单细胞转录组数据批次效应评测的研究进展

单细胞转录组测序(Single cell RNA sequencing,ScRNA seq)是一种变革性的生物技术,以前所未有的高分辨率来解析组织复杂性,解决了普通转录组测序(Bulk RNA sequencing)无法回答的问题。但单细胞数据的高通量及复杂性给分析带来极大难度,批次效应(Batch effects,BEs)的处理便是主要挑战之一。批次效应是高通量生物数据分析中的技术性偏倚,其来源及处理具有高复杂性和研究依赖性。根据组织类型、测序技术及实验设计的不同,测序数据需采用不同的评估、分析、测量及处置流程来实现有效的批次效应处理。评测批次效应在单细胞数据分析中极易被忽略,但却有助于判断批次效应的来源、对数据变异的解释度、对数据分析结果的影响度及处理方法,是有效处理批次效应的基础。因此,本篇综述聚焦单细胞转录组数据的批次效应,分别论述批次效应的概念、与普通转录组批次效应的区别、评测方法及面临的挑战,并对未来发展做出展望。     

昆虫RNA-Seq数据的分析流程

随着高通量RNA测序(RNA-Seq)技术的发展和测序成本迅速下降,RNA-Seq技术已经成为生物学研究的重要工具,为生物学家全面地了解和研究转录组提供了机遇。高通量测序具有读长短、存在一定比例的测序错误、数据量大等特点,因此RNA-Seq数据分析与基因组分析和传统的EST数据分析有所不同。本文通过介绍不同的测序平台、原始数据产生和低质量数据过滤的计算流程,对短序列比对、转录组拼接、功能注释、以及差异表达分析进行了研究和分析,最后对RNA-Seq在昆虫学研究中的应用进行了综述,并对RNA-Seq技术进行了总结和展望。     

下一代测序技术数据分析进展

<正>简要综述了序列读取后对基因组的定位和比对后的分析进展,结合RNA测序、miRNA测序、捕获测序探讨了基因组数据分析及其应用。下一代测序(NGS)技术正在极大地改变生物学家进行研究的方式。不同的NGS平台如Illumina Solexa、Life Tech SOLiD和Roche 454提供了前所未有的大规模并行测序能力,短时间内对生物样     

地中海富盐菌中非编码RNA的鉴定与分析

【目的】通过转录组高通量测序技术(即RNA-seq),结合生物信息学分析和分子生物学方法,在组学水平鉴定极端嗜盐菌中可能的非编码RNA(nc RNA)。【方法】将培养至对数中期的地中海富盐菌在不同盐浓度下处理30分钟,提取RNA,进行链特异的转录组测序和5′端区分的转录组测序,通过生物信息学分析在全基因组范围内鉴定nc RNA,预测其转录边界;然后通过Northern blot和环化RNA反转录聚合酶链式反应(CR-RT-PCR)对部分预测的nc RNA进行实验验证。【结果】比较两种RNA-seq技术在不同培养条件下的RNA测序结果和对转录单元的精细分析,共鉴定到105个高可信度的nc RNA,并发现4个在不同盐度下表达差异较大的nc RNA,通过Northern blot和CR-RT-PCR验证了inc RNA1436和inc RNA1903的表达情况、转录本、转录起始位点及终止位点等。【结论】首次在组学水平鉴定了地中海富盐菌中的nc RNA,不同盐浓度刺激下部分nc RNA的转录差异暗示其有可能参与地中海富盐菌对盐胁迫的适应,高可信度nc RNA的组学发现为今后全面开展嗜盐古菌nc RNA的功能机制研究提供了基础数据及重要的切入点。     

《自然一方法学》：研究探讨RNA-seq数据分析方法

高通量RNA测序（RNA—seq）有望描绘出转录组的整体图像,实现样本内所有基因及其亚型的完整注释和定量。随着测序价格的不断下降。以及个人化测序仪的上市,更多的实验室有机会尝试这种新技术。然而,测序之后的数据分析才是真正的挑战。     

PCR扩增循环数对细菌群落多样性测序分析的影响

运用高通量测序技术分析复杂样品中微生物群落组成及变化趋势,已经成为目前微生物研究领域的热点之一。本研究以复杂土壤样品和应用范围较广的瘤胃食糜样品为对象,选取20、25和30三个扩增循环数分别对样品的16S r RNA基因的V3区进行扩增,然后进行文库构建和测序。最后通过数据分析比较不同的扩增循环数对细菌多样性测定结果的影响。结果表明,扩增循环数越多,捕获到的细菌数量和种类越多;但并非循环数越多,群落中的微生物组成比例最优。整体来看,当扩增循环数为25时,样品中物种的数量和组成是最优的。     

基于新一代高通量测序的环境微生物转录组学研究进展

环境微生物转录组学是一门新兴学科,它以复杂环境样品中的微生物mRNA为研究对象,利用近年兴起的RNA-Seq高通量测序技术,在整体水平上对环境微生物的基因表达水平和调控规律进行研究.本文概述了环境微生物转录组研究从样品的采集保存、RNA提取、mRNA的富集、cDNA合成直到高通量测序及数据分析的基本流程.总结了该技术面临的主要瓶颈:环境样品mRNA含量低、腐植酸等干扰杂质多、rRNA去除程度有限.针对RNA的提取、纯化以及mRNA的富集这些重点步骤,详细阐述了近年来在提高mRNA的得率与纯度上的方法学进展.重点介绍了高通量测序数据的处理及分析方法,从测序数据的质量控制、序列组装、rRNA的鉴定及去除、功能基因注释及分类到差异表达基因的鉴定.最后总结了近年来环境微生物转录组学在新基因的发现、不同环境条件下微生物的基因表达及调控规律研究、有机物的代谢路径分析等3个主要研究领域的广泛应用.随着测序技术及生物信息学分析工具的发展进步,环境微生物转录组学将具有更广阔的应用前景.     

医学研究生"高通量测序技术"应用能力的培养

高通量测序技术目前已广泛的应用于临床研究领域。与传统测序方式相比,该技术具有通量高、耗时短、成本低等特点。研究生教育中对高通量测序技术的新进展及其在疾病研究、临床诊断等方面的应用方面的介绍较少。培养医学研究生对高通量测序技术的应用能力,可以增强研究生对高通量测序技术的方法、原理、应用范围、数据分析的理解,为医学研究生应用高通量测序技术发现及解决临床问题打下一定的基础。     

16S rRNA基因在微生物生态学中的应用

16S rRNA(Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物(Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。     

Mitochondrial respiratory chain function promotes extracellular matrix integrity in cartilage

Energy metabolism and extracellular matrix (ECM) function together orchestrate and maintain tissue organization, but crosstalk between these processes is poorly understood. Here, we used single-cell RNA-Seq (scRNA-Seq) analysis to uncover the importance of the mitochondrial respiratory chain for ECM homeostasis in mature cartilage. This tissue produces large amounts of a specialized ECM to promote skeletal growth during development and maintain mobility throughout life. A combined approach of high-resolution scRNA-Seq, mass spectrometry/matrisome analysis, and atomic force microscopy was applied to mutant mice with cartilage-specific inactivation of respiratory chain function. This genetic inhibition in cartilage results in the expansion of a central area of 1-month-old mouse femur head cartilage, showing disorganized chondrocytes and increased deposition of ECM material. scRNA-Seq analysis identified a cell cluster–specific decrease in mitochondrial DNA–encoded respiratory chain genes and a unique regulation of ECM-related genes in nonarticular chondrocytes. These changes were associated with alterations in ECM composition, a shift in collagen/noncollagen protein content, and an increase of collagen crosslinking and ECM stiffness. These results demonstrate that mitochondrial respiratory chain dysfunction is a key factor that can promote ECM integrity and mechanostability in cartilage and presumably also in many other tissues.     

scdNet: a computational tool for single-cell differential network analysis

Background

Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks.

Results

Addressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). The tool features a sample size adjustment of gene-gene correlation, comparison of inter-state correlations, and construction of differential networks. A simulation analysis demonstrated the power of scdNet in the analyses of sparse scRNA-Seq data matrices, with low requirement on the sample size, high computation efficiency, and tolerance of sequencing noises. Applying the tool to analyze two datasets of single circulating tumor cells (CTCs) of prostate cancer and early mouse embryos, our data demonstrated that differential gene regulation plays crucial roles in anti-androgen resistance and early embryonic development.

Conclusions

Overall, the tool is widely applicable to datasets generated by the emerging technology to bring biological insights into tumor heterogeneity and other studies. MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet.

     

Antibody variable-region sequencing as a method for hybridoma cell-line authentication

Cross-contamination and misidentification of various cell lines is a widespread problem that can lead to spurious scientific conclusions. DNA fingerprinting is a powerful identification technique, which can be effectively used for the authentication of human cell lines. In contrast to human cancer cell lines, little attention has so far been given to establishing authentication practices for hybridoma cell lines. Since the majority of hybridomas stem from inbred animals, they have high genetic uniformity, which reduces the applicability of DNA fingerprinting. In the present study, we propose antibody variable-region sequencing as a method of choice for hybridoma cell-line authentication. This method focuses on the most diverse characteristic of hybridoma cell lines and thereby achieves a very high discriminatory power. The sequencing of light-chain variable regions has proven to be especially suitable for routine use because of its high success rate. Two other possible authentication methods, random amplified polymorphic DNA analysis and two-dimensional gel electrophoresis, were also examined. Compared to these and other methods that can be used for discrimination between hybridoma cell lines, variable-region sequencing has many advantages, most notably those of a very high discriminatory power, insensitivity to changes in experimental conditions, simple data analysis, and accessibility to most laboratories. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Simon Koren and Miha Kosmač contributed equally to this work.     

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。     

Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing

Unsatisfied clinical outcome drives to better understand hepatic carcinogenesis, microenvironment and escape of immune surveillance in hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-Seq) has generated enormous data to pinpoint pathophysiologic alterations in tumor microenvironment (TME) or trace lineage development in cancer stem cells (CSCs), circulating tumor cells (CTCs), and subsets of immune cells, such as exhausting T cells, tumor-associated macrophages (TAMs), dendritic cells or other lineages. New insights have significantly advanced current understanding in progression, poor responses to molecular-targeted therapeutics or immune checkpoint inhibitors, metastasis in both basic research and clinical practice. The present review intends to cover a basic workflow of the scRNA-seq technology, existing limitations and improvement areas. Moreover, in-depth understanding in TME, exhausting T cells, CSCs, CTCs, tumor-associated macrophages, dendritic cells in HCC facilitates implementation of personalized and precise therapy in an era of availability with an array of systemic regimens.     

Secondary Analysis of the NCI-60 Whole Exome Sequencing Data Indicates Significant Presence of Propionibacterium acnes Genomic Material in Leukemia (RPMI-8226) and Central Nervous System (SF-295, SF-539, and SNB-19) Cell Lines

The NCI-60 human tumor cell line panel has been used in a broad range of cancer research over the last two decades. A landmark 2013 whole exome sequencing study of this panel added an exceptional new resource for cancer biologists. The complementary analysis of the sequencing data produced by this study suggests the presence of Propionibacterium acnes genomic sequences in almost half of the datasets, with the highest abundance in the leukemia (RPMI-8226) and central nervous system (SF-295, SF-539, and SNB-19) cell lines. While the origin of these contaminating bacterial sequences remains to be determined, observed results suggest that computational control for the presence of microbial genomic material is a necessary step in the analysis of the high throughput sequencing (HTS) data.