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。     

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.     

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.     

Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3′-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3′-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3′-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.     

Optimization of Single-Cell Electroporation Protocol for Forced Gene Expression in Primary Neuronal Cultures

The development and function of the central nervous system (CNS) are realized through interactions between many neurons. To investigate cellular and molecular mechanisms of the development and function of the CNS, it is thus crucial to be able to manipulate the gene expression of single neurons in a complex cell population. We recently developed a technique for gene silencing by introducing small interfering RNA into single neurons in primary CNS cultures using single-cell electroporation. However, we had not succeeded in forced gene expression by introducing expression plasmids using single-cell electroporation. In the present study, we optimized the experimental conditions to enable the forced expression of green fluorescent protein (GFP) in cultured cerebellar Purkinje neurons using single-cell electroporation. We succeeded in strong GFP expression in Purkinje neurons by increasing the inside diameter of micropipettes or by making the size of the original plasmid smaller by digestion and cyclizing it by ligation. Strong GFP expression in Purkinje neurons electroporated under the optimal conditions continued to be observed for more than 25 days after electroporation. Thus, this technique could be used for forced gene expression in single neurons to investigate cellular and molecular mechanisms of the development, function, and disease of the CNS.     

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...     

Analyzing Microbial Population Heterogeneity—Expanding the Toolbox of Microfluidic Single-Cell Cultivations

Recent research on population heterogeneity revealed fascinating insights into microbial behavior. In particular emerging single-cell technologies, image-based microfluidics lab-on-chip systems generate insights with spatio-temporal resolution, which are inaccessible with conventional tools. This review reports recent developments and applications of microfluidic single-cell cultivation technology, highlighting fields of broad interest such as growth, gene expression and antibiotic resistance and susceptibility. Combining advanced microfluidic single-cell cultivation technology for environmental control with automated time-lapse imaging as well as smart computational image analysis offers tremendous potential for novel investigation at the single-cell level. We propose on-chip control of parameters like temperature, gas supply, pressure or a change in cultivation mode providing a versatile technology platform to mimic more complex and natural habitats. Digital analysis of the acquired images is a requirement for the extraction of biological knowledge and statistically reliable results demand for robust and automated solutions. Focusing on microbial cultivations, we compare prominent software systems that emerged during the last decade, discussing their applicability, opportunities and limitations. Next-generation microfluidic devices with a high degree of environmental control combined with time-lapse imaging and automated image analysis will be highly inspiring and beneficial for fruitful interdisciplinary cooperation between microbiologists and microfluidic engineers and image analysts in the field of microbial single-cell analysis.     

VEGF inhibitor (Iressa) arrests histone deacetylase expression: single-cell cotransfection imaging cytometry for multi-target-multi-drug analysis

Multi-target-multi-drug approaches are needed to accelerate the process of drug discovery screening and to design efficient therapeutic strategies against diseases that involve alterations in multiple cellular targets. Herein we report single-cell cotransfection imaging cytometry to quantitatively screen drug-induced off-target effects. Vascular endothelial growth factor (VEGF) and histone deacetylase (HDAC) genes amplified from the genomic DNA were cloned in fluorescently tagged gene constructs (RFP-HDAC/YFP-VEGF). These gene constructs were cotransfected in HEK-293 cells to explore the possibility of off-target effects of 4-phenylbutyrate and Iressa on the expression of VEGF and HDAC through single-cell imaging cytometry. Iressa (10 μM) treatment at the time of cotransfection or 48 h after cotransfection of RFP-HDAC/YFP-VEGF plasmids in HEK-293 cells resulted in off-target effects on HDAC expression. These results suggest possible applications of Iressa in the treatment of diseases in which expression of both HDAC and VEGF should be inhibited. 4-Phenylbutyrate (2.0 mM) did not show any off-target effects on VEGF expression. The developed quantitative multicolor live single-cell cotransfection imaging can be employed to select better drug combinations for faster screening and greater accuracy in multi-target-multi-drug analysis by increasing the on-target/desired off-target effects and eliminating the undesirable off-target effects.     

An RNAi screening platform to identify secretion machinery in mammalian cells

Integrative approaches to study protein function in a cellular context are a vital aspect of understanding human disease. Genome sequencing projects provide the basic catalogue of information with which to unravel gene function, but more systematic applications of this resource are now necessary. Here, we describe and test a platform with which it is possible to rapidly use RNA interference in cultured mammalian cells to probe for proteins involved in constitutive protein secretion. Synthetic small interfering RNA molecules are arrayed in chambered slides, then incubated with cells and an assay for secretion performed. Automated microscopy is used to acquire images from the experiments, and automated single-cell analysis rapidly provides reliable quantitative data. In test arrays of 92 siRNA spots targeting 37 prospective membrane traffic proteins, our approach identifies 7 of these as being important for the correct delivery of a secretion marker to the cell surface. Correlating these findings with other screens and bioinformatic information makes these candidates highly likely to be novel membrane traffic machinery components.