Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 8.84 MB
Add to list
  1. Search results
  2. Unsupervised high-dimensional analysi...

Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species

Martin Guilliams (UGent) , Charles-Antoine Dutertre, Charlotte Scott (UGent) , Naomi McGovern, Dorine Sichien (UGent) , Svetoslav Chakarov, Sofie Van Gassen (UGent) , Jinmiao Chen, Michael Poidinger, Sofie De Prijck (UGent) , et al.
(2016) IMMUNITY. 45(3). p.669-684
Author
Organization
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients.
Keywords
CLONOGENIC PROGENITOR, SIGNALING CONTROLS, STEADY-STATE, LYMPH-NODES, BONE-MARROW, MACROPHAGES, EXPRESSION, RESPONSES, BLOOD, HOMEOSTASIS

Downloads

  • Download
    2718 16Guilliams.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 8.84 MB

Citation

Please use this url to cite or link to this publication:

MLA
Guilliams, Martin, et al. “Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.” IMMUNITY, vol. 45, no. 3, 2016, pp. 669–84, doi:10.1016/j.immuni.2016.08.015.
APA
Guilliams, M., Dutertre, C.-A., Scott, C., McGovern, N., Sichien, D., Chakarov, S., … Ginhoux, F. (2016). Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. IMMUNITY, 45(3), 669–684. https://doi.org/10.1016/j.immuni.2016.08.015
Chicago author-date
Guilliams, Martin, Charles-Antoine Dutertre, Charlotte Scott, Naomi McGovern, Dorine Sichien, Svetoslav Chakarov, Sofie Van Gassen, et al. 2016. “Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.” IMMUNITY 45 (3): 669–84. https://doi.org/10.1016/j.immuni.2016.08.015.
Chicago author-date (all authors)
Guilliams, Martin, Charles-Antoine Dutertre, Charlotte Scott, Naomi McGovern, Dorine Sichien, Svetoslav Chakarov, Sofie Van Gassen, Jinmiao Chen, Michael Poidinger, Sofie De Prijck, Simon Tavernier, Ivy Low, Sergio Erdal Irac, Citra Nurfarah Mattar, Hermi Rizal Sumatoh, Gillian Hui Ling Low, Tam John Kit Chung, Dedrick Kok Hong Chan, Ker Kan Tan, Tony Lim Kiat Hon, Even Fossum, Bjarne Bogen, Mahesh Choolani, Jerry Kok Yen Chan, Anis Larbi, Hervé Luche, Sandrine Henri, Yvan Saeys, Evan William Newell, Bart Lambrecht, Bernard Malissen, and Florent Ginhoux. 2016. “Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.” IMMUNITY 45 (3): 669–684. doi:10.1016/j.immuni.2016.08.015.
Vancouver
1.
Guilliams M, Dutertre C-A, Scott C, McGovern N, Sichien D, Chakarov S, et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. IMMUNITY. 2016;45(3):669–84.
IEEE
[1]
M. Guilliams et al., “Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species,” IMMUNITY, vol. 45, no. 3, pp. 669–684, 2016.
@article{8163748,
  abstract     = {{Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients.}},
  author       = {{Guilliams, Martin and Dutertre, Charles-Antoine and Scott, Charlotte and McGovern, Naomi and Sichien, Dorine and Chakarov, Svetoslav and Van Gassen, Sofie and Chen, Jinmiao and Poidinger, Michael and De Prijck, Sofie and Tavernier, Simon and Low, Ivy and Irac, Sergio Erdal and Mattar, Citra Nurfarah and Sumatoh, Hermi Rizal and Low, Gillian Hui Ling and Chung, Tam John Kit and Chan, Dedrick Kok Hong and Tan, Ker Kan and Hon, Tony Lim Kiat and Fossum, Even and Bogen, Bjarne and Choolani, Mahesh and Chan, Jerry Kok Yen and Larbi, Anis and Luche, Hervé and Henri, Sandrine and Saeys, Yvan and Newell, Evan William and Lambrecht, Bart and Malissen, Bernard and Ginhoux, Florent}},
  issn         = {{1074-7613}},
  journal      = {{IMMUNITY}},
  keywords     = {{CLONOGENIC PROGENITOR,SIGNALING CONTROLS,STEADY-STATE,LYMPH-NODES,BONE-MARROW,MACROPHAGES,EXPRESSION,RESPONSES,BLOOD,HOMEOSTASIS}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{669--684}},
  title        = {{Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species}},
  url          = {{http://dx.doi.org/10.1016/j.immuni.2016.08.015}},
  volume       = {{45}},
  year         = {{2016}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: