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.     

转录因子与microRNA在基因表达调控中的功能联系及差异

转录因子和微RNA(microRNA)是最大的两类反式作用因子,它们是基因表达调控的重要调控因子.它们协调发挥调控作用,精细调控基因的表达,在细胞分化和动物生长发育过程中发挥重要的作用.随着对转录因子和microRNA研究的深入,人们发现转录因子和microRNA在基因表达调控网络中关系紧密,它们的分子作用机制有许多相似之处,两者都通过各自的顺式作用元件调控基因表达,且作用的方式类似.但转录因子和microRNA也存在不同之处,转录因子既可以激活基因表达,也可抑制基因表达,而microRNA主要是抑制基因表达.另外,转录因子调控区的复杂性一般高于microRNA的调控区域.本文综述了转录因子和microRNA的异同点,并提出了未来转录因子和microRNA的研究方向.     

MicroRNA与细胞信号转导通路研究进展

成熟的microRNA(miRNA)是一种长约22 nt的非编码RNA,通过与靶基因的3′非翻译区(3′UTR)结合来调控靶基因的表达。直至目前,在不同物种中发现的miRNA达6 397个。miRNA的发现为基因表达调控研究打开了新的窗口。目前研究者不仅证实miRNA在生物体生长、发育和疾病发生等过程中发挥着重要的作用,而且开始进一步探寻其发挥作用的分子机理。综述了miRNA与细胞信号转导途径之间的关系,从而有助于从基因水平上理解疾病的发生机制,为疾病的诊断、治疗提供依据。     

Single-cell Transcriptomic Analysis Reveals the Cellular Heterogeneity of Mesenchymal Stem Cells

Ex vivo-expanded mesenchymal stem cells(MSCs) have been demonstrated to be a heterogeneous mixture of cells exhibiting varying proliferative,multipotential,and immunomodulatory capacities.However,the exact characteristics of MSCs remain largely unknown.By singlecell RNA sequencing of 61,296 MSCs derived from bone marrow and Wharton’s jelly,we revealed five distinct subpopulations.The developmental trajectory of these five MSC subpopulations was mapped,revealing a differentiation path from stem-l...     

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing(sc RNA-seq)analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in sc RNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model(sc LM), a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, sc LM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. sc LM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that sc LM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of sc LM, we apply it to our in-house and public experimental sc RNA-seq datasets. sc LM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the sc LM method is available at https://github.com/QSong-github/sc LM.     

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)—one drawn upon the cell-points expressing the gene (the"foreground curve") and the other drawn upon all cell-points in the cluster (the"background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.     

仅基于RNA元件构建可诱导哺乳动物细胞基因表达的调控系统

大量的临床前和临床研究结果已表明基因治疗是理想的疾病治疗手段,然而如何实现治疗基因表达的精确调控仍然是研究人员面临的主要挑战。目前临床前研究常用的基因调控系统多基于控制转录,对反式转录激活因子和专门启动子元件的依赖限制了该系统的临床应用。最近,仅采用RNA元件构建的几种基因表达调控系统得到开发,其作用机制为核酶介导的RNA自我切割、RNA干扰、mRNA翻译启动或终止控制等。该类系统的调控活性由小分子配体反式控制,诱导基因表达的变化幅度可观,反应快速,在哺乳动物体内外均可实现。该系统结构模块化,调控活性可调节,可以克服现有转录调节系统的一些应用局限,对将来基因治疗的临床应用具有重要意义。     

Expression of Tyrosinase Gene in Transgenic Albino Mice: The Heritable Patterned Coat Colors

To elucidate the regulatory mechanism for tyrosinase gene expression in vivo, we microinjected a mouse tyrosinase minigene, mg-Qrs-J, into the fertilized eggs of BALB/c albino mice. As a result, we obtained six pigmented founder mice that exhibited non-standard coat color variations as well as the wild-type phenotype. These founder mice were subsequently crossed with BALB/c albino mice to establish the transgenic lines. As a consequence, two primary lines and five sublines have been obtained from four of the six founder mice. We found that not only uniformly pigmented phenotypes but also patterned phenotypes were inherited by their descendants. The possible underlying mechanism of the patterned phenotypes is discussed.     

DNA甲基化与基因表达调控研究进展

表观遗传修饰是指不改变DNA序列的、可遗传的对碱基和组蛋白的化学修饰,主要包括DNA甲基化、组蛋白修饰、染色质重塑以及非编码RNA等.表观遗传修饰是更高层次的基因表达调控手段.DNA甲基化是一种重要的表观遗传修饰,参与基因表达调控、基因印记、转座子沉默、X染色体失活以及癌症发生等重要生物学过程.近年来随着研究方法和技术的进步,全基因组DNA甲基化的研究广泛兴起,多个物种全基因组甲基化图谱被破译,全局水平对DNA甲基化的研究不仅利于在宏观层面上了解DNA甲基化的特性与规律,同时也为深入分析DNA甲基化的生物学功能与调控奠定了基础.结合最新研究进展综述DNA甲基化在基因组中的分布模式、规律以及和基因转录的关系等.     

竞争性内源RNA:一种全新的基因表达调控模式

竞争性内源RNA(ceRNA)假说是一种全新的基因表达调控模式:mRNA、假基因转录物和长链非编码RNA等转录物通过microRNA应答元件竞争结合相同的microRNA来调控各自的表达水平,从而影响细胞的功能.迄今为止,多家实验室已从生物信息学、细胞生物学和动物实验等层面验证了该假说.本文追溯了ceRNA假说提出的历程,讨论了ceRNA调控网络的影响因素,并提出了一些有待进一步完善的内容.ceRNA假说大大拓展了人类基因组中功能遗传信息的范畴,也为解析一些人类疾病发生的机制提供了新线索.     

MicroRNA调控造血干细胞发育

造血干细胞是目前研究最为深入的成体干细胞,是极富应用前景的研究领域,然而其维持自我更新以及多向分化潜能的分子机制尚不明.MicroRNA (miRNA)是一类崭新的调控性非编码小分子RNA,在监控生物体个体发育和细胞增殖、分化进程中起着重要作用.miRNA参与包括胚胎干细胞和多种成体干细胞的发育进程,人类造血干细胞及其发育过程中也存在特征性miRNA表达谱,参与调控造血干细胞发育进程,以miRNA为分子靶点的防治造血功能低下疾患的研究具有广阔的应用前景.     

Diversity of Gene Expression in Hepatocellular Carcinoma Cells

Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that compared to normal cells, there is a higher genetic diversity in tumor cells, while higher genetic diversity is associated with higher progression risks of tumor. We thus hypothesized that tumor diversity also holds true at the gene expression level. To test this hypothesis, we used t-test to compare the means of Simpson's diversity index for gene expression(SDIG) between tumor and non-tumor samples.We found that the mean SDIG in tumor tissues is significantly higher than that in the non-tumor or normal tissues(P 0.05) for most datasets. We also combined microarrays and next-generation sequencing data for validation. This cross-platform and cross-experimental validation greatly increased the reliability of our results.