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GSE32519
Homo sapiens
29 Downloadable Samples
Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)
Description
Gene expression studies are used to help identify disease-associated genes, by comparing the levels of expressed transcripts between cases and controls, and to identify functional genetic variants known as expression quantitative loci (eQTLs). While many of these studies are performed in blood or lymphoblastoid cell lines due to tissue accessibility, the relevance of expression differences in tissues that are not the primary site of disease is unclear. Further, many eQTLs are tissue specific. Thus, there is a clear and compelling need to conduct gene expression studies in tissues that are specifically relevant to the disease of interest. One major technical concern about using autopsy-derived tissue is how representative it is of physiologic conditions, given the effect of postmortem interval on tissue degradation.
Publication Title
Postmortem cardiac tissue maintains gene expression profile even after late harvesting.
Sample Metadata Fields
Specimen part, Disease, Cell line
View Samples- Homo sapiens
- 12 Downloadable Samples
IlluminaHiSeq2500
Description
Epigenetic modifications determine the structure and regulation of eukaryotic genomes and define key signatures of cell lineage specification. Technologies that facilitate the targeted manipulation of epigenetic marks could be used to precisely control cell phenotype or interrogate the relationship between the epigenome and transcriptional control. Here we have generated a programmable acetyltransferase based on the CRISPR/Cas9 gene regulation system, consisting of the nuclease-null dCas9 protein fused to the catalytic core of the human acetyltransferase p300. This fusion protein catalyzes acetylation of histone H3 lysine 27 (H3K27) at its target sites, leading to robust transcriptional activation of target genes from promoters, proximal enhancers, and distal enhancers. In contrast to conventional dCas9-based activators, the acetyltransferase fusion effectively activated genes from enhancer regions and with individual guide RNAs. The core p300 domain was also portable to other programmable DNA-binding proteins. This technology enables the targeted perturbation of native epigenetic architecture and will be useful for reprogramming the epigenome for applications in genomics, genetics, disease modeling, and manipulating cell fate. Overall design: HEK293T cells were transfected in triplicate with plasmids expressing synthetic transcription factors. The synthetic TFs were either (a) dCas9-VP64 fusion protein and a targeting guide RNA (gRNA), or (b)dCas9-p300 fusion protein containing the catalytic domain of p300 and a targeting guide RNA (gRNA). As a control, cells were transfected with plasmids expressing dCas9 alone and dCas9 fused with a aceryltransferase null mutatnt form of the p300 catalytic domain (D1399Y, as in text). After transfection, RNA-seq was used to identify differential expressin at on-target and off-target sites.
Publication Title
Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 2 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
ChIP-seq analysis reveals distinct H3K27me3 profiles that correlate with transcriptional activity.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 5 Downloadable Samples
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the 9 cell mixture dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 3 Downloadable Samples
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 5 human lung adenocarcinoma cell lines H2228, H1975, A549, H838 and HCC827. For the single cell designs, the five cell lines were mixed equally and processed by 10X chromium and CEL-seq2, referred to as sc_10X_5cl, and sc_CEL-seq2_5cl respectively in analysis that follows. For CEL-seq2, three plates were sorted and processed.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Subject
View Samples- Mus musculus
- 2 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Transcriptional control is dependent on a vast network of epigenetic modifications. One epigenetic mark of particular interest is tri-methylation of lysine 27 on histone H3 (H3K27me3), which is catalyzed and maintained by the Polycomb Repressor Complex (PRC2). Although this histone mark is studied widely, the precise relationship between its local pattern of enrichment and regulation of gene expression is currently unclear. We have used ChIP-seq to generate genome wide maps of H3K27me3 enrichment, and have identified three enrichment profiles with distinct regulatory consequences. First, a broad domain of H3K27me3 enrichment across the body of genes corresponds to the canonical view of H3K27me3 as inhibitory to transcription. Second, a peak of enrichment around the transcription start site is commonly associated with bivalent genes, where H3K4me3 also marks the TSS. Finally and most surprisingly, we identified an enrichment profile with a peak in the promoter of genes that is associated with active transcription. Genes with each of these three profiles were found in different proportions in each of the cell types studied. The data analysis techniques developed here will be useful for the identification of common enrichment profiles for other histone modifications that have important consequences for transcriptional regulation.
Publication Title
ChIP-seq analysis reveals distinct H3K27me3 profiles that correlate with transcriptional activity.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 1 Downloadable Sample
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the RNAmix_CEL-seq2 dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 1 Downloadable Sample
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the RNAmix_CEL-seq2 dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 1 Downloadable Sample
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 56 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Intravaginal HIV microbicides could provide women with a self-controlled means for HIV prevention, but results from clinical trials have been largely disappointing. We postulated that unrecognized effects of intravaginal gels on the upper female reproductive tract (FRT) might contribute to the lower-than-expected efficacy of HIV microbicides. In this observational crossover study, 28 healthy female volunteers used no product (control cycle) or used a nightly application of intravaginal nonoxynol-9 gel [N9] as a 'failed' microbicide or the universal placebo gel [UPG] as a 'safe' gel, from the end of menses to the mid-luteal phase (intervention cycles). They then underwent sample collection for measurements of T-cell phenotypes, transcriptional profiling, and protein levels from 3 anatomic sites above the vagina: the cervical transformation zone, the endocervix and the endometrium. We used hierarchical statistical models to estimate mean (95% CI) intervention:control fold-changes in relevant phenotype levels. Exposure to N9 and UPG generated a common 'harm signature' that included transcriptional up-regulation of inflammatory genes CCL20 and IL8 in the cervix, decreased protein concentrations of secretory leukocyte protease inhibitor and increased percentages of terminally differentiated CD4+ effector T-cells in the endocervix, and transcriptional up-regulation of inflammatory mediators KIR3DS1, glycodelin-A, and osteopontin in the endometrium. These results underscore the need to consider the effects of microbicide agents and gel excipients on the upper FRT in studies of vaginal microbicides. Given the pro-inflammatory effects of UPG on the upper FRT, it may not be a suitable placebo for microbicide trials.
Publication Title
Unexpected Inflammatory Effects of Intravaginal Gels (Universal Placebo Gel and Nonoxynol-9) on the Upper Female Reproductive Tract: A Randomized Crossover Study.
Sample Metadata Fields
No sample metadata fields
View Samples