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GSE26101
Homo sapiens
16 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a)
Description
A characteristic feature of anaplastic large cell lymphoma (ALCL) is the significant reduction of the T-cell expression program despite its T-cell origin, a finding very similar to the loss of B-cell identity of classical Hodgkin lymphoma (cHL). Previously we demonstrated that epigenetic mechanisms are active in cHL to induce this peculiar phenotype. The results show that combined DNA demethylation and histone acetylation of T-cell lines induce an almost complete extinction of the T-cell phenotype, including the down-regulation of essential T-cell receptor signalling pathway genes such as CD3, LCK and ZAP70, as well as an up-regulation of ALCL-characteristic genes. In contrast, combined DNA demethylation and histone acetylation of ALCL cells is not able to reconstitute their T-cell phenotype. This clearly demonstrates that similar epigenetic mechanisms are active in ALCL and cHL which are responsible for the extinction of their cell type characteristic phenotype.
Publication Title
Histone acetylation and DNA demethylation of T cells result in an anaplastic large cell lymphoma-like phenotype.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Homo sapiens
- 71 Downloadable Samples
NextSeq 500
Description
During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome. Overall design: PBMCs were isolated from 8 individuals bearing or not TLR10 polymorphism and were infected ex vivo with Salmonella enterica serovar Typhimurium. RNA was extracted before infection, 4 hours post infection and 8 hours post infection.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 62 Downloadable Samples
- NextSeq 500
Description
During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome. Overall design: Whole-blood (WB) cells and PBMCs were isolated from 4 healthy individuals and were infected ex vivo with Salmonella enterica serovar Typhimurium or with PBS as control. RNA was extracted 4 hours later.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Disease stage, Subject
View Samples- Mus musculus
- 24 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Cancer stemness in Wnt-driven mammary tumorigenesis.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 24 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The Wnt/beta-catenin signalling pathway plays a central role in mammary stem cell homeostasis and in breast cancer. We employed the CD29hiCD24+ cell surface antigens to identify a subpopulation of mammary CSCs from Apc1572T/+, a mouse model for metaplastic breast adenocarcinoma, a subtype of triple-negative breast cancer in man. The MaCSCs are capable of recapitulating tumorigenesis when transplanted at low multiplicities in vivo, and of forming self-renewing organoids in vitro. Expression profiling of the different subpopulations sorted from normal and neoplastic mammary tissues revealed that the normal stem cell compartment is more similar to tumor cells than to their own differentiated progenies. Accordingly, Wnt signaling was found to be activated in the subpopulation encompassing normal mammary stem cells, though to a lesser degree than in the tumor cells. By comparing normal with cancer mouse mammary compartments, we were able to derive a MaCSC-specific signature composed of human orthologous genes able to predict poor survival, relapse and distant metastasis in human breast cancer. Finally, upon intravenous injection, only MaCSCs among the different tumor cell subpopulations are able to form metastatic lesions in a broad spectrum of anatomical sites. Overall, our data indicate that constitutive Wnt signaling activation interferes with mammary stem cell homeostasis leading to metaplasia and basal-like adenocarcinomas.
Publication Title
Cancer stemness in Wnt-driven mammary tumorigenesis.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 24 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Constitutive activation of the Wnt pathway leads to adenoma formation, an obligatory step towards intestinal cancer. In view of the established role of Wnt in regulating stemness, we attempted the isolation of cancer stem cells (CSCs) from Apc- and Apc/KRAS-mutant intestinal tumours. Whereas CSCs are present in malignant Apc/KRASmutant carcinomas, they appear to be very rare (<10-6) in the benign Apcmutant adenomas. In contrast, the Lin-CD24hiCD29+ subpopulation of adenocarcinoma cells appear to be enriched in CSCs with increased levels of active -catenin. Expression profiling analysis of the CSC-enriched subpopulation confirmed their enhanced Wnt activity and revealed additional differential expression of other signalling pathways, growth factor binding proteins, and extracellular matrix components. As expected, genes characteristic of the Paneth cell lineage (e.g. defensins) are co-expressed together with stem cell genes (e.g. Lgr5) within the CSC-enriched subpopulation. This is of interest as it may indicate a cancer stem cell niche role for tumor-derived Paneth-like cells, similar to their role in supporting Lgr5+ stem cells in the normal intestinal crypt. Overall, our results indicate that oncogenic KRAS activation in Apc-driven tumours results in the expansion of the CSCs compartment by increasing b-catenin intracellular stabilization.
Publication Title
Cancer stemness in Apc- vs. Apc/KRAS-driven intestinal tumorigenesis.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 13 Downloadable Samples
- NextSeq 500
Description
During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome. Overall design: PBMCs were isolated from a healthy individual and were infected ex vivo with Salmonella enterica serovar Typhimurium or with PBS as control. Monocytes and NKT cells were sorted from naïve and infected PBMCs. RNA was extracted 4 hours post infection.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 12 Downloadable Samples
- NextSeq 500
Description
During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome. Overall design: Frozen PBMCs from healthy individual were defrosted and infectd ex vivo with Salmonella enterica serovar Typhimurium.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Mus musculus
- 15 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Knee joint synovium was used for gene expression analysis of mouse collagen induced arthritis (CIA). Synovium was prepared at day 30 after initial sensitization from: healthy controls, CIA animals with no, with mild, with moderate, or with severe joint inflammation. Each sample group is represented by three replicates, each consisting of tissue collected from three to four animals.
Publication Title
Computational design and application of endogenous promoters for transcriptionally targeted gene therapy for rheumatoid arthritis.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 77 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Molecular pathway profiling of T lymphocyte signal transduction pathways; Th1 and Th2 genomic fingerprints are defined by TCR and CD28-mediated signaling.
Sample Metadata Fields
Cell line, Treatment, Time
View Samples