Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
Publ. Version/Full Text
Research data
DOI
PMC
 |
Open Access Gold (Paid Option) |
|
as soon as is submitted to ZB.
Distinguishing activated T regulatory cell and T conventional cells by single-cell technologies.
Immunology 166, 121-137 (2022)
Publ. Version/Full Text
Research data
DOI
PMC
Resting conventional T cells (Tconv) can be distinguished from T regulatory cells (Treg) by the canonical markers FOXP3, CD25 and CD127. However, the expression of these proteins alters after T-cell activation leading to overlap between Tconv and Treg. The objective of this study was to distinguish resting and antigen-responsive T effector (Tconv) and Treg using single-cell technologies. CD4+ Treg and Tconv cells were stimulated with antigen and responsive and non-responsive populations processed for targeted and non-targeted single-cell RNAseq. Machine learning was used to generate a limited set of genes that could distinguish responding and non-responding Treg and Tconv cells and which was used for single-cell multiplex qPCR and to design a flow cytometry panel. Targeted scRNAseq clearly distinguished the four-cell populations. A minimal set of 27 genes was identified by machine learning algorithms to provide discrimination of the four populations at >95% accuracy. In all, 15 of the genes were validated to be differentially expressed by single-cell multiplex qPCR. Discrimination of responding Treg from responding Tconv could be achieved by a flow cytometry strategy that included staining for CD25, CD127, FOXP3, IKZF2, ITGA4, and the novel marker TRIM which was strongly expressed in Tconv and weakly expressed in both responding and non-responding Treg. A minimal set of genes was identified that discriminates responding and non-responding CD4+ Treg and Tconv cells and, which have identified TRIM as a marker to distinguish Treg by flow cytometry.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Activation ; Cd4 Cell ; T Cell ; Transcriptomics ; Treg
ISSN (print) / ISBN
0019-2805
e-ISSN
1365-2567
Journal
Immunology
Quellenangaben
Volume: 166,
Issue: 1,
Pages: 121-137
Publisher
Wiley
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Pancreatic Beta Cell Research (IPI)
Grants
INNODIATranslational approaches to disease modifying therapy of type 1 diabetes: an innovation approach towards understanding and arresting Type 1 diabetes
Deutsche Forschungsgemeinschaft
Deutsche Forschungsgemeinschaft