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accession-icon GSE76699
A serial screen for roadblocks to reprogramming identifies the sumoylation effector protein Sumo2
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

The generation of induced pluripotent stem cells (iPSCs) from differentiated cells following forced expression of Oct4, Klf4, Sox2 and c-Myc (OKSM) is slow and inefficient, suggesting that transcription factors have to overcome somatic barriers that resist cell fate change. Here, we performed an ubiased serial shRNA enrichment screen to identify novel repressors of somatic cell reprogramming into iPSCs. This effort uncovered the sumoylation effector protein Sumo2 as one of the strongest roadblocks to iPSC formation. Depletion of Sumo2 both enhances and accelerates reprogramming, yielding transgene-independent, chimera-competent iPSCs after as little as 36 hours of OKSM expression. We further show that the Sumo2 pathway acts independently of exogenous c-Myc expression and in parallel with small molecule enhancers of reprogramming. Critically, suppression of SUMO2 also promotes the generation of human iPSCs. Together, our results reveal sumoylation as a crucial post-transcriptional mechanism that resists the acquisition of pluripotency from fibroblasts using defined factors.

Publication Title

A Serial shRNA Screen for Roadblocks to Reprogramming Identifies the Protein Modifier SUMO2.

Sample Metadata Fields

Sex, Specimen part, Time

View Samples
accession-icon GSE11582
Genetic Analysis of Human Traits In-Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines
  • organism-icon Pan troglodytes, Homo sapiens
  • sample-icon 355 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells. These cell lines have been used to search for genetic variants that are associated with drug response as well as with more basic cellular traits such as RNA levels. In setting out to extend such studies by searching for genetic variants contributing to drug response, we observed that phenotypes in LCLs were, in our lab and others, significantly affected by experimental confounders (i.e. in vitro growth rate, metabolic state, and relative levels of the Epstein-Barr virus used to transform the cells). As we did not find any SNPs associated with genome-wide significance to drug response, we evaluated whether incorporating RNA expression levels (and eQTLs) in the analysis could increase power to detect such effects. As previously shown, cis-acting eQTLs were detectable for a sizeable fraction of RNAs and baseline levels of many RNAs predicted response to several drugs. However, we found only limited evidence that SNPs influenced drug response through their effect on expression of RNA. Efforts to use LCLs to map genes underlying cellular traits will require great care to control experimental confounders, unbiased methods for integrating and interpreting such multi-dimensional data, and much larger sample sizes than have been applied to date.

Publication Title

Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE34721
Continuous analysis captures cellular states that reflect dominant effects of the HTT CAG repeat in human lymphoblastoid cell lines.
  • organism-icon Homo sapiens
  • sample-icon 221 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

In Huntingtons disease (HD), expanded HTT CAG repeat length correlates strongly with age at motor onset, indicating that it determines the rate of the disease process leading to diagnostic clinical manifestations. Similarly, in normal individuals, HTT CAG repeat length is correlated with biochemical differences that reveal it as a functional polymorphism. Here, we tested the hypothesis that gene expression signatures can capture continuous, length-dependent effects of the HTT CAG repeat. Using gene expression datasets for 107 HD and control lymphoblastoid cell lines, we constructed mathematical models in an iterative manner, based upon CAG correlated gene expression patterns in randomly chosen training samples, and tested their predictive power in test samples. Predicted CAG repeat lengths were significantly correlated with experimentally determined CAG repeat lengths, whereas models based upon randomly permuted CAGs were not at all predictive. Predictions from different batches of mRNA for the same cell lines were significantly correlated, implying that CAG length-correlated gene expression is reproducible. Notably, HTT expression was not itself correlated with HTT CAG repeat length. Taken together, these findings confirm the concept of a gene expression signature representing the continuous effect of HTT CAG length and not primarily dependent on the level of huntingtin expression. Such global and unbiased approaches, applied to additional cell types and tissues, may facilitate the discovery of therapies for HD by providing a comprehensive view of molecular changes triggered by HTT CAG repeat length for use in screening for and testing compounds that reverse effects of the HTT CAG expansion.

Publication Title

Dominant effects of the Huntington's disease HTT CAG repeat length are captured in gene-expression data sets by a continuous analysis mathematical modeling strategy.

Sample Metadata Fields

Sex

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accession-icon GSE26712
A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer
  • organism-icon Homo sapiens
  • sample-icon 195 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients

Publication Title

A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer.

Sample Metadata Fields

Specimen part

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accession-icon GSE18521
A gene signature predictive for outcome in advanced ovarian cancer identifies a novel survival factor: MAGP2
  • organism-icon Homo sapiens
  • sample-icon 68 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2.

Sample Metadata Fields

Specimen part, Disease stage, Cell line, Treatment

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accession-icon GSE18520
Whole-genome oligonucleotide expression analysis of papillary serous ovarian adenocarcinomas
  • organism-icon Homo sapiens
  • sample-icon 56 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

To demonstrate the use of a whole-genome oligonucleotide array to perform expression profiling on a series of microdissected late-stage, high-grade papillary serous ovarian adenocarcinomas to establish a prognostic gene signature correlating with survival and to identify novel survival factors in ovarian cancer.

Publication Title

A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2.

Sample Metadata Fields

Specimen part, Disease stage

View Samples
accession-icon GSE20576
Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells
  • organism-icon Mus musculus
  • sample-icon 61 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302), Affymetrix HT Mouse Genome 430A Array (htmg430a)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE20572
mRNA profiling of genetically matched ESCs and iPSCs
  • organism-icon Mus musculus
  • sample-icon 57 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302), Affymetrix HT Mouse Genome 430A Array (htmg430a)

Description

Induced pluripotent stem cells (iPSCs) can be generated by enforced expression of defined transcription factors in somatic cells. It remains controversial whether iPSCs are equivalent to blastocyst-derived embryonic stem cells (ESCs). Using genetically matched cells, we found that the overall mRNA expression patterns of these cell types are indistinguishable with the exception of a few transcripts encoded on chromosome 12qF1.

Publication Title

Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE22043
Expressoin data from iPSC with different cell of origin
  • organism-icon Mus musculus
  • sample-icon 45 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Induced pluripotent stem cells (iPSCs) have been derived from various somatic cell populations through ectopic expression of defined factors. It remains unclear whether iPSCs generated from different cell types are molecularly and functionally similar.

Publication Title

Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE141332
A post-transcriptional program of chemoresistance by AU-rich elements and TTP in quiescent leukemic cells
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 36 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st), Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

A post-transcriptional program of chemoresistance by AU-rich elements and TTP in quiescent leukemic cells.

Sample Metadata Fields

Specimen part, Cell line, Treatment

View Samples
...

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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