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文献精选 > 高引权威"单细胞测序"文献

A Single-Cell Sequencing Guide for Immunologists. ​

文献信息 ​

DOI10.3389/fimmu.2018.02425
PMID30405621
期刊Frontiers in immunology
影响因子5.9
JCR 分区Q1
发表年份2018
被引次数107
关键词10X基因组铬技术, MARS-seq, SMART-seq, 树突状细胞, Fluidigm C1
文献类型Journal Article, Research Support, Non-U.S. Gov't, Review
ISSN1664-3224
页码2425
期号9()
作者Peter See, Josephine Lum, Jinmiao Chen, Florent Ginhoux

一句话小结 ​

本研究比较了四种常用的单细胞测序（scRNA-seq）平台，分析了它们在不同实验中的优缺点，以帮助用户选择适合的方案。研究结果为整合不同平台数据集和利用无偏生物信息学方法识别未知单细胞群体提供了重要参考，推动了免疫学领域的发展。

在麦伴科研 (maltsci.com) 搜索更多文献 ​

10X基因组铬技术 · MARS-seq · SMART-seq · 树突状细胞 · Fluidigm C1

摘要 ​

近年来，单细胞测序（scRNA-seq）技术在免疫学领域得到了迅速发展。随着可用技术的种类繁多，用户在选择最适合其生物学问题的scRNA-seq方案/平台时面临越来越大的困难。在此，我们比较了四种常用scRNA-seq平台的优缺点，以明确它们在不同实验应用中的适用性。我们还探讨了如何整合不同scRNA-seq平台生成的数据集，以及如何利用无偏生物信息学方法识别未知的单细胞群体。

英文摘要 ​

In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods.

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主要研究问题 ​

  1. 在选择单细胞测序平台时，哪些特定的生物学问题应该优先考虑？
  2. 不同的单细胞测序技术在数据整合方面有哪些具体的挑战和解决方案？
  3. 如何评估不同单细胞测序平台在识别未知细胞群体方面的性能差异？
  4. 单细胞测序的结果如何影响免疫学研究中的实验设计和数据分析策略？
  5. 在单细胞测序数据的生物信息学分析中，常用的无偏方法有哪些，它们各自的优缺点是什么？

核心洞察 ​

研究背景和目的 ​

随着单细胞RNA测序（scRNA-seq）技术的快速发展，免疫学领域的研究逐渐重视细胞的异质性。传统的免疫细胞分析方法如流式细胞术和基因表达研究，往往无法充分解析细胞群体中的异质性。因此，本研究旨在比较四种常用的scRNA-seq平台，帮助研究者选择适合其生物学问题的最佳方法，并探讨如何整合不同平台生成的数据。

主要方法/材料/实验设计 ​

本研究比较了以下四种scRNA-seq方法：

  1. MARS-seq：一种自动化的3'端计数方法，适合于高通量分析。
  2. SMART-seq2：改进的SMART-seq方法，生成全长cDNA，适用于对转录本进行全面分析。
  3. Fluidigm C1：微流控系统，能够捕获和处理单个细胞，适合小规模样本。
  4. 10X Genomics Chromium：基于液滴的高通量系统，适合大规模样本分析。

以下是技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

方法优势局限性
MARS-seq高通量，适合大规模细胞分析仅生成部分cDNA，不适合全转录组分析
SMART-seq2生成全长cDNA，适合检测基因异构体与单核苷酸多态性无法实现样本的多重化处理，增加了成本与复杂性
Fluidigm C1允许单细胞可视化，适合少量样本对细胞形状与大小有要求，处理时间较长
10X Genomics Chromium高通量、快速，适合多样本处理对细胞输入量控制差，可能遗漏稀有细胞群体

主要结论/意义/创新性 ​

本研究为免疫学研究者提供了选择合适scRNA-seq平台的指南，强调了技术选择与生物学问题之间的关联。通过比较不同平台的优缺点，研究者可以更好地设计实验，揭示免疫系统中的细胞异质性。此外，数据整合和无偏生物信息学方法的应用为未知细胞群体的识别提供了新的视角。

研究局限性和未来方向 ​

本研究的局限性在于未能全面覆盖所有scRNA-seq方法，并且对不同平台的比较主要基于已有文献。未来的研究应进一步探索新兴技术的整合应用，建立更全面的细胞图谱。此外，研究者应关注技术标准化和数据处理算法的改进，以提高数据的可靠性和可比性。

参考文献 ​

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  2. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. - Amir Giladi;Franziska Paul;Yoni Herzog;Yaniv Lubling;Assaf Weiner;Ido Yofe;Diego Jaitin;Nina Cabezas-Wallscheid;Regine Dress;Florent Ginhoux;Andreas Trumpp;Amos Tanay;Ido Amit - Nature cell biology (2018)
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  8. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. - Diego Adhemar Jaitin;Assaf Weiner;Ido Yofe;David Lara-Astiaso;Hadas Keren-Shaul;Eyal David;Tomer Meir Salame;Amos Tanay;Alexander van Oudenaarden;Ido Amit - Cell (2016)
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引用本文的文献 ​

  1. Beyond bulk: a review of single cell transcriptomics methodologies and applications. - Ashwinikumar Kulkarni;Ashley G Anderson;Devin P Merullo;Genevieve Konopka - Current opinion in biotechnology (2019)
  2. How to dissect the plasticity of antigen-specific immune response: a tissue perspective. - D Amodio;V Santilli;P Zangari;N Cotugno;E C Manno;S Rocca;P Rossi;C Cancrini;A Finocchi;A Chassiakos;C Petrovas;P Palma - Clinical and experimental immunology (2020)
  3. Understanding the Heterogeneity of Resident Liver Macrophages. - Camille Blériot;Florent Ginhoux - Frontiers in immunology (2019)
  4. Exploring the RNA Gap for Improving Diagnostic Yield in Primary Immunodeficiencies. - Jed J Lye;Anthony Williams;Diana Baralle - Frontiers in genetics (2019)
  5. RNA sequencing by direct tagmentation of RNA/DNA hybrids. - Lin Di;Yusi Fu;Yue Sun;Jie Li;Lu Liu;Jiacheng Yao;Guanbo Wang;Yalei Wu;Kaiqin Lao;Raymond W Lee;Genhua Zheng;Jun Xu;Juntaek Oh;Dong Wang;X Sunney Xie;Yanyi Huang;Jianbin Wang - Proceedings of the National Academy of Sciences of the United States of America (2020)
  6. Environmental Carcinogenesis at the Single-Cell Level. - Gregory Chang;Kohei Saeki;Hitomi Mori;Shiuan Chen - Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology (2020)
  7. The liver as an immunological barrier redefined by single-cell analysis. - Zania Stamataki;Leo Swadling - Immunology (2020)
  8. Unravelling the heterogeneity and dynamic relationships of tumor-infiltrating T cells by single-cell RNA sequencing analysis. - Xin Yu;Lei Zhang;Ashutosh Chaudhry;Aaron S Rapaport;Wenjun Ouyang - Journal of leukocyte biology (2020)
  9. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. - Wen Wen;Wenru Su;Hao Tang;Wenqing Le;Xiaopeng Zhang;Yingfeng Zheng;Xiuxing Liu;Lihui Xie;Jianmin Li;Jinguo Ye;Liwei Dong;Xiuliang Cui;Yushan Miao;Depeng Wang;Jiantao Dong;Chuanle Xiao;Wei Chen;Hongyang Wang - Cell discovery (2020)
  10. The Role of Single-Cell Technology in the Study and Control of Infectious Diseases. - Weikang Nicholas Lin;Matthew Zirui Tay;Ri Lu;Yi Liu;Chia-Hung Chen;Lih Feng Cheow - Cells (2020)

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