Immunology Driven by Large-Scale Single-Cell Sequencing

被引：51

作者：

Gomes, Tomas ^{[1
]}

Teichmann, Sarah A. ^{[1
,2
,3
]}

Talavera-Lopez, Carlos ^{[1
,2
]}

机构：

[1] Wellcome Sanger Inst, Wellcome Genome Campus, Hinxton, England

[2] EBI, EMBL, Wellcome Genome Campus, Hinxton, England

[3] Univ Cambridge, Dept Phys, Cavendish Lab, Theory Condensed Matter, Cambridge, England

来源：

TRENDS IN IMMUNOLOGY | 2019年 / 40卷 / 11期

基金：

欧盟地平线“2020”;

关键词：

REGULATORY NETWORK INFERENCE; RNA-SEQ; CHROMATIN ACCESSIBILITY; CLONALITY INFERENCE; EXPRESSION; RECONSTRUCTION; HETEROGENEITY; EPITOPE; LEVEL;

D O I：

10.1016/j.it.2019.09.004

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

100102 ;

摘要：

The immune system encompasses a large degree of phenotypic diversity and plasticity in its cell types, and more is to be uncovered. We argue that large, multiomic datasets of single-cell resolution, in conjunction with improved computational methods, will be essential to resolving immune cell identity. Existing datasets, combined with 'big data' methodologies, can serve as platform to support future studies in immunology. Technical and analytical advances in multio-mies and spatial integration can provide a reference for gene regulation and cellular interaction! in spatially structured tissue contexts. We posit that these developments may allow guided functional studies of immune cell populations and lay the groundwork for informed cell engineering and precision medicine.

引用

页码：1011 / 1021

页数：11

共 100 条

[1] A functional perspective on phenotypic heterogeneity in microorganisms [J].

Ackermann, Martin .

NATURE REVIEWS MICROBIOLOGY, 2015, 13 (08) :497-508

[2]

Aibar S, 2017, NAT METHODS, V14, P1083, DOI [10.1038/nmeth.4463, 10.1038/NMETH.4463]

[3] DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning [J].

Angermueller, Christof ;

Lee, Heather J. ;

Reik, Wolf ;

Stegle, Oliver .

GENOME BIOLOGY, 2017, 18

[4]

[Anonymous], 2019, BIORXIV, DOI DOI 10.1101/538652

[5]

[Anonymous], 2018, NEUROSCIENCE, DOI DOI 10.1101/459891

[6]

[Anonymous], 2018, BIORXIV, DOI DOI 10.1101/413047

[7] Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets [J].

Argelaguet, Ricard ;

Velten, Britta ;

Arnol, Damien ;

Dietrich, Sascha ;

Zenz, Thorsten ;

Marioni, John C. ;

Buettner, Florian ;

Huber, Wolfgang ;

Stegle, Oliver .

MOLECULAR SYSTEMS BIOLOGY, 2018, 14 (06)

[8] Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment [J].

Azizi, Elham ;

Carr, Ambrose J. ;

Plitas, George ;

Cornish, Andrew E. ;

Konopacki, Catherine ;

Prabhakaran, Sandhya ;

Nainys, Juozas ;

Wu, Kenmin ;

Kiseliovas, Vaidotas ;

Setty, Manu ;

Choi, Kristy ;

Fromme, Rachel M. ;

Phuong Dao ;

McKenney, Peter T. ;

Wasti, Ruby C. ;

Kadaveru, Krishna ;

Mazutis, Linas ;

Rudensky, Alexander Y. ;

Pe'er, Dana .

CELL, 2018, 174 (05) :1293-+

[9] An ontology for cell types [J].

Bard, J ;

Rhee, SY ;

Ashburner, M .

GENOME BIOLOGY, 2005, 6 (02)

[10] Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity [J].

Blecher-Gonen, Ronnie ;

Bost, Pierre ;

Hilligan, Kerry L. ;

David, Eyal ;

Salame, Tomer Meir ;

Roussel, Elsa ;

Connor, Lisa M. ;

Mayer, Johannes U. ;

Halpern, Keren Bahar ;

Toth, Beata ;

Itzkovitz, Shalev ;

Schwikowski, Benno ;

Ronchese, Franca ;

Amit, Ido .

CELL SYSTEMS, 2019, 8 (02) :109-+

← 1 2 3 4 5 6 7 8 9 10 →