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Curated Papers > Highly Cited Authoritative Literature on "Single-Cell Sequencing"

A Single-Cell Sequencing Guide for Immunologists. ​

Literature Information ​

DOI10.3389/fimmu.2018.02425
PMID30405621
JournalFrontiers in immunology
Impact Factor5.9
JCR QuartileQ1
Publication Year2018
Times Cited107
Keywords10X genomics chromium, MARS-seq, SMART-seq, dendritic cells, fluidigm C1
Literature TypeJournal Article, Research Support, Non-U.S. Gov't, Review
ISSN1664-3224
Pages2425
Issue9()
AuthorsPeter See, Josephine Lum, Jinmiao Chen, Florent Ginhoux

TL;DR ​

This study evaluates four widely used single-cell RNA sequencing (scRNA-seq) platforms, highlighting their respective advantages and limitations to guide users in selecting the most appropriate protocol for their immunological research. Additionally, it discusses strategies for integrating datasets from different platforms and employing unbiased bioinformatics methods to identify unknown single cell populations, underscoring the importance of tailored approaches in advancing immunological studies.

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10X genomics chromium · MARS-seq · SMART-seq · dendritic cells · fluidigm C1

Abstract ​

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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Primary Questions Addressed ​

  1. What are the specific criteria that immunologists should consider when selecting a single-cell sequencing platform for their research?
  2. How do the integration methods of datasets from different scRNA-seq platforms affect the analysis of immune cell populations?
  3. What are the emerging trends in single-cell sequencing technologies that could further enhance immunological studies?
  4. How can unbiased bioinformatics methods help in the identification of rare immune cell populations in single-cell datasets?
  5. What are the common challenges faced by researchers when analyzing single-cell sequencing data in immunology, and how can they be overcome?

Key Findings ​

Research Background and Objectives ​

The review discusses the increasing adoption of single-cell RNA sequencing (scRNA-seq) technologies in immunology, highlighting the challenges researchers face in selecting appropriate protocols for their specific biological questions. The primary aim is to compare four widely-used scRNA-seq platforms—MARS-seq, SMART-seq2, Fluidigm C1, and 10X Genomics Chromium—regarding their advantages and limitations, and to provide guidance for integrating datasets generated by different methods.

Main Methods/Materials/Experimental Design ​

The review evaluates the following scRNA-seq methods:

  1. MARS-seq: Utilizes FACS to sort single cells into 384-well plates, employing a 3' end-counting approach and unique molecular identifiers (UMIs) for quantifying gene expression.
  2. SMART-seq2: Focuses on generating full-length cDNA with enhanced yield and coverage, suitable for identifying gene isoforms but lacks multiplexing capabilities.
  3. Fluidigm C1: An automated microfluidic system that processes individual cells for mRNA quantification, offering full-length sequencing but requiring a minimum number of cells.
  4. 10X Genomics Chromium: A droplet-based method that allows high throughput and rapid processing but can introduce biases in cell representation.

The review also outlines the integration of datasets and unbiased bioinformatics approaches for identifying unknown cell populations.

Mermaid diagram

Key Results and Findings ​

  • MARS-seq detects 500-3,000 genes per cell, suitable for large sample sizes and flexible time management.
  • SMART-seq2 achieves the highest gene detection (4,000-7,000 genes) but is labor-intensive and less suitable for high-throughput scenarios.
  • Fluidigm C1 can analyze 300-7,000 genes per cell but is limited by cell size and requires immediate processing.
  • 10X Genomics Chromium offers rapid processing with 500-1,500 genes detectable but may miss rare populations due to its workflow.

Main Conclusions/Significance/Innovation ​

The review emphasizes the importance of choosing the right scRNA-seq platform based on the specific research questions, balancing factors like cell number, sensitivity, and cost. It highlights that scRNA-seq can uncover previously masked cellular heterogeneity and offers insights into immune cell dynamics and development. Furthermore, the integration of different scRNA-seq datasets can enhance the understanding of immune responses and lead to new therapeutic strategies.

Research Limitations and Future Directions ​

  • Limitations: The review notes that the choice of platform may lead to biases in data interpretation and that certain methods may not adequately represent rare cell populations.
  • Future Directions: Continued advancements in sequencing technologies and computational methods are expected to broaden the applications of scRNA-seq in immunology. There is a need for comprehensive reference databases to support more robust analyses, as well as efforts to multiplex scRNA-seq with other techniques to study multiple molecular features simultaneously.

References ​

  1. Single-cell RNA sequencing to explore immune cell heterogeneity. - Efthymia Papalexi;Rahul Satija - Nature reviews. Immunology (2018)
  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)
  3. Mapping the human DC lineage through the integration of high-dimensional techniques. - Peter See;Charles-Antoine Dutertre;Jinmiao Chen;Patrick Günther;Naomi McGovern;Sergio Erdal Irac;Merry Gunawan;Marc Beyer;Kristian Händler;Kaibo Duan;Hermi Rizal Bin Sumatoh;Nicolas Ruffin;Mabel Jouve;Ester Gea-Mallorquí;Raoul C M Hennekam;Tony Lim;Chan Chung Yip;Ming Wen;Benoit Malleret;Ivy Low;Nurhidaya Binte Shadan;Charlene Foong Shu Fen;Alicia Tay;Josephine Lum;Francesca Zolezzi;Anis Larbi;Michael Poidinger;Jerry K Y Chan;Qingfeng Chen;Laurent Rénia;Muzlifah Haniffa;Philippe Benaroch;Andreas Schlitzer;Joachim L Schultze;Evan W Newell;Florent Ginhoux - Science (New York, N.Y.) (2017)
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  5. High-dimensional analysis of the murine myeloid cell system. - Burkhard Becher;Andreas Schlitzer;Jinmiao Chen;Florian Mair;Hermi R Sumatoh;Karen Wei Weng Teng;Donovan Low;Christiane Ruedl;Paola Riccardi-Castagnoli;Michael Poidinger;Melanie Greter;Florent Ginhoux;Evan W Newell - Nature immunology (2014)
  6. The Role of the Immune System in Metabolic Health and Disease. - Niv Zmora;Stavros Bashiardes;Maayan Levy;Eran Elinav - Cell metabolism (2017)
  7. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. - Tamar Hashimshony;Florian Wagner;Noa Sher;Itai Yanai - Cell reports (2012)
  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)
  9. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. - Alexandra-Chloé Villani;Rahul Satija;Gary Reynolds;Siranush Sarkizova;Karthik Shekhar;James Fletcher;Morgane Griesbeck;Andrew Butler;Shiwei Zheng;Suzan Lazo;Laura Jardine;David Dixon;Emily Stephenson;Emil Nilsson;Ida Grundberg;David McDonald;Andrew Filby;Weibo Li;Philip L De Jager;Orit Rozenblatt-Rosen;Andrew A Lane;Muzlifah Haniffa;Aviv Regev;Nir Hacohen - Science (New York, N.Y.) (2017)
  10. The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting. - Miriam Merad;Priyanka Sathe;Julie Helft;Jennifer Miller;Arthur Mortha - Annual review of immunology (2013)

Literatures Citing This Work ​

  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)

... (97 more literatures)

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