Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Human RORM-NM-3t+ CD34+ cells are lineage-specified progenitors of group 3 RORM-NM-3t+ innate lymphoid cells

ABSTRACT: Group 3 innate lymphoid cells (ILC3) are defined by the expression of RORM-NM-3t, which is selectively required for their development. The lineage-specified progenitor cells of human ILC3 and their developmental site after birth remain undefined. Here we identified a novel population of human CD34+ hematopoietic progenitor cells (HPC) expressing RORM-NM-3t and sharing with ILC3 a distinct transcriptional signature. RORM-NM-3t+ CD34+ HPC were located in tonsils and intestinal lamina propria (LP) and selectively differentiated towards ILC3. Conversely, RORM-NM-3t- CD34+ HPC displayed commitment potential for both ILC3 and NK cells and the differentiation fate towards these two cell lineages was determined by cytokine and aryl hydrocarbon receptor (AhR) signaling. Thus, we propose that RORM-NM-3t+ CD34+ cells represent human lineage-specified progenitors of IL-22+ ILC3 and that tonsils as well as intestinal LP might be preferential sites of their differentiation. ILC3, NK cells and the CD34+ HPC subsets were sorted from tonsils of 6 distinct donors to purity above 95%. cRNA of the sorted cell populations was hybridized to an Agilent Whole Human Genome Oligo Microarrays (8x60K v2, Design ID 039494)

ORGANISM(S): Homo sapiens

SUBMITTER: Chiara Romagnani

PROVIDER: E-GEOD-63197 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

Project description:A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count. Raw data has been deposited at the European Genome-Phenome Archive (www.ebi.ac.uk/ega) under accession EGAS00001002636.

2017-07-31 | E-MTAB-5909 | biostudies-arrayexpress

Immune and HCT116 RNA mixtures

Project description:These experiments were designed as a benchmark tool for deconvolution methods. 5 immune cell populations were sorted from 3 healthy donors' peripheral bloods. Peripheral Blood Mononuclear Cells (PBCMs) and PolymorphoNuclear Cells (PMN) were separated using gradient centrifugation. T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-) and NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+) were FACS-sorted from PBMCs and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were sorted from PMNs. RNA was extracted from the purified cell population, as well as from the HCT116 colon cancer cell line. RNAs from pure populations were then mixed in various proportions. RNA from HCT116 cells and FACS-sorted T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-), NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+), and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were mixed in various proportions.

2016-02-18 | E-GEOD-64385 | biostudies-arrayexpress

RNA-seq of mouse small intestinal CCR6+ group3 innate lymphoid cells

Project description:CCR6+NKp46- ILC3 were isolated from the small intestine of untreated mice by flow cytometry. ILC3 from 3 mice were pooled for subsequent RNA sequencing.

2019-10-11 | E-MTAB-8387 | biostudies-arrayexpress

Group 3 innate lymphoid cells continuously require the transcription factor GATA3 after commitment

Project description:GATA3 is indispensable for the development of all IL-7RÎ±-expressing innate lymphoid cells (ILCs) and maintenance of type 1 ILCs (ILC1s) and type 2 ILCs (ILC2s). However, the importance of low GATA3 expression in type 3 ILCs (ILC3s) is still elusive. Here, we report that GATA3 regulates homeostasis of ILC3s by controlling IL-7RÎ± expression. In addition, GATA3 is critical for the development of NKp46+ ILC3 subset partially through regulating the balance between T-bet and RORÎ³t. Genome-wide analyses indicate that while GATA3 positively regulates CCR6+ and NKp46+ ILC3 subset-specific genes in respective lineages, it negatively regulates CCR6+ ILC3-specific genes in NKp46+ ILC3s. Furthermore, GATA3 regulates IL-22 production in both CCR6+ and NKp46+ ILC3s. Thus, low GATA3 expression is critical for the development and function of ILC3 subsets. To identify GATA3 regulated genes in total ILC3s with RNA-Seq; To identify unique genes expressed by CCR6+ ILC3 or NKp46+ ILC3 and GATA3 regulated genes within these two ILC3 subsets with RNA-Seq; To identify GATA3 direct binding sites in ILC3s, ILC2s and Th2 cells with ChIP-Seq.

2015-11-23 | E-GEOD-71198 | biostudies-arrayexpress

Transcriptional profiles of human circulating innate lymphoid cells and CD4 T helper cells

Project description:Innate lymphoid cells (ILCs) are part of the innate immune cell family. Three different subsets of ILCs, ILC1s, ILC2s and ILCPs can be identified in human peripheral blood. Based on their expression of transcription factors and cytokines, they are considered as being the innate counterparts of CD4 T helper subsets, namely Th1s, Th2s and Th17s. However, ILCs and Th cells have different roles in immunity. Therefore, we compared the transcriptomes of sorted ILC1s, ILC2s, ILCPs, Th1s, Th2s and Th17s from the peripheral blood of three different donors. RNA sequencing of ILC and Th subsets revealed differences in the expression of tens to hundreds of genes. These genes are involved in cell trafficking, innate activation and inhibitory functions. ILC and Th cell subsets also differ in their expressions of long-non coding RNAs.

2020-02-04 | E-MTAB-8494 | biostudies-arrayexpress

Expression data from human hematopoietic stem and progenitor compartments from patients with acute myeloid leukemia with normal karyotype and healthy controls

Project description:We applied a novel approach of parallel transcriptional analysis of multiple, highly fractionated stem and progenitor populations from patients with acute myeloid leukemia (AML) and a normal karyotype. We isolated phenotypic long-term HSC (LT-HSC), short-term HSC (ST-HSC), and committed granulocyte-monocyte progenitors (GMP) from individual patients, and measured gene expression profiles of each population, and in comparison to their phenotypic counterparts from age-matched healthy controls. Bone marrow samples from AML patients with normal karyotype and age-matched healthy controls were used in this study. Hematopoietic stem and progenitor compartments were purified by multiparameter-high speed fluorescence-activated cell sorting (FACS) from CD34+ enriched bone marrow to isolate LT-HSC (Lin-/CD34+/CD38-/CD90+), ST-HSC (Lin-/CD34+/CD38-/CD90-), and GMP (Lin-/CD34+/CD38+/CD123+/CD45R+).

2012-09-05 | E-GEOD-35008 | biostudies-arrayexpress

Expression data from human hematopoietic stem and progenitor compartments from patients with acute myeloid leukemia with monosomy 7 and healthy controls

Project description:We applied a novel approach of parallel transcriptional analysis of multiple, highly fractionated stem and progenitor populations in a genetically defined subset of AML (AML with monosomy 7). We isolated phenotypic long-term HSC (LT-HSC), short-term HSC (ST-HSC), and committed granulocyte-monocyte progenitors (GMP) from individual patients with AML, and measured gene expression profiles of each population, and in comparison to their phenotypic counterparts from age-matched healthy controls. Bone marrow samples from AML patients bearing monosomy 7 and age-matched healthy controls were used in this study. Hematopoietic stem and progenitor compartments were purified by multiparameter-high speed fluorescence-activated cell sorting (FACS) from CD34+ enriched bone marrow to isolate LT-HSC (Lin-/CD34+/CD38-/CD90+), ST-HSC (Lin-/CD34+/CD38-/CD90-), and GMP (Lin-/CD34+/CD38+/CD123+/CD45R+).

2012-09-05 | E-GEOD-35010 | biostudies-arrayexpress

Chronic growth factor signaling in Acute Myeloid Leukemia is connected to a specific chromatin signature [Methylation]

Project description:We performed genome-wide DNaseI hypersensitive site in FLT3-ITD and WT patient samples. We report corresponding gene expression, promoter methylation patterns of differential DHSs as well as differentially occupied TF binding motifs using surrounding DNAseI cut profiles. We examined association patterns of FLT3 ITD and WT AMLs and found that FLT3-ITD signaling is associated with common gene expression and chromatin signature. Examination of DNA methylation patterns in FLT3 ITD and WT AML via microarray study.

2015-07-03 | E-GEOD-64934 | biostudies-arrayexpress

Methylation arrays (MethylationEPIC) reveal specific DNA hypermethylation of T cells during human hematopoietic differentiation

Project description:Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. We used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages. We identified a plethora of DNA methylation changes that occur during human hematopoietic differentiation. Interestingly, we observed that T lymphocytes display a substantial enhancement of de novo CpG hypermethylation as compared to other hematopoietic cell populations.

2018-06-08 | E-MTAB-6315 | biostudies-arrayexpress

Human RORγt+ CD34+ cells are lineage-specified progenitors of group 3 RORγt+ innate lymphoid cells

Project description:Group 3 innate lymphoid cells (ILC3) are defined by the expression of RORγt, which is selectively required for their development. The lineage-specified progenitor cells of human ILC3 and their developmental site after birth remain undefined. Here we identified a novel population of human CD34+ hematopoietic progenitor cells (HPC) expressing RORγt and sharing with ILC3 a distinct transcriptional signature. RORγt+ CD34+ HPC were located in tonsils and intestinal lamina propria (LP) and selectively differentiated towards ILC3. Conversely, RORγt- CD34+ HPC displayed commitment potential for both ILC3 and NK cells and the differentiation fate towards these two cell lineages was determined by cytokine and aryl hydrocarbon receptor (AhR) signaling. Thus, we propose that RORγt+ CD34+ cells represent human lineage-specified progenitors of IL-22+ ILC3 and that tonsils as well as intestinal LP might be preferential sites of their differentiation.

2014-11-25 | GSE63197 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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