单细胞转录组测序技术及在哺乳动物上的应用

哺乳动物的器官是由多种细胞类型组成,它们通过细胞间的相互作用来发出信号,以维持体内平衡和确保机体发育。传统转录组测序是以大量细胞或组织为研究样本,反映的是细胞总体上转录组特征,不能分析单个细胞的基因表达情况,而单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)技术的发展为揭示单个细胞转录组特征提供了有效方法。本文通过对scRNA-seq平台、scRNA-seq主要技术类型及scRNA-seq在哺乳动物上的应用展开综述。     

单细胞转录组测序技术在动物上的应用研究

细胞是机体最基本的结构组成及功能单位,细胞类型和功能由其整个转录表达谱决定,通过单细胞转录组测序可以获得单个细胞转录表达谱,由此以高精度分辨率鉴定细胞类型、细胞状态以及稀有类型细胞,从而可以在单细胞水平分析细胞动态变化及细胞间的关系,深入解析驱动细胞变化及细胞异常背后的分子细胞机制。随着单细胞测序技术稳定性和测序通量的提高,以及测序成本的降低,其在发育生物学、肿瘤、免疫及疾病等领域被广泛应用,研究对象主要涉及人及模式生物,在动物上的应用研究相对较少。本文主要介绍单细胞转录组测序技术及其生物学应用并综述目前其在动物上的一些开创性研究,以期为今后更好的在动物上应用单细胞转录组测序提供方法参考。     

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。     

植物单细胞转录组测序研究进展

单细胞转录组测序是一种在单细胞水平上研究基因表达的技术.多孔板法和液滴法是目前应用于植物研究的两类主要的单细胞转录组技术.首先概述了植物单细胞转录组测序的技术原理和数据分析流程,然后介绍了植物单细胞转录组的研究进展,重点阐述了单细胞转录组测序技术在鉴定植物细胞类型、揭示细胞演化轨迹和构建细胞间调控网络中的应用.单细胞转...     

单细胞转录组高通量测序分析新进展

细胞异质性是生物组织的普遍特征。常规转录组测序(RNA-Seq)技术需要上万个细胞,所测结果实际上是一群细胞基因表达的平均值,所以难以鉴别细胞之间基因表达的异质性。单细胞RNA-Seq技术的分辨率精确至单个细胞,为辨别异质性群体中各种细胞类型的转录组特征提供了有力的工具。近年来单细胞RNA-Seq技术发展迅速,在方法学上包括cDNA扩增方法的多样化、对灵敏度和技术噪声的定量分析、浅覆盖高通量单细胞RNA-Seq方法和原位RNA-Seq技术等;在技术应用方面应用范围从早期胚胎发育扩大到组织器官发育、免疫和肿瘤等多个领域。文章对单细胞RNA-Seq在方法学和技术应用两方面的研究进展进行了详细阐述。     

单细胞转录组测序在少突胶质谱系细胞异质性与神经系统疾病中的应用

少突胶质细胞是中枢神经系统中形成髓鞘的高度特化的胶质细胞，由少突胶质前体细胞分化而来。长期以来，围绕少突胶质谱系细胞开展的研究主要集中在少突胶质细胞发育、髓鞘形成以及少突胶质谱系细胞在神经系统疾病中的作用等。新兴的单细胞转录组测序技术可以在转录组层面鉴定出特定类型细胞，为少突胶质谱系细胞的研究提供助力。本综述主要关注常见单细胞测序技术的发展以及它们在少突胶质细胞功能异质性和神经系统疾病研究中的应用，并对已取得的成果进行总结阐述，为单细胞测序技术在中枢神经系统疾病中少突胶质谱系细胞相关研究的应用和开发提供思路和参考。     

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.     

单细胞转录组测序在药物成瘾研究中的应用

药物成瘾是复杂的中枢神经系统疾病，相关基础与临床研究均证实药物成瘾的神经机制及神经环路在成瘾行为形成的不同阶段逐渐发生改变。利用全基因组关联研究、全基因组测序、全外显子测序或高通量转录组测序等技术的组学研究对包括药物成瘾在内的精神疾病遗传的脆弱性进行了深入研究。上述单核苷酸多态性检测技术或测序技术主要预测疾病的遗传风险位点。然而，许多中枢神经系统疾病的发生与环境因素密切相关，而且在疾病发展的不同阶段，相关基因的表达存在脑区特异性的细胞异质性信息。因此，传统研究对发病机制的解释存在一定的局限性。单细胞转录组测序技术是针对单个细胞进行转录水平的测定，规避了传统测序对细胞群体平均转录水平检测的缺点，可以定量描述细胞异质性。近年来，单细胞转录测序技术在神经精神科学研究中的应用逐渐受到关注，本文总结了该技术在神经科学研究中的重要应用，并以药物成瘾为例，重点阐述说明其在中枢神经系统疾病中的应用价值。     

单细胞转录组研究进展

单细胞转录组分析以单个细胞为特定研究对象,提取mRNA进行逆转录、放大和高通量测序分析,能揭示该细胞内整体水平的基因表达状态和基因结构信息,准确反映细胞间的异质性,深入理解其基因型和表型之间的相互关系,在发育生物学、基础医学、临床诊断和药物开发等领域都发挥重要作用．本文主要介绍了单细胞转录组分析的特点和技术发展历史以及常用研究策略和不同技术的优缺点,并就其面临挑战和未来发展前景进行了讨论,为该技术的进一步研究与应用提供参考．     

时空转录组研究进展

时空异质性是造成不同组织功能差异的关键因素,在细胞命运调控过程中发挥重要作用。时空转录组(stRNA-seq)是一项将定量转录组与高分辨率组织成像相结合的新兴组学技术。它将表达数据锚定到目标器官或组织的物理图上,并以无偏见的生物信息学方式对组织切片和细胞层进行分子表征,反映特定细胞内基因的时空异质性表达丰度。受益于高通量测序技术的快速发展,时空转录组为探究各类细胞基因表达的时空异质性提供了新的实验思路和方法。该文介绍了时空转录组的原理和发展进程,以及不同时空转录组的技术特点和优劣,总结了时空转录组在动物、植物和微生物中的应用,可为今后系统开展时空转录组研究提供理论参考。     

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...     

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.     

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.     

Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders

Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.     

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.     

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.     

New technologies to study helminth development and host-parasite interactions

How parasites develop and survive, and how they stimulate or modulate host immune responses are important in understanding disease pathology and for the design of new control strategies. Microarray analysis and bulk RNA sequencing have provided a wealth of data on gene expression as parasites develop through different life-cycle stages and on host cell responses to infection. These techniques have enabled gene expression in the whole organism or host tissue to be detailed, but do not take account of the heterogeneity between cells of different types or developmental stages, nor the spatial organisation of these cells. Single-cell RNA-seq (scRNA-seq) adds a new dimension to studying parasite biology and host immunity by enabling gene profiling at the individual cell level. Here we review the application of scRNA-seq to establish gene expression cell atlases for multicellular helminths and to explore the expansion and molecular profile of individual host cell types involved in parasite immunity and tissue repair. Studying host-parasite interactions in vivo is challenging and we conclude this review by briefly discussing the applications of organoids (stem-cell derived mini-tissues) to examine host-parasite interactions at the local level, and as a potential system to study parasite development in vitro. Organoid technology and its applications have developed rapidly, and the elegant studies performed to date support the use of organoids as an alternative in vitro system for research on helminth parasites.