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。     

第三代测序技术在微生物研究中的应用

1977年Sanger发明的双末端终止法开启了测序之旅,而测序技术在30多年内不断革新。每种新技术的出现都有超过前代产品的独特之处,但也会不可避免的存在自身局限性,关键在于掌握每种技术的优缺点并加以合理应用。第三代测序技术是一种集高通量、快速度、长读长及低成本等多种优点于一身的新型测序技术,它的出现为基因组学、转录组学及DNA甲基化等研究注入了新活力。本文在介绍基本技术原理的基础上,着重概述了第三代测序技术在微生物研究中的应用,从而揭示了其广泛的应用前景。     

Genome rearrangements derived from homoeologous recombination following allopolyploidy speciation in coffee

Allopolyploidization is widespread and has played a major role in flowering plant diversification. Genomic changes are common consequences of allopolyploidization, but their mechanisms of occurrence and dynamics over time are still poorly understood. Coffea arabica, a recently formed allotetraploid, was chosen as a model to investigate genetic changes in allopolyploid using an approach that exploits next‐generation sequencing technologies. Genes affected by putative homoeolog loss were inferred by comparing the numbers of single‐nucleotide polymorphisms detected using RNA‐seq in individual accessions of C. arabica, and between accessions of its two diploid progenitor species for common sequence positions. Their physical locations were investigated and clusters of genes exhibiting homoeolog loss were identified. To validate these results, genome sequencing data were generated from one accession of C. arabica and further analyzed. Genomic rearrangements involving homoeologous exchanges appear to occur in C. arabica and to be a major source of genetic diversity. At least 5% of the C. arabica genes were inferred to have undergone homoeolog loss. The detection of a large number of homoeologous exchange events (HEEs) shared by all accessions of C. arabica strongly reinforces the assumption of a single allopolyploidization event. Furthermore, HEEs were specific to one or a few accessions, suggesting that HEE accumulates gradually. Our results provide evidence for the important role of HEE in allopolyploid genome evolution.     

Elucidating cryptic dynamics of Theileria communities in African buffalo using a high‐throughput sequencing informatics approach

1. Increasing access to next‐generation sequencing (NGS) technologies is revolutionizing the life sciences. In disease ecology, NGS‐based methods have the potential to provide higher‐resolution data on communities of parasites found in individual hosts as well as host populations.
2. Here, we demonstrate how a novel analytical method, utilizing high‐throughput sequencing of PCR amplicons, can be used to explore variation in blood‐borne parasite (Theileria—Apicomplexa: Piroplasmida) communities of African buffalo at higher resolutions than has been obtained with conventional molecular tools.
3. Results reveal temporal patterns of synchronized and opposite fluctuations of prevalence and relative abundance of Theileria spp. within the host population, suggesting heterogeneous transmission across taxa. Furthermore, we show that the community composition of Theileria spp. and their subtypes varies considerably between buffalo, with differences in composition reflected in mean and variance of overall parasitemia, thereby showing potential to elucidate previously unexplained contrasts in infection outcomes for host individuals.
4. Importantly, our methods are generalizable as they can be utilized to describe blood‐borne parasite communities in any host species. Furthermore, our methodological framework can be adapted to any parasite system given the appropriate genetic marker.
5. The findings of this study demonstrate how a novel NGS‐based analytical approach can provide fine‐scale, quantitative data, unlocking opportunities for discovery in disease ecology.

     

Potentials of single‐cell biology in identification and validation of disease biomarkers

Single‐cell biology is considered a new approach to identify and validate disease‐specific biomarkers. However, the concern raised by clinicians is how to apply single‐cell measurements for clinical practice, translate the message of single‐cell systems biology into clinical phenotype or explain alterations of single‐cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single‐cell gene sequencing in the identification and development of disease‐specific biomarkers, the definition and significance of single‐cell biology and single‐cell systems biology in the understanding of single‐cell full picture, the development and establishment of whole‐cell models in the validation of targeted biological function and the figure and meaning of single‐molecule imaging in single cell to trace intra‐single‐cell molecule expression, signal, interaction and location. We headline the important role of single‐cell biology in the discovery and development of disease‐specific biomarkers with a special emphasis on understanding single‐cell biological functions, e.g. mechanical phenotypes, single‐cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi‐dimensional, multi‐layer, multi‐crossing and stereoscopic single‐cell biology definitely benefits the discovery and development of disease‐specific biomarkers.     

二代测序应用于检测GT1-7细胞中KISS1和GnRH启动子的DNA甲基化状态的研究

目的:利用二代测序技术检测GT1-7细胞中KISS1和GnRH基因启动子范围内的甲基化状态,并用金标准的亚硫酸氢盐修饰后的克隆测序作为对照,比较二代测序与金标准克隆测序在研究DNA甲基化检测中的差别。方法:提取GT1-7细胞基因组DNA并进行亚硫酸氢盐处理。进行巢式PCR,将PCR产物进行二代测序。同时采用金标准的亚硫酸氢盐修饰后克隆测序的方法作为对照,对相同批次的PCR产物进行克隆测序。结果:PCR产物二代测序结果表明KISS1和GnRH两个基因的27个CpG甲基化位点信息完整,结果准确。挑取10个克隆进行一代测序结果表明序列无丢失,KISS1和GnRH两个基因的27个CpG甲基化位点信息完整。结论:利用高通量的二代测序技术能够有效的对DNA甲基化的PCR产物进行检测,二代测序和克隆测序都是研究DNA甲基化的有效方法,但前者与克隆测序相比每一个读取序列(reads)都相当于一个单克隆,且二代测序每个区段得到成百上千个reads,因此二代测序结果更加精确。     

Exploring Biotic Interactions Within Protist Cell Populations Using Network Methods

The study of diseased human cells and of cells isolated from the natural environment will likely be revolutionized by single cell genomics (SCG). Here, we used protein similarity networks to explore within‐ and between‐cell DNA differences from SCG data derived from six individual rhizarian cells related to Paulinella ovalis and proteins from the complete genome of another rhizarian, Bigelowiella natans. We identified shared and distinct DNA components within our SCG data and between P. ovalis and B. natans. We show that network properties such as assortativity and degree effectively discriminate genome features between SCG assemblies and that SCG data follow the power law with a small number of protein families dominating networks.