文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

免疫学家单细胞测序指南

A Single-Cell Sequencing Guide for Immunologists.

机构信息

Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore.

Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, Shanghai, China.

出版信息

Front Immunol. 2018 Oct 23;9:2425. doi: 10.3389/fimmu.2018.02425. eCollection 2018.


DOI:10.3389/fimmu.2018.02425
PMID:30405621
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6205970/
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.

摘要

近年来,单细胞测序(scRNA-seq)方法在免疫学领域的应用迅速增加。随着各种技术的广泛应用,用户越来越难以选择最佳的 scRNA-seq 方案/平台来解决他们感兴趣的生物学问题。在这里,我们比较了四种常用的 scRNA-seq 平台的优缺点,以阐明它们在不同实验应用中的适用性。我们还讨论了如何整合来自不同 scRNA-seq 平台的数据集,以及如何使用无偏生物信息学方法识别未知的单细胞群体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/536668da8988/fimmu-09-02425-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/0bfa942586e5/fimmu-09-02425-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/9f7b7bf42eeb/fimmu-09-02425-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/25154485912d/fimmu-09-02425-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/a828a11a1d9e/fimmu-09-02425-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/536668da8988/fimmu-09-02425-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/0bfa942586e5/fimmu-09-02425-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/9f7b7bf42eeb/fimmu-09-02425-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/25154485912d/fimmu-09-02425-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/a828a11a1d9e/fimmu-09-02425-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1abf/6205970/536668da8988/fimmu-09-02425-g0005.jpg

相似文献

[1]
A Single-Cell Sequencing Guide for Immunologists.

Front Immunol. 2018-10-23

[2]
Revolutionizing immunology with single-cell RNA sequencing.

Cell Mol Immunol. 2019-2-22

[3]
JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets.

J Immunol. 2017-5-1

[4]
Data Analysis in Single-Cell Transcriptome Sequencing.

Methods Mol Biol. 2018

[5]
Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Methods Mol Biol. 2021

[6]
Single-Cell Transcriptomics of Immune Cells: Cell Isolation and cDNA Library Generation for scRNA-Seq.

Methods Mol Biol. 2020

[7]
Random forest based similarity learning for single cell RNA sequencing data.

Bioinformatics. 2018-7-1

[8]
Comparative Analysis of Single-Cell RNA Sequencing Platforms and Methods.

J Biomol Tech. 2021-12-15

[9]
Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis.

J Am Soc Nephrol. 2018-12-3

[10]
Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2.

Genomics Proteomics Bioinformatics. 2021-4

引用本文的文献

[1]
What's next for computational systems biology?

Front Syst Biol. 2023-9-19

[2]
The Role of Omics Techniques in Diabetic Wound Healing: Recent Insights into the Application of Single-Cell RNA Sequencing, Bulk RNA Sequencing, Spatial Transcriptomics, and Proteomics.

Adv Ther. 2025-5-17

[3]
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.

J Mach Learn Res. 2023-4

[4]
Decoding B Cells in Autoimmune Diseases Through ScRNA + BCR-Seq: Current Knowledge and Future Directions.

Cells. 2025-4-3

[5]
scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.

Commun Biol. 2025-2-13

[6]
Comprehensive guide for epigenetics and transcriptomics data quality control.

STAR Protoc. 2025-3-21

[7]
Novel vaccine strategies to induce respiratory mucosal immunity: advances and implications.

MedComm (2020). 2025-1-16

[8]
Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3.

BMC Genomics. 2024-11-21

[9]
Gut microbiota in health and disease: advances and future prospects.

MedComm (2020). 2024-11-20

[10]
Single cell analysis of Idh mutant growth plates identifies cell populations responsible for longitudinal bone growth and enchondroma formation.

Sci Rep. 2024-10-31

本文引用的文献

[1]
Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis.

Nat Cell Biol. 2018-6-18

[2]
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

Nat Biotechnol. 2018-4-2

[3]
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.

Science. 2018-3-15

[4]
Platforms for Single-Cell Collection and Analysis.

Int J Mol Sci. 2018-3-11

[5]
Homeostatic control of dendritic cell numbers and differentiation.

Immunol Cell Biol. 2018-3-23

[6]
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries.

Cell. 2018-1-11

[7]
The Human Cell Atlas.

Elife. 2017-12-5

[8]
Single-cell transcriptomics to explore the immune system in health and disease.

Science. 2017-10-6

[9]
Single-cell RNA sequencing to explore immune cell heterogeneity.

Nat Rev Immunol. 2017-8-7

[10]
Simultaneous epitope and transcriptome measurement in single cells.

Nat Methods. 2017-9

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索