MATLAB 7.X生物信息工具箱的应用———基因表达图谱分析（4）*

基因表达图谱原则上可了解整体细胞基因表达的信息,是基因组功能分析的重要研究手段。MATLAB 7.X生物信息工具箱为基因表达谱数据的分析和处理提供了一个综合环境,通过众多统计函数和绘图函数的结合使用,过滤不合格的基因数据和噪声数据,从而对基因表达数据进行聚类分析和主成分分析,绘制相关的基因表达图谱,完成基因芯片数据表达图谱的分析,分析结果可视化程度高,图表清晰、直观。本文主要以酿酒酵母Saccharomyces cerevisiae为例,详细描述了利用MATLAB 7.X生物信息工具箱对其基因表达图谱进行分析的过程。     

整合素在细胞响应机械应力中的作用

机械应力在细胞生长、分化和基因表达等生理学过程和某些病理学过程中起了重要的作用.细胞粘附分子——整合素是机械信号转导中重要的跨膜分子.细胞通过整合素与胞外基质蛋白、细胞骨架蛋白以及聚焦粘附激酶等的反应,将感应的力信号转化为化学信号,从而调节细胞的生理机能,其中整合素与胞外基质蛋白之间的动态和特异性反应在细胞的机械信号转导过程中起了功能性作用.     

高等转基因动物基因发育特异性表达调控研究

一、前言最初研究高等动物基因表达调控采用的是细胞融合技术,然而细胞融合只能把两套基因组合并而并不能有选择地把特定的基因转入到细胞中。基因工程的诞生克服了如上的不足,推动了基因表达调控的研究。但在早期由于理论和技术的限制也只能把外源基因导入体外培养的动物细胞中,此法因局限于部分分化或完全分化的细胞中,故仍难于直接揭示胚胎发育过程中基因表达的动态分子机     

筛选差异表达基因方法的新进展

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选、削减杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT—PCR、RDA、SSH、cDNA微阵列(基因芯片)、基因表达的系统分析(SAGE)等技术。本着重对这些方法的优缺点及改进进行论述和评介,并对技术的发展趋势进行了分析。     

分离差异表达基因的方法

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选,扣除杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT-PCR,RDA,SSH,cDNA微阵列(基因芯片)等技术。这里着重对这些方法的优缺点及改进进行了论述和评介,并对技术的发展趋势进行了分析。     

从水分胁迫的识别到ABA积累的细胞信号转导

由于植物在生长和发育过程中不可避免地要遭受各种环境胁迫的影响,植物只有通过对环境胁迫的快速感知和主动反应才得以生存和发展.植物这种对环境胁迫的快速感知和主动反应体现在环境胁迫下植物可以通过一系列基因的表达调控来实现各种抗逆的生理生化反应.虽然得以鉴定的水分胁迫应答基因越来越多,但其中只有极少的基因在抗逆中的基本功能已得到初步认识.从细胞对水分胁迫原初信号的感知到基因表达调控包括了一系列复杂的细胞逆境信息传递过程.脱落酸(abscisic acid, ABA)作为重要的细胞逆境信号物质介导了一系列基因表达,因此从细胞对水分胁迫原初信号的感知到编码ABA生物合成关键酶基因的表达是一条最为关键的细胞逆境信息传递途径.逆境应答基因功能的鉴定以及对整个细胞信号传递过程中详尽的分子机制的了解无疑是今后最有趣的也是最为重要的研究课题.     

干旱胁迫与ABA的信号转导

植物经历干旱胁迫时,ABA被普遍认为是一种干旱信号而传递干旱信息.在干旱信号ABA的转导过程中,从ABA的被感知到保卫细胞发生变化引起气孔关闭以及ABA诱导的基因表达都经历了复杂的变化.本文对ABA的信号转导过程进行了综述.     

少突胶质细胞发育分化的表观遗传学调控研究进展

少突胶质细胞的发育分化是由遗传的和后生的机制共同参与调控的一系列动态过程,其中,对于后生调控机制的研究称为表观遗传学。既往对少突胶质细胞的研究主要集中在相关基因本身的特性研究。近年来,关于寻址组蛋白修饰的研究使我们对少突胶质细胞发育和衰老过程中基因表达的后生调控有了新的认识。这些理论将有助于我们更好地理解脱髓鞘及衰老后髓鞘修复障碍的原因和防治途径。     

表观遗传修饰与干细胞分化调控

干细胞具有自我更新和多种分化潜能的特性。干细胞向分化细胞的转变涉及到基因表达模式的改变,与自我更新有关的基因关闭．与细胞特化有关的基因激活。表观遗传调控机制,包括DNA甲基化、组蛋白修饰和微RNA（microRNA）介导的基因调控,在多个层面上控制发育过程中基因表达。近年研究表明,动态的表观遗传调控机制在干细胞自我更新和分化中起关键作用。     

基因表达系列分析技术在真菌功能基因组学中的应用

基因表达系列分析( SAGE)是一种在mRNA水平上高通量、快速、灵敏分析细胞或组织基因表达信息,并在基因组学研究中广泛应用的技术.该技术不仅能够全面地分析特定组织或细胞表达的基因,比较不同时空条件下基因表达的差异,还可以在全基因组范围内获得基因的表达谱,从而发现新基因.综述基因表达系列分析技术在材料用量、标签长度、技术流程和标签测序等方面的研究进展及该技术在病原真菌、工业真菌和食用真菌功能基因组学中的应用.     

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.     

Inference of trajectory presence by tree dimension and subset specificity by subtree cover

The complexity of biological processes such as cell differentiation is reflected in dynamic transitions between cellular states. Trajectory inference arranges the states into a progression using methodologies propelled by single-cell biology. However, current methods, all returning a best trajectory, do not adequately assess statistical significance of noisy patterns, leading to uncertainty in inferred trajectories. We introduce a tree dimension test for trajectory presence in multivariate data by a dimension measure of Euclidean minimum spanning tree, a test statistic, and a null distribution. Computable in linear time to tree size, the tree dimension measure summarizes the extent of branching more effectively than globally insensitive number of leaves or tree diameter indifferent to secondary branches. The test statistic quantifies trajectory presence and its null distribution is estimated under the null hypothesis of no trajectory in data. On simulated and real single-cell datasets, the test outperformed the intuitive number of leaves and tree diameter statistics. Next, we developed a measure for the tissue specificity of the dynamics of a subset, based on the minimum subtree cover of the subset in a minimum spanning tree. We found that tissue specificity of pathway gene expression dynamics is conserved in human and mouse development: several signal transduction pathways including calcium and Wnt signaling are most tissue specific, while genetic information processing pathways such as ribosome and mismatch repair are least so. Neither the tree dimension test nor the subset specificity measure has any user parameter to tune. Our work opens a window to prioritize cellular dynamics and pathways in development and other multivariate dynamical systems.     

Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy

Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1-2 d for progressing through the analysis procedure.     

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or"tipping point"of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regu-latory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the"dark genes"that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes. The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.     

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.