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GSE33538
Homo sapiens
24 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
MicroRNAs (miRs) function primarily as post-transcriptional negative regulators of gene expression through binding to their mRNA targets. Reliable prediction of a miRs targets is a considerable bioinformatic challenge of great importance for inferring the miRs function. Sequence-based prediction algorithms have high false-positive rates, are not in agreement, and are not biological context specific. Here we introduce CoSMic (Context-Specific MicroRNA analysis), an algorithm that combines sequence-based prediction with miR and mRNA expression data. CoSMic differs from existing methodsit identifies miRs that play active roles in the specific biological system of interest and predicts with less false positives their functional targets. We applied CoSMic to search for miRs that regulate the migratory response of human mammary cells to epidermal growth factor (EGF) stimulation. Several such miRs, whose putative targets were significantly enriched by migration processes were identified. We tested three of these miRs experimentally, and showed that they indeed affected the migratory phenotype; we also tested three negative controls. In comparison to other algorithms CoSMic indeed filters out false positives and allows improved identification of context-specific targets. CoSMic can greatly facilitate miR research in general and, in particular, advance our understanding of individual miRs function in a specific context.
Publication Title
Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 16 Downloadable Samples
IlluminaHiSeq2000
Description
p53 is a pivotal tumor suppressor and a major barrier against cancer. We now report that silencing of the Hippo pathway tumor suppressors LATS1 and LATS2 in non-transformed mammary epithelial cells reduces p53 phosphorylation and increases its association with the p52 NF-?B subunit. Moreover, it partly shifts p53’s conformation and transcriptional output towards a state resembling cancer-associated p53 mutants, and endow p53 with the ability to promote cell migration. Notably, LATS1 and LATS2 are frequently downregulated in breast cancer; we propose that such downregulation might benefit cancer by converting p53 from a tumor suppressor into a tumor facilitator. Overall design: MCF10A cells transfected with siRNA against LATS1/2 alone, p53 alone or LATS1/2 and p53 together. Two independent MCF10A batches provided biological replicates
Publication Title
Down-regulation of LATS kinases alters p53 to promote cell migration.
Sample Metadata Fields
No sample metadata fields
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- 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- Homo sapiens
- 188 Downloadable Samples
- Illumina HiSeq 2500
Description
Background: The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. Results: In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3'-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. Conclusions: Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2-3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies. Overall design: We perform an unbiased evaluation of RNA-seq of archived tumor tissues by comparing the same library preparation methods for both FF and FFPE matched tumor samples and for small amounts of total RNA starting material. We have 3 matched FF/FFPE tumor samples with a moderate archival time of about 4-5 years (T1=T3), and additional 3 FFPE tumor samples archived for more than 10 years (T4-T6). all samples were tested with two protocols: illumina Truseq RNA after poly(A) selection (mRNA-seq); and Truseq after ribosomal depletion (RiboZero). Several initial amounts of starting material was tested for eacg protocol.
Publication Title
mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues.
Sample Metadata Fields
Specimen part, Disease, Subject
View Samples- Homo sapiens
- 10 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Che-1 is a RNA Polymerase II binding protein involved in the regulation of gene transcription. We have observed that Che-1 depletion induces apoptosis in several cancer cells expressing mutated forms of p53. We used microarrays to investigate classes of genes regulated by Che-1 in one of these cell lines.
Publication Title
Che-1 promotes tumor cell survival by sustaining mutant p53 transcription and inhibiting DNA damage response activation.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 8 Downloadable Samples
- NextSeq 500
Description
The X-linked DDX3X gene encodes an ATP-dependent DEAD-box RNA helicase frequently altered in various human cancers including melanomas. Despite its important roles in translation and splicing, how DDX3X dysfunction specifically rewires gene expression in melanoma remains completely unknown. Here we uncover a DDX3X-driven post-transcriptional program that dictates melanoma phenotype and poor disease prognosis. Through an unbiased analysis of translating ribosomes we identified the microphtalmia-associated transcription factor, MITF, as a key DDX3X translational target that directs a proliferative-to-metastatic phenotypic switch in melanoma cells. Mechanistically, DDX3X controls MITF mRNA translation via an internal ribosome entry site (IRES) embedded within the 5' untranslated region. Through this exquisite translation-based regulatory mechanism, DDX3X steers MITF protein levels dictating melanoma metastatic potential in vivo and response to targeted therapy. Together these findings unravel a post-transcriptional layer of gene regulation that may provide a unique therapeutic vulnerability in aggressive male melanomas. Overall design: We sequenced transcripts associated with translationally active ribosomes (polysomes) isolated by sucrose gradient fractionation from DDX3X and control siRNA-transduced HT144 cells. Experiments were performed in duplicates.
Publication Title
The X-Linked DDX3X RNA Helicase Dictates Translation Reprogramming and Metastasis in Melanoma.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Gene 2.0 ST Array (mogene20st)
Description
CD8+ T cells are pre-programmed for cytotoxic differentiation. However, a subset of effector CD8+ T cells (Tc17) produce IL-17 and fail to express cytotoxic genes. Here, we show that the transcription factors directing IL-17 production inhibit cytotoxicity despite persistent Runx3 expression. Cytotoxic gene repression did not require the transcription factor Thpok. We further show that STAT3 restrained cytotoxic gene expression in CD8+ T cells and that RORgt represses cytotoxic genes by inhibiting the functions but not the expression of the cytotoxic transcription factors T-bet and Eomesodermin. Thus, the transcriptional circuitry directing IL-17 expression inhibits cytotoxic functions.
Publication Title
A STAT3-dependent transcriptional circuitry inhibits cytotoxic gene expression in T cells.
Sample Metadata Fields
Specimen part
View Samples