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s...     

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.     

Differential expression analyses for single-cell RNA-Seq: old questions on new data

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.     

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing(RNA-seq),single-cell RNA-seq(sc RNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network(CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network(CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations.c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells.Intuitively, each CCSN can be viewed as the transformation from less ‘‘reliable" gene expression to more ‘‘reliable" gene–gene associations in a cell. Based on CCSN, we further design network flow entropy(NFE) to estimate the differentiation potency of a single cell. A number of sc RNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing sc RNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/Lin Li-0909/c-CSN.     

A resource of single-cell gene expression profiles in a planarian Dugesia japonica

The freshwater planarian Dugesia japonica maintains an abundant heterogeneous cell population called neoblasts, which include adult pluripotent stem cells. Thus, it is an excellent model organism for stem cell and regeneration research. Recently, many single-cell RNA sequencing (scRNA-seq) databases of several model organisms, including other planarian species, have become publicly available; these are powerful and useful resources to search for gene expression in various tissues and cells. However, the only scRNA-seq dataset for D. japonica has been limited by the number of genes detected. Herein, we collected D. japonica cells, and conducted an scRNA-seq analysis. A novel, automatic, iterative cell clustering strategy produced a dataset of 3,404 cells, which could be classified into 63 cell types based on gene expression profiles. We introduced two examples for utilizing the scRNA-seq dataset in this study using D. japonica. First, the dataset provided results consistent with previous studies as well as novel functionally relevant insights, that is, the expression of DjMTA and DjP2X-A genes in neoblasts that give rise to differentiated cells. Second, we conducted an integrative analysis of the scRNA-seq dataset and time-course bulk RNA-seq of irradiated animals, demonstrating that the dataset can help interpret differentially expressed genes captured via bulk RNA-seq. Using the R package “Seurat” and GSE223927, researchers can easily access and utilize this dataset.     

单细胞转录组技术及其在人类细胞图谱构建中的应用

单细胞转录组技术在单细胞水平上进行转录组测序,提供了单个细胞的基因表达差异信息,使在单细胞尺度下研究个体细胞、相关环境细胞及其相互作用的机理成为可能.近年来,单细胞转录组技术在c DNA扩增原理上经历了从末端加尾、体外逆转录到模板置换的方法发展,大大提高了基因检测的数量、基因表达的准确性等.同时,在单细胞选取方式上进行了从96/384孔板到油包水液滴以及纳米微孔的创新,在提高通量和重复性的同时降低了整体实验成本.单细胞转录组技术广泛应用于细胞群体分类和异质性研究,推动了从发育生物学到正常、病态组织细胞图谱的构建.本文对单细胞转录组技术近年的技术进展以及在人类细胞图谱构建中的应用进行了综述.     

Single-cell RNA sequencing reveals a high-resolution cell atlas of xylem in Populus

High-throughput single-cell RNA sequencing (scRNA-seq) has advantages over traditional RNA-seq to explore spatiotemporal information on gene dynamic expressions in heterogenous tissues. We performed Drop-seq, a method for the dropwise sequestration of single cells for sequencing, on protoplasts from the differentiating xylem of Populus alba × Populus glandulosa. The scRNA-seq profiled 9,798 cells, which were grouped into 12 clusters. Through characterization of differentially expressed genes in each cluster and RNA in situ hybridizations, we identified vessel cells, fiber cells, ray parenchyma cells and xylem precursor cells. Diffusion pseudotime analyses revealed the differentiating trajectory of vessels, fiber cells and ray parenchyma cells and indicated a different differentiation process between vessels and fiber cells, and a similar differentiation process between fiber cells and ray parenchyma cells. We identified marker genes for each cell type (cluster) and key candidate regulators during developmental stages of xylem cell differentiation. Our study generates a high-resolution expression atlas of wood formation at the single cell level and provides valuable information on wood formation.     

scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data

Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.     

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method,reveals non-genetic gene-expression heterogeneity

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.     

单细胞转录组测序技术在细胞分类中的应用

多细胞有机体的细胞类型多且复杂,细胞间普遍存在异质性。目前,单细胞转录组测序(single-cell RNA sequencing,scRNA-seq)技术是一项新兴的研究单个细胞转录水平的技术,其从数千个平行的细胞中生成转录谱,揭示个体细胞基因组的差异性表达,反映细胞间的异质性,从而鉴定出不同细胞类型,形成组织或器官的细胞图谱,在生物和临床医学等领域发挥重要作用。该文在对scRNA-seq测序平台进行阐述和比较的基础上,着重介绍其在神经系统和免疫系统细胞类型探索中的应用,并且总结scRNA-seq与空间转录组技术相结合的研究成果。     

MISC: missing imputation for single-cell RNA sequencing data

Background

Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNA-seq data, we need to know where the missing data is; how much data is missing; and what are the values of these data.

Methods

To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for single-cell RNA-seq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNA-seq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zero-inflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements.

Results

We compared the raw data without imputation, the mean-smooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CP-CML to the BC-CML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary.

Conclusions

Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for single-cell RNA-seq data.

     

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。     

Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data

Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical for understanding such heterogeneity. Here, we propose an approach for detecting communities from a genetic network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities were partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights, even for a large, sparse dataset, in the single-cell analysis field.     

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.