A statistical framework for multiparameter analysis at the single-cell level

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.     

单细胞转录组高通量测序分析新进展

细胞异质性是生物组织的普遍特征。常规转录组测序(RNA-Seq)技术需要上万个细胞,所测结果实际上是一群细胞基因表达的平均值,所以难以鉴别细胞之间基因表达的异质性。单细胞RNA-Seq技术的分辨率精确至单个细胞,为辨别异质性群体中各种细胞类型的转录组特征提供了有力的工具。近年来单细胞RNA-Seq技术发展迅速,在方法学上包括cDNA扩增方法的多样化、对灵敏度和技术噪声的定量分析、浅覆盖高通量单细胞RNA-Seq方法和原位RNA-Seq技术等;在技术应用方面应用范围从早期胚胎发育扩大到组织器官发育、免疫和肿瘤等多个领域。文章对单细胞RNA-Seq在方法学和技术应用两方面的研究进展进行了详细阐述。     

Current challenges and solutions of de novo assembly

Background: Next-generation sequencing (NGS) technologies have fostered an unprecedented proliferation of high-throughput sequencing projects and a concomitant development of novel algorithms for the assembly of short reads. However, numerous technical or computational challenges in de novo assembly still remain, although many new ideas and solutions have been suggested to tackle the challenges in both experimental and computational settings.Results: In this review, we first briefly introduce some of the major challenges faced by NGS sequence assembly. Then, we analyze the characteristics of various sequencing platforms and their impact on assembly results. After that, we classify de novo assemblers according to their frameworks (overlap graph-based, de Bruijn graph-based and string graph-based), and introduce the characteristics of each assembly tool and their adaptation scene. Next, we introduce in detail the solutions to the main challenges of de novo assembly of next generation sequencing data, single-cell sequencing data and single molecule sequencing data. At last, we discuss the application of SMS long reads in solving problems encountered in NGS assembly.Conclusions: This review not only gives an overview of the latest methods and developments in assembly algorithms, but also provides guidelines to determine the optimal assembly algorithm for a given input sequencing data type.     

单细胞组学技术及其在植物保卫细胞研究中的应用

单细胞组学技术在动物研究中已经得到广泛应用,但在植物学领域尤其是保卫细胞研究中还处于起步阶段。由保卫细胞构成的气孔承担着植物生命过程中水分散发及气体交换大门的作用。将单细胞组学技术应用到保卫细胞功能解析中将有助于了解保卫细胞参与的基本生理过程。该文综述了植物单细胞组学技术的发展、保卫细胞研究现状及单细胞组学技术在植物保卫细胞研究中的初步应用,为借助该技术解决植物生物学中保卫细胞发育、代谢及对环境胁迫响应等基本问题提供研究思路和方法。     

A case study on the detailed reproducibility of a Human Cell Atlas project

Background: Reproducibility is a defining feature of a scientific discovery. Reproducibility can be at different levels for different types of study. The purpose of the Human Cell Atlas (HCA) project is to build maps of molecular signatures of all human cell types and states to serve as references for future discoveries. Constructing such a complex reference atlas must involve the assembly and aggregation of data from multiple labs, probably generated with different technologies. It has much higher requirements on reproducibility than individual research projects. To add another layer of complexity, the bioinformatics procedures involved for single-cell data have high flexibility and diversity. There are many factors in the processing and analysis of single-cell RNA-seq data that can shape the final results in different ways. Methods: To study what levels of reproducibility can be reached in current practices, we conducted a detailed reproduction study for a well-documented recent publication on the atlas of human blood dendritic cells as an example to break down the bioinformatics steps and factors that are crucial for the reproducibility at different levels. Results: We found that the major scientific discovery can be well reproduced after some efforts, but there are also some differences in some details that may cause uncertainty in the future reference. This study provides a detailed case observation on the on-going discussions of the type of standards the HCA community should take when releasing data and publications to guarantee the reproducibility and reliability of the future atlas. Conclusion: Current practices of releasing data and publications may not be adequate to guarantee the reproducibility of HCA. We propose building more stringent guidelines and standards on the information that needs to be provided along with publications for projects that evolved in the HCA program.     

Single-cell metagenomics: challenges and applications

With the development of high throughput sequencing and single-cell genomics technologies, many uncultured bacterial communities have been dissected by combining these two techniques. Especially, by simultaneously leveraging of single-cell genomics and metagenomics, researchers can greatly improve the efficiency and accuracy of obtaining whole genome information from complex microbial communities, which not only allow us to identify microbes but also link function to species, identify subspecies variations, study host-virus interactions and etc. Here, we review recent developments and the challenges need to be addressed in single-cell metagenomics, including potential contamination, uneven sequence coverage, sequence chimera, genome assembly and annotation. With the development of sequencing and computational methods, single-cell metagenomics will undoubtedly broaden its application in various microbiome studies.     

The hierarchy quorum sensing network in Pseudomonas aeruginosa

Pseudomonas aeruginosa causes severe and persistent infections in immune compromised individuals and cystic fibrosis sufferers. The infection is hard to eradicate as P. aeruginosa has developed strong resistance to most conventional antibiotics. The problem is further compounded by the ability of the pathogen to form biofilm matrix, which provides bacterial cells a protected environment withstanding various stresses including antibiotics. Quorum sensing (QS), a cell density-based intercellular communication system, which plays a key role in regulation of the bacterial virulence and biofilm formation, could be a promising target for developing new strategies against P. aeruginosa infection. The QS network of P. aeruginosa is organized in a multi-layered hierarchy consisting of at least four interconnected signaling mechanisms. Evidence is accumulating that the QS regulatory network not only responds to bacterial population changes but also could react to environmental stress cues. This plasticity should be taken into consideration during exploration and development of anti-QS therapeutics.     

Noise transmission during the dynamic pattern formation in fly embryos

Background: Developmental patterning is highly reproducible and accurate at the single-cell level during fly embryogenesis despite the gene expression noise and external perturbations such as the variation of the embryo length, temperature and genes. To reveal the underlying mechanism, it is very important to characterize the noise transmission during the dynamic pattern formation. Two hypotheses have been proposed. The “channel” scenario requires a highly reproducible input and an accurate interpretation by downstream genes. In contrast, the “filter” scenario proposes a noisy input and a noise filter via the cross-regulation of the downstream network. It has been under great debates which scenario the fly embryogenesis follows. Results: The first 3-h developmental patterning of fly embryos is orchestrated by a hierarchical segmentation gene network, which rewires upon the maternal to zygotic transition. Starting from the highly reproducible maternal gradients, the positional information is refined to the single-cell precision through the highly dynamical evolved zygotic gene expression profiles. Thus the fly embryo development might strictly fit into neither the originally proposed “filter” nor “channel” scenario. The controversy that which scenario the fly embryogenesis follows could be further clarified by combining quantitative measurements and modeling. Conclusions: Fly embryos have become one of the perfect model systems for quantitative systems biology studies. The underlying mechanism discovered from fly embryogenesis will deepen our understanding of the noise control of the gene network, facilitate searching for more efficient and safer methods for cell programming and reprogramming, and have the great potential for tissue engineering and regenerative medicine.     

Beyond toothpicks: new methods for isolating mutant bacteria

Over the past 50 years genetic analysis in microbiology has relied predominantly on selections and plate assays using chromogenic enzyme substrates - for example, X-gal assays for the detection of beta-galactosidase activity. Recent advances in fluorescent assays and high throughput screening technologies have paved the way for the rapid isolation of mutants that confer complex phenotypes and for the quantitative analysis of the evolution of new traits in bacterial populations. This Review highlights the power of novel single-cell screening technologies and their applications to genetics, evolution and the biotechnological uses of bacteria.     

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.     

信息素介导的西方蜜蜂与狄斯瓦螨互作研究进展

狄斯瓦螨是一种严重危害西方蜜蜂的体外寄生螨,是世界养蜂业的最大威胁.人们广泛采用化学方法防治狄斯瓦螨,但易引起狄斯瓦螨的抗药性、蜜蜂中毒和蜂产品药物残留等问题.为此,人们尝试了多种蜂螨绿色防控技术.其中利用蜜蜂信息素防治狄斯瓦螨是一个重要的研究方向.研究表明,狄斯瓦螨能利用蜜蜂信息素识别处于不同发育阶段的寄主,并对特定时期的寄主表现出高度的选择性.近年来,多种能作用于狄斯瓦螨的蜜蜂信息素相继被报道.这些信息素包括成蜂、蛹和幼虫信息素.有的信息素对狄斯瓦螨表现出驱避作用,有的则表现出引诱作用.本文对这些信息素的种类、主要组成成分、对狄斯瓦螨的作用等进行了综述,旨在为今后的研究与应用提供参考.     

从专刊论文管窥我国食用菌科学研究动态

1978年以来,我国食用菌产业科学技术研究成果斐然,近年来基础研究和应用基础研究在各个领域迅速展开,科学研究为食用菌产业发展提供了有力的技术支撑。本期《菌物学报》“食用菌专刊”共刊登了27篇论文,其中综述1篇,研究论文26篇。尽管这些论文尚不能代表我国食用菌科学研究的最高水平,但基本上反映了我国食用菌科学研究的最新动态。论文主要以香菇、侧耳类、金针菇、羊肚菌类、草菇等为研究材料,研究课题主要集中在食用菌组学分析、营养及生物活性物质和重要性状遗传等方面。总体而言,我国食用菌基础研究和应用基础研究相对滞后,它亟需学术界凝聚共识,鼎力支持以国家自然科学基金为代表的基础研究项目,提高我国食用菌基础研究水平。     

多基因联合揭示海南鲌的遗传结构与遗传多样性

海南鲌(Culter recurviceps)是我国华南地区重要经济鱼类, 由于受到近些年水利开发、过度捕捞、环境污染等诸多因素的影响, 其资源量快速下降, 亟需得到更多的关注和保护。为保护和合理开发海南鲌种质资源, 本研究采集了华南地区23个地理群体207尾海南鲌样本, 测定了2个线粒体基因(Cytb和ND2)并从Barcode of Life Data System数据库获得相对应线粒体COI基因, 结合多种分析方法(系统发育分析、分化时间估算、单倍型网状图、群体遗传分析和Mantel检验)对海南鲌的遗传结构和遗传多样性展开研究。系统发育分析和单倍型网状图表明华南地区海南鲌群体被分成3个谱系(I、II和III), 其中谱系I和III由珠江的群体组成, 谱系II由海南岛的群体组成。分化时间估算发现3个谱系之间的分化时间介于0.028-0.251 Ma之间, 表明华南地区更新世气候变化可能是造成海南鲌谱系分化的重要原因。群体遗传分析发现海南鲌群体之间存在极显著的遗传分化(F_ST = 0.511, P < 0.001), 并且符合距离隔离模式(R = 0.348, P = 0.0010)。群体动态历史分析表明, 海南鲌群体可能在0.010-0.025 Ma经历了群体扩张, 表明更新世的气候波动也影响了海南鲌的群体大小和分布。综上所述, 海南鲌群体由3个谱系组成, 更新世气候变化是导致3个谱系分化和影响海南鲌群体动态历史的重要因素。此外, 海南鲌群体之间的遗传分化也可能受到了空间距离的影响。     

JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters

With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps—low-dimensional discretized representation of datasets–that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering.     

Development of single-cell PCR methods for the Raphidophyceae

Many Raphidophytes are important algal bloom-forming species. Morphology-based identification of these species is often ambiguous, however, as many species are very similar in shape and size. To accurately detect the presence of these species in pre-bloom conditions, single-cell PCR is probably the most rapid and convenient method. However, direct single-cell PCRs with conserved primers are apparently not effective, probably due to the impermeability of the cell wall. We report here an effective detergent-based pre-PCR cell lysis method, which turned out to be a critical step for effective single-cell PCR of the Raphidophytes. Two PCR-based methods, nested SC-PCR and SC-RAPD, were evaluated. The nested SC-PCR involves two consecutive PCRs, the first of which is performed with the D1 and D2 primers (external primers) resulting in an amplification of a partial LSU rRNA gene. The second amplification is performed with primers targeting the LSU domain and specifically annealing to Chattonella ovata and Chattonella marina only. The SC-RAPD performed with the established random primers, RP1–RP4, produced unique haplotypes that could be exploited to differentiate the two Chattonella species. The assay was demonstrated to be sensitive, with the lowest detection limit of a single Raphidophyceae cell. The method developed is a valuable tool for the study of intra-specific variations of the Raphidophytes and represents a platform for further development of species-specific SC-RAPD for all members of the Raphidophyceae.