conservation of cell differentiation programs between species, we observed different cell lineage compositions during hematopoiesis in the human and mouse fetal livers. Taken together, these results reveal the dynamic cell landscape of fetal liver development and illustrate the similarities and differences in liver development between species, providing an extensive resource for inducing various liver cell lineages in vitro.Subject terms: Developmental biology, Stem-cell differentiation, Stem-cell differentiation, Developmental biology     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

单细胞RNA测序技术在病毒研究中的应用

单细胞RNA测序(single-cell RNA sequencing, scRNA-seq)技术已经成为不同领域中研究细胞异质性的有效工具。在病毒研究领域中,利用该技术分析病毒和细胞的转录组,可以在单细胞水平上检测病毒感染的动态变化,了解病毒与细胞间复杂的相互作用。本文简述了scRNA-seq技术,着重介绍病毒感染宿主细胞后scRNA-seq研究的最新进展,同时也描述了细胞周期、基因表达、细胞状态等细胞异质性对病毒感染过程的影响,以及病毒变异对其本身感染过程的影响。此外,本文还分析了scRNA-seq在研究病毒–宿主互作动态变化方面具有的独特优势,及其在病毒研究领域中广阔的应用前景,为揭示病毒的感染与致病机制、抗病毒靶标的开发等提供参考。