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本文引用的文献

1
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions.MetaCell:基于 K-近邻图分区的单细胞 RNA-seq 数据分析。
Genome Biol. 2019 Oct 11;20(1):206. doi: 10.1186/s13059-019-1812-2.
2
A Multicolor Fluorescence Hybridization Approach Using an Extended Set of Fluorophores to Visualize Microorganisms.一种使用扩展荧光团集来可视化微生物的多色荧光杂交方法。
Front Microbiol. 2019 Jun 19;10:1383. doi: 10.3389/fmicb.2019.01383. eCollection 2019.
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Unmasking cellular response of a bloom-forming alga to viral infection by resolving expression profiles at a single-cell level.通过在单细胞水平解析表达谱,揭示形成水华的藻类对病毒感染的细胞反应。
PLoS Pathog. 2019 Apr 24;15(4):e1007708. doi: 10.1371/journal.ppat.1007708. eCollection 2019 Apr.
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EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.EmptyDrops:用于区分基于液滴的单细胞 RNA 测序数据中的细胞和空液滴。
Genome Biol. 2019 Mar 22;20(1):63. doi: 10.1186/s13059-019-1662-y.
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The single-cell transcriptional landscape of mammalian organogenesis.哺乳动物器官发生的单细胞转录组图谱。
Nature. 2019 Feb;566(7745):496-502. doi: 10.1038/s41586-019-0969-x. Epub 2019 Feb 20.
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Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations.敏感的高通量单细胞 RNA-seq 揭示了酵母群体中克隆内转录物的相关性。
Nat Microbiol. 2019 Apr;4(4):683-692. doi: 10.1038/s41564-018-0346-9. Epub 2019 Feb 4.
7
Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation.单细胞成像和 RNA 测序揭示了裂殖酵母生长和适应过程中基因表达异质性的模式。
Nat Microbiol. 2019 Mar;4(3):480-491. doi: 10.1038/s41564-018-0330-4. Epub 2019 Feb 4.
8
Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue.病毒包容性单细胞 RNA 测序揭示了进展为重症登革热的分子特征。
Proc Natl Acad Sci U S A. 2018 Dec 26;115(52):E12363-E12369. doi: 10.1073/pnas.1813819115. Epub 2018 Dec 7.
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Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies.教程:单细胞 RNA 测序研究实验设计指南。
Nat Protoc. 2018 Dec;13(12):2742-2757. doi: 10.1038/s41596-018-0073-y.
10
A deadly dance: the choreography of host-pathogen interactions, as revealed by single-cell technologies.致命之舞:单细胞技术揭示的宿主-病原体相互作用的编舞。
Nat Commun. 2018 Nov 6;9(1):4638. doi: 10.1038/s41467-018-06214-0.

利用单细胞转录组学来理解微生物真核生物的功能状态和相互作用。

Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes.

机构信息

Institute of Plant and Microbial Biology, Academia Sinica, Taipei 11529, Taiwan.

Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2019 Nov 25;374(1786):20190098. doi: 10.1098/rstb.2019.0098. Epub 2019 Oct 7.

DOI:10.1098/rstb.2019.0098
PMID:31587645
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6792447/
Abstract

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue 'Single cell ecology'.

摘要

理解真核微生物的多样性和进化仍然是现代生物学的主要挑战之一。近年来,我们在新的真核物种和谱系的发现和系统发育定位方面取得了进展,这反过来又彻底改变了我们对真核生命之树的看法。但我们仍然不知道这些微生物真核生物的大多数生命周期、生理学和细胞状态,以及它们与其他生物的相互作用。在这里,我们讨论了高通量全基因组基因表达分析真核单细胞如何揭示原生生物生物学。首先,我们回顾了不同的单细胞转录组学方法,特别关注微生物真核生物的应用。然后,我们讨论了培养中的原生生物单细胞基因表达分析,以及从这些方法中可以学到什么。最后,我们设想将单细胞转录组学应用于原生生物群落,不仅可以研究群落成分,还可以研究不同细胞和生理状态的基因表达特征,以及种间相互作用的转录动态。总的来说,我们认为单细胞转录组学可以为我们理解微生物真核生物的生物学做出重大贡献。本文是关于“单细胞生态学”的讨论会议的一部分。