Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcription factor TCF1 binds Rorgt and orchestrates a regulatory network that determines homeostatic Th17 cell state [RNA-seq]

ABSTRACT: CD4+ T helper 17 (Th17) cells encompass a spectrum of cell states including homeostatic cells that maintain physiological functions such as barrier integrity and pathogenic cells that drive autoimmune tissue inflammation. Identifying the regulators that determine these cell states will provide means to control tissue inflammation without compromising the physiological functions of Th17 cells. Here, we identified TCF1 as the key regulator that determines Th17 cell state. IL-23, a cytokine critical for inducing pathogenic Th17 cells, decreased TCF1 expression. Consistent with this observation, conditional deletion of Tcf7/TCF1 in mature myelin-specific T cells conferred pathogenicity to homeostatic Th17 cells independent of IL-23. Conversely, sustained TCF1 expression impaired acquisition of pathogenicity. Integration of transcriptional and chromatin accessibility data showed that TCF1 maintained homeostatic state through a regulatory network involving ETS family transcription factors, EGR1, and FOXO1 and by binding to and interfering with RORt activity. Our findings provide mechanistic insight into how the homeostatic and pathogenic Th17 cell states are determined and into the association of genetic variants in TCF7 with susceptibility to autoimmunity.

ORGANISM(S): Mus musculus

PROVIDER: GSE233907 | GEO | 2024/11/03

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcription factor TCF1 binds Rorgt and orchestrates a regulatory network that determines homeostatic Th17 cell state [ATAC-seq]

Project description:CD4+ T helper 17 (Th17) cells encompass a spectrum of cell states including homeostatic cells that maintain physiological functions such as barrier integrity and pathogenic cells that drive autoimmune tissue inflammation. Identifying the regulators that determine these cell states will provide means to control tissue inflammation without compromising the physiological functions of Th17 cells. Here, we identified TCF1 as the key regulator that determines Th17 cell state. IL-23, a cytokine critical for inducing pathogenic Th17 cells, decreased TCF1 expression. Consistent with this observation, conditional deletion of Tcf7/TCF1 in mature myelin-specific T cells conferred pathogenicity to homeostatic Th17 cells independent of IL-23. Conversely, sustained TCF1 expression impaired acquisition of pathogenicity. Integration of transcriptional and chromatin accessibility data showed that TCF1 maintained homeostatic state through a regulatory network involving ETS family transcription factors, EGR1, and FOXO1 and by binding to and interfering with RORt activity. Our findings provide mechanistic insight into how the homeostatic and pathogenic Th17 cell states are determined and into the association of genetic variants in TCF7 with susceptibility to autoimmunity.

2024-11-03 | GSE233905 | GEO

Population transcriptional profiling of KO or WT cells,, differentiated in vitro for 48-96h towards Th17 cells

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Population transcriptional profiling of KO or WT cells,, differentiated in vitro for 48-96h towards Th17 cells

2015-11-20 | E-GEOD-75104 | biostudies-arrayexpress

Population transcriptional profiling of Th17 cells, isolated from CNS or LN at peak of disease in EAE

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Population transcriptional profiling of Th17 cells, isolated from CNS or LN at peak of disease in EAE

2015-11-20 | E-GEOD-75105 | biostudies-arrayexpress

Population transcriptional profiling of in vitro polarized Th17 cells, either sorted for IL17A/GFP+ or unsorted

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Population transcriptional profiling of in vitro polarized Th17 cells, either sorted for IL17A/GFP+ or unsorted.

2015-11-20 | E-GEOD-75103 | biostudies-arrayexpress

Population transcriptional profiling of Th17 cells, isolated from the lamina propria of the large intestine from 3-6 month old IL-17GFP KI mice

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Population transcriptional profiling of Th17 cells, isolated from the lamina propria of the large intestine from 3-6 month old IL-17GFP KI mice

2015-11-20 | E-GEOD-75106 | biostudies-arrayexpress

Single-cell transcriptional profiling of Th17 cells, harvested at peak of disease in EAE from LN [EAE-LN-IL-17A/GFP+]

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Single-cell transcriptional profiling of Th17 cells, harvested at peak of disease in EAE from LN

2015-11-20 | E-GEOD-75108 | biostudies-arrayexpress

Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [IL1B_IL6_IL23-48h-IL-17A/GFP+]

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h

2015-11-20 | E-GEOD-75109 | biostudies-arrayexpress

Single-cell transcriptional profiling of Th17 cells, harvested at peak of disease in EAE from CNS [EAE-CNS-IL-17A/GFP+]

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Single-cell transcriptional profiling of Th17 cells, harvested at peak of disease in EAE from CNS

2015-11-20 | E-GEOD-75107 | biostudies-arrayexpress

Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [TGFB1_IL6-48h]

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h

2015-11-20 | E-GEOD-75110 | biostudies-arrayexpress

Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [TGFB1_IL6-48h-IL-17A/GFP+]

Project description:Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones. Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h

2015-11-20 | E-GEOD-75111 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data