单细胞尺度下的微生物学研究:意义与方法

最近研究表明,即便是处于同一种群中的微生物细胞,在基因转录和翻译、蛋白活性、以及代谢物丰度等多个水平都可能存在显著差异,说明微生物细胞间存在着多个层次上的异质性;同时,传统微生物学研究方法需要将所研究的微生物对象在实验室实现再次培养,然后对纯培养的微生物种群进行研究,这样往往造成实验室的研究结果无法真实地反映微生物细胞在自然界中的原始状态,急需发展新的原位研究手段;此外,自然界中的微生物目前只有极少部分可以在实验室中进行培养,仍有大量微生物无法通过传统方法进行发掘和研究。单细胞尺度微生物学为解决这些微生物学研究中的重要挑战提供了一种新的策略和技术思路,有望帮助我们更为直观、深入地了解每个细胞内部的状态,以及其在自然界的生理生态功能。本文对单细胞尺度微生物学研究的意义以及当前单细胞尺度微生物学的研究方法,特别是新兴的微生物单细胞组学方法进行了介绍。     

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves

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Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility

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Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution

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单细胞转录组研究进展

单细胞转录组分析以单个细胞为特定研究对象,提取mRNA进行逆转录、放大和高通量测序分析,能揭示该细胞内整体水平的基因表达状态和基因结构信息,准确反映细胞间的异质性,深入理解其基因型和表型之间的相互关系,在发育生物学、基础医学、临床诊断和药物开发等领域都发挥重要作用．本文主要介绍了单细胞转录组分析的特点和技术发展历史以及常用研究策略和不同技术的优缺点,并就其面临挑战和未来发展前景进行了讨论,为该技术的进一步研究与应用提供参考．     

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

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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.     

Whole-exome sequencing and microRNA profiling reveal PI3K/AKT pathway’s involvement in juvenile myelomonocytic leukemia

Background: Clinical studies and genetic analyses have revealed that juvenile myelomonocytic leukemia (JMML) is caused by somatic and/or germline mutations of genes involved in the RAS/MAPK signalling pathway. Given the vastly different clinical prognosis among individual patients that have had this disease, mutations in genes of other pathways may be involved. Methods: In this study, we conducted whole-exome and cancer-panel sequencing analyses on a bone marrow sample from a 2-year old juvenile myelomonocytic leukemia patient. We also measured the microRNA profile of the same patient’s bone marrow sample and the results were compared with the normal mature monocytic cells from the pooled peripheral blood. Results: We identified additional novel mutations in the PI3K/AKT pathway and verified with a cancer panel targeted sequencing. We have confirmed the previously tested PTPN11 gene mutation (exon 3 181G>T) in the same sample and identified new nonsynonymous mutations in NTRK1, HMGA2, MLH3, MYH9 and AKT1 genes. Many of the microRNAs found to be differentially expressed are known to act as oncogenic MicroRNAs (onco-MicroRNAs or oncomiRs), whose target genes are enriched in the PI3K/AKT signalling pathway. Conclusions: Our study suggests an alternative mechanism for JMML pathogenesis in addition to RAS/MAPK pathway. This discovery may provide new genetic markers for diagnosis and new therapeutic targets for JMML patients in the future.     

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30–50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.     

Differential expression analyses for single-cell RNA-Seq: old questions on new data

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.     

Metabolic potential of a single cell belonging to one of the most abundant lineages in freshwater bacterioplankton

Actinobacteria within the acI lineage are often numerically dominating in freshwater ecosystems, where they can account for >50% of total bacteria in the surface water. However, they remain uncultured to date. We thus set out to use single-cell genomics to gain insights into their genetic make-up, with the aim of learning about their physiology and ecological niche. A representative from the highly abundant acI-B1 group was selected for shotgun genomic sequencing. We obtained a draft genomic sequence in 75 larger contigs (sum=1.16 Mb), with an unusually low genomic G+C mol% (∼42%). Actinobacteria core gene analysis suggests an almost complete genome recovery. We found that the acI-B1 cell had a small genome, with a rather low percentage of genes having no predicted functions (∼15%) as compared with other cultured and genome-sequenced microbial species. Our metabolic reconstruction hints at a facultative aerobe microorganism with many transporters and enzymes for pentoses utilization (for example, xylose). We also found an actinorhodopsin gene that may contribute to energy conservation under unfavorable conditions. This project reveals the metabolic potential of a member of the global abundant freshwater Actinobacteria.