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文献精选 > 高引权威"单细胞测序"文献

Single-cell sequencing techniques from individual to multiomics analyses. ​

文献信息 ​

DOI10.1038/s12276-020-00499-2
PMID32929221
期刊Experimental & molecular medicine
影响因子12.9
JCR 分区Q1
发表年份2020
被引次数137
关键词单细胞测序, 多组学分析, 转录组学, 表观基因组学, 计算整合方法
文献类型Journal Article, Research Support, Non-U.S. Gov't, Review
ISSN1226-3613
页码1419-1427
期号52(9)
作者Yukie Kashima, Yoshitaka Sakamoto, Keiya Kaneko, Masahide Seki, Yutaka Suzuki, Ayako Suzuki

一句话小结 ​

本研究回顾了单细胞测序技术在基因组学、表观基因组学和转录组学中的应用，重点介绍了多组学数据整合的方法及其实验实例。通过这些技术，我们能够深入了解单细胞的分子特征及其在疾病中的相关性，推动精准医学的发展。

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单细胞测序 · 多组学分析 · 转录组学 · 表观基因组学 · 计算整合方法

摘要 ​

在这里，我们回顾了单细胞测序技术在单细胞个体和多组学分析中的应用。我们主要描述了单细胞基因组学、表观基因组学和转录组学的方法，以及它们的应用实例。对于多层数据集的整合，例如来源于单细胞RNA测序的转录组数据和来源于单细胞ATAC-seq的染色质可及性数据，存在多种计算整合方法。我们还描述了用于同时测量两个或多个组学层的单细胞实验方法。通过利用大量单细胞测序技术和积累的数据集，我们能够详细了解每个细胞的基本分子特征以及与疾病相关的特征。

英文摘要 ​

Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

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主要研究问题 ​

  1. 单细胞测序技术在不同疾病模型中的应用效果如何？
  2. 如何选择适合特定研究目标的单细胞组学技术？
  3. 在多组学分析中，数据整合的方法有哪些优势和挑战？
  4. 单细胞转录组测序与单细胞ATAC-seq结合的具体案例有哪些？
  5. 当前单细胞测序技术的最新进展对临床研究有何影响？

核心洞察 ​

研究背景和目的 ​

单细胞测序技术的快速发展使得研究者能够观察单个细胞的多层次状态，包括基因组、表观基因组和转录组的异质性。本文旨在回顾单细胞测序技术，探讨其在个体和多组学分析中的应用，尤其是在疾病相关的分子特征研究中的重要性。

主要方法/材料/实验设计 ​

本研究综述了多种单细胞测序技术，包括单细胞RNA测序（scRNA-seq）、单细胞基因组测序和单细胞表观基因组测序。技术路线的流程图如下：

Mermaid diagram

  1. 单细胞RNA测序：通过不同的WTA方法（如Smart-seq、Drop-seq等）实现对单细胞转录组的高效测序。文中详细介绍了细胞解离、逆转录和cDNA扩增等步骤。

  2. 单细胞基因组测序：使用全基因组扩增（WGA）技术分析个体细胞的遗传异质性，方法包括MDA、DOP-PCR等。

  3. 单细胞表观基因组测序：探讨了DNA甲基化、染色质可及性等表观遗传特征的测量方法，如ATAC-seq和ChIP-seq。

关键结果和发现 ​

  • 技术发展：单细胞测序技术的不断进步使得在高通量、低成本的情况下，可以同时处理数千个单细胞的数据。
  • 应用实例：研究表明，scRNA-seq可以揭示肿瘤微环境中的细胞异质性，并为癌症免疫治疗提供潜在的生物标志物。
  • 数据整合：通过计算方法（如Seurat和LIGER），研究者能够整合不同层次的单细胞组学数据，以获得更全面的生物学理解。

主要结论/意义/创新性 ​

单细胞测序技术为解析细胞异质性提供了强大的工具，尤其是在理解疾病机制和细胞发育过程中具有重要意义。随着技术的进步，未来可能实现单细胞多组学的整合分析，进一步推动个性化医疗的发展。

研究局限性和未来方向 ​

  • 局限性：当前技术仍面临数据处理复杂、成本高和样本捕获效率低等挑战。
  • 未来方向：需要开发新的自动化平台和算法，以实现更高效的单细胞多组学分析。此外，空间转录组学技术的进步将有助于在组织切片中保留空间信息，从而更好地理解细胞间的相互作用和微环境的影响。

参考文献 ​

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引用本文的文献 ​

  1. Opportunities for Single-Cell Sequencing Technologies and Data Science. - Lisa Maria Mustachio;Jason Roszik - Cancers (2020)
  2. Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens. - Yukie Kashima;Yosuke Togashi;Shota Fukuoka;Takahiro Kamada;Takuma Irie;Ayako Suzuki;Yoshiaki Nakamura;Kohei Shitara;Tatsunori Minamide;Taku Yoshida;Naofumi Taoka;Tatsuya Kawase;Teiji Wada;Koichiro Inaki;Masataka Chihara;Yukihiko Ebisuno;Sakiyo Tsukamoto;Ryo Fujii;Akihiro Ohashi;Yutaka Suzuki;Katsuya Tsuchihara;Hiroyoshi Nishikawa;Toshihiko Doi - Scientific reports (2021)
  3. From Transcriptomics to Treatment in Inherited Optic Neuropathies. - Michael James Gilhooley;Nicholas Owen;Mariya Moosajee;Patrick Yu Wai Man - Genes (2021)
  4. Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models. - P Jonathan Li;Jeroen P Roose;David M Jablons;Johannes R Kratz - Cancers (2021)
  5. The role of epigenetic modifications for the pathogenesis of Crohn's disease. - M Hornschuh;E Wirthgen;M Wolfien;K P Singh;O Wolkenhauer;J Däbritz - Clinical epigenetics (2021)
  6. Novel Tools and Investigative Approaches for the Study of Oligodendrocyte Precursor Cells (NG2-Glia) in CNS Development and Disease. - Christophe Galichet;Richard W Clayton;Robin Lovell-Badge - Frontiers in cellular neuroscience (2021)
  7. Temporal single-cell regeneration studies: the greatest thing since sliced pancreas? - Juan Domínguez-Bendala;Mirza Muhammad Fahd Qadir;Ricardo Luis Pastori - Trends in endocrinology and metabolism: TEM (2021)
  8. Single cell RNA sequencing approaches to cardiac development and congenital heart disease. - Tahmina Samad;Sean M Wu - Seminars in cell & developmental biology (2021)
  9. Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology. - Leho Tedersoo;Mads Albertsen;Sten Anslan;Benjamin Callahan - Applied and environmental microbiology (2021)
  10. Single-Cell Mapping of GLP-1 and GIP Receptor Expression in the Dorsal Vagal Complex. - Mette Q Ludwig;Petar V Todorov;Kristoffer L Egerod;David P Olson;Tune H Pers - Diabetes (2021)

... (127 更多 篇文献)

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