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。     

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...     

单细胞转录组测序技术及在哺乳动物上的应用

哺乳动物的器官是由多种细胞类型组成,它们通过细胞间的相互作用来发出信号,以维持体内平衡和确保机体发育。传统转录组测序是以大量细胞或组织为研究样本,反映的是细胞总体上转录组特征,不能分析单个细胞的基因表达情况,而单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)技术的发展为揭示单个细胞转录组特征提供了有效方法。本文通过对scRNA-seq平台、scRNA-seq主要技术类型及scRNA-seq在哺乳动物上的应用展开综述。     

单细胞RNA测序数据分析方法研究进展

单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。     

《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。     

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。     

高通量RNA甲基化测序数据处理与分析研究进展

随着高通量测序技术快速发展,Me RIP-seq(methylated RNA immunoprecipitation sequencing)测序技术开启了RNA表观遗传学研究新局面,能够在全基因组范围内描述RNA甲基化.从Me RIP-seq高通量数据中挖掘RNA甲基化模式,有助于揭示m RNA甲基化在调控基因表达、剪切等方面所发挥的潜在功能,有效指导癌症的干预治疗.本文从Me RIP-seq测序原理出发,较全面地综述Me RIP-seq数据处理和分析方法研究现状,并对其所面临的计算问题进行讨论和展望.     

单细胞测序技术及应用进展

同一组织中的细胞往往被认为是具有相同状态的功能单位,传统的检测手段分析的是细胞群体的总体平均反应。然而通过对单个细胞的DNA或RNA进行测序,表明组织系统层面的功能是由异质性细胞构成的。单细胞测序以单个细胞为单位,通过全基因组或转录组扩增,进行高通量测序,能够揭示单个细胞的基因结构和基因表达状态,反映细胞间的异质性,在肿瘤、发育生物学、微生物学、神经科学等领域发挥重要作用,正成为生命科学研究的焦点。单细胞测序的难点是单个细胞的分离、单细胞基因组和转录组的扩增。本文主要介绍和分析了单细胞测序技术中常用的单细胞分离技术、单细胞基因组扩增技术和转录组扩增技术及其优缺点,并对当前已经取得成果的应用领域进行了阐述,为单细胞测序技术的研究与应用提供参考。     

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.     

Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.     

scMAGIC: accurately annotating single cells using two rounds of reference-based classification

Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.     

Exploration of Genetic Variations through Single‐cell Whole‐genome Sequencing in the Model Ciliate Tetrahymena thermophila

Ciliates are unicellular eukaryotes with separate germline and somatic genomes and diverse life cycles, which make them a unique model to improve our understanding of population genetics through the detection of genetic variations. However, traditional sequencing methods cannot be directly applied to ciliates because the majority are uncultivated. Single‐cell whole‐genome sequencing (WGS) is a powerful tool for studying genetic variation in microbes, but no studies have been performed in ciliates. We compared the use of single‐cell WGS and bulk DNA WGS to detect genetic variation, specifically single nucleotide polymorphisms (SNPs), in the model ciliate Tetrahymena thermophila. Our analyses showed that (i) single‐cell WGS has excellent performance regarding mapping rate and genome coverage but lower sequencing uniformity compared with bulk DNA WGS due to amplification bias (which was reproducible); (ii) false‐positive SNP sites detected by single‐cell WGS tend to occur in genomic regions with particularly high sequencing depth and high rate of C:G to T:A base changes; (iii) SNPs detected in three or more cells should be reliable (an detection efficiency of 83.4–97.4% was obtained for combined data from three cells). This analytical method could be adapted to measure genetic variation in other ciliates and broaden research into ciliate population genetics.