单细胞转录组分析研究进展

目前常规的转录组分析方法无法揭示单个细胞之间基因表达的异质性,也难以对极少量细胞进行分析,单细胞转录组分析技术为此提供了有效的研究工具。对单细胞转录组分析技术的历史、发展、策略、方法和应用进行综述。     

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.     

单细胞测序技术及应用进展

同一组织中的细胞往往被认为是具有相同状态的功能单位,传统的检测手段分析的是细胞群体的总体平均反应。然而通过对单个细胞的DNA或RNA进行测序,表明组织系统层面的功能是由异质性细胞构成的。单细胞测序以单个细胞为单位,通过全基因组或转录组扩增,进行高通量测序,能够揭示单个细胞的基因结构和基因表达状态,反映细胞间的异质性,在肿瘤、发育生物学、微生物学、神经科学等领域发挥重要作用,正成为生命科学研究的焦点。单细胞测序的难点是单个细胞的分离、单细胞基因组和转录组的扩增。本文主要介绍和分析了单细胞测序技术中常用的单细胞分离技术、单细胞基因组扩增技术和转录组扩增技术及其优缺点,并对当前已经取得成果的应用领域进行了阐述,为单细胞测序技术的研究与应用提供参考。     

单细胞测序方法研究进展

近年来,高通量测序技术(Next-generation sequencing,NGS)快速发展,已广泛应用于生命科学各个领域,但传统的混合细胞测序(Bulk cell sequencing)检测的是细胞群体的总平均反应,无法反应每个细胞的真实情况,这会影响研究者对细胞功能认知的准确性。单细胞测序技术(Single cell sequencing,sc-Seq)的出现,从一定程度上解决了传统测序固有的缺陷。单细胞测序是针对单个细胞的RNA或DNA进行测序,能够准确测出单个细胞的基因结构和表达状态,从而分析相同表型细胞的异质性。本文首先介绍单细胞测序的原理、测序类型和测序平台,有助于理解单细胞测序和在进行科研项目时设计合适的项目方案。进一步介绍单细胞转录组测序的分析流程和各种常用的分析工具或软件,并重点阐述单细胞转录组测序分析中的细胞聚类和拟时序分析的原理和研究进展,为进行单细胞转录组测序数据分析提供参考。最后,本文简述了单细胞测序研究热度、单细胞测序的应用、挑战和展望等,有助于更全面地认识单细胞测序。     

空间转录物组学技术进展及其在神经科学领域中的应用

空间转录物组学是在单细胞RNA测序技术基础上实现细胞空间位置信息测定的组学技术。该技术克服了单细胞转录物组学在单细胞分离建库过程中丢失细胞在组织中空间信息的问题,可同时提供研究对象的转录物组数据信息和在组织中的空间位置信息。空间转录物组学技术对研究细胞谱系的发生过程、细胞间的调控机制和相互作用等具有重要作用,是组学技术研究的重要发展方向和热点。近年来,空间转录物组学技术发展迅速,新的检测方法不断产生,检测灵敏度、分辨率和检测通量等技术指标不断提升。本文根据获取空间信息的原理不同,将较为常用的空间转录物组学技术进行了分类,总结了各类方法的检测原理、代表性技术手段及其相应的技术指标。随后,从脑细胞类型区分与细胞层图谱构建、神经系统相关疾病特征分析与标志物研究两个方面举例论述了空间转录物组学技术在神经科学中的应用。最后,对空间转录物组学技术目前存在的问题进行了总结,并对其未来的发展方向进行了展望。     

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。     

单细胞转录组测序技术发展及应用

生物组织由多种异质性细胞组成,单个细胞之间的差异可能会对多细胞生物的功能产生深远影响。近年来开发的单细胞RNA-seq技术可以对单个细胞进行无偏、可重复、高分辨率和高通量的转录分析。相对于传统的群体细胞的转录组分析,单细胞RNA-seq技术从另外一个维度了解转录组信息,揭示生物组织的细胞构成、转录组动力学以及基因间的调节关系。随着细胞捕获、分子生物学和生物信息学等关键技术的发展和完善,其在生物学和医学领域的应用将会越来越广泛。该文对单细胞RNA-seq技术的发展历史和应用进行了阐述。     

单细胞转录组测序技术新进展及其在造血系统研究中的应用

单细胞测序技术凭借其能全面反映细胞群体异质性这一优势,近年来发展迅速.其中,单细胞转录组测序技术提供了在分子水平上对细胞作分类或表征的替代方法,在发育生物学、神经科学、血液学、免疫及癌症等研究领域均展示出了广泛的应用前景.本文总结了近年来单细胞转录组测序技术的主要发展趋势,并列举了该技术在造血系统中的应用.     

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。     

Single Cell Visualization of Yeast Gene Expression Shows Correlation of Epigenetic Switching between Multiple Heterochromatic Regions through Multiple Generations

Differences in gene expression between individual cells can be mediated by epigenetic regulation; thus, methods that enable detailed analyses of single cells are crucial to understanding this phenomenon. In this study, genomic silencing regions of Saccharomyces cerevisiae that are subject to epigenetic regulation, including the HMR, HML, and telomere regions, were investigated using a newly developed single cell analysis method. This method uses fluorescently labeled proteins to track changes in gene expression over multiple generations of a single cell. Epigenetic control of gene expression differed depending on the specific silencing region at which the reporter gene was inserted. Correlations between gene expression at the HMR-left and HMR-right regions, as well as the HMR-right and HML-right regions, were observed in the single-cell level; however, no such correlations involving the telomere region were observed. Deletion of the histone acetyltransferase GCN5 gene from a yeast strain carrying a fluorescent reporter gene at the HMR-left region reduced the frequency of changes in gene expression over a generation. The results presented here suggest that epigenetic control within an individual cell is reversible and can be achieved via regulation of histone acetyltransferase activity.     

A detailed study of the regulation and evolution of the two classes of patatin genes in Solanum tuberosum L.

The class-specific expression of patatin genes was investigated by analysing four new patatin genes. A class I patatin gene from cv. Berolina as well as a class I and two class II patatin genes from the monohaploid cultivar AM 80/5793 were isolated and partially sequenced. Sequence comparison indicates rearrangements as the major source for the generation of diversity between the different members of the classes. The expression of single genes was studied in potato plants transformed with chimaeric genes where the putative patatin promoters were fused to the GUS reporter gene. A detailed histochemical analysis reveals that both class I genes are expressed as the previously described class I patatin gene B33 from cv. Berolina [1], i.e. in the starch-containing cells of potato tubers and in sucrose-induced leaves. The class II gene pgT12 shows the same pattern as the previously described class II gene pgT2 [2], i.e. expression in root tips and in the vascular tissue of tubers, whereas no activity was detectable for pgT4. Thus the expression pattern of both classes of genes seems to be stable at least within or even between different cultivars.     

RNA Amplification in Brain Tissues

Recent developments in gene array technologies, specifically cDNA microarray platforms, have made it easier to try to understand the constellation of gene alterations that occur within the CNS. Unlike an organ that is comprised of one principal cell type, the brain contains a multiplicity of both neuronal (e.g., pyramidal neurons, interneurons, and others) and noneuronal (e.g., astrocytes, microglia, oligodendrocytes, and others) populations of cells. An emerging goal of modern molecular neuroscience is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subtypes and noneuronal cells. At present, an optimal methodology to assess gene expression is to evaluate single cells, either identified physiologically in living preparations, or by immunocytochemical or histochemical procedures in fixed cells in vitro or in vivo. Unfortunately, the quantity of RNA harvested from a single cell is not sufficient for standard RNA extraction methods. Therefore, exponential polymerase-chain reaction (PCR) based analyses and linear RNA amplifications, including a newly developed terminal continuation (TC) RNA amplification methodology, have been used in combination with single cell microdissection procedures to enable the use of cDNA microarray analysis within individual populations of cells obtained from postmortem brain samples as well as the brains of animal models of neurodegeneration.     

Single-Cell Gene Expression Analysis: Implications for Neurodegenerative and Neuropsychiatric Disorders

Technical and experimental advances in microaspiration techniques, RNA amplification, quantitative real-time polymerase chain reaction (qPCR), and cDNA microarray analysis have led to an increase in the number of studies of single-cell gene expression. In particular, the central nervous system (CNS) is an ideal structure to apply single-cell gene expression paradigms. Unlike an organ that is composed of one principal cell type, the brain contains a constellation of neuronal and noneuronal populations of cells. A goal is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and noneuronal cells. The unprecedented resolution afforded by single-cell RNA analysis in combination with cDNA microarrays and qPCR-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease states. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models as well as postmortem human brain tissues. This focused review illustrates the potential power of single-cell gene expression studies within the CNS in relation to neurodegenerative and neuropsychiatric disorders such as Alzheimer's disease (AD) and schizophrenia, respectively.     

A highly sensitive and accurate gene expression analysis by sequencing (“bead-seq”) for a single cell

Analyses of gene expressions in single cells are important for understanding detailed biological phenomena. Here, a highly sensitive and accurate method by sequencing (called “bead-seq”) to obtain a whole gene expression profile for a single cell is proposed. A key feature of the method is to use a complementary DNA (cDNA) library on magnetic beads, which enables adding washing steps to remove residual reagents in a sample preparation process. By adding the washing steps, the next steps can be carried out under the optimal conditions without losing cDNAs. Error sources were carefully evaluated to conclude that the first several steps were the key steps. It is demonstrated that bead-seq is superior to the conventional methods for single-cell gene expression analyses in terms of reproducibility, quantitative accuracy, and biases caused during sample preparation and sequencing processes.