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Homo sapiens
6 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Kaposi sarcoma is the most common cancer in AIDS patients and is typified by red skin lesions.The disease is caused by the KSHV virus (HHV8) and is recognisable by its distinctive red skin lesions. The lesions are KSHV infected spindle cells expressing markers of the lymphatic endothelial and blood vessel endothelial cells as well as other cell types. The effects of KSHV infection of lymphatic endothelial cells (LEC) cultured in 3D matrix for three days were assayed using Affymetrix hgu133plus2 chips.
Publication Title
KSHV-initiated notch activation leads to membrane-type-1 matrix metalloproteinase-dependent lymphatic endothelial-to-mesenchymal transition.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 10 Downloadable Samples
Ion Torrent Proton
Description
We purified by magnet assisted cell sorting microglial cells from brains of adult Rab7 null mutant, aged mice and respective controls, isolated total RNA and performed RNAseq to determine the transciptome profiles. Overall design: Examination of transcriptomes of Rab7 null mutants and control (2 replicates each) and aged mice and young controls (3 replicates each)
Publication Title
Age-related myelin degradation burdens the clearance function of microglia during aging.
Sample Metadata Fields
Age, Specimen part, Cell line, Subject
View Samples
GSE35048- Rattus norvegicus
- 8 Downloadable Samples
- Affymetrix Rat Gene 1.0 ST Array (ragene10st)
Description
Introduction: The kidney is the major arbiter of extracellular phosphate homeostasis. The vast majority of glomerular filtrated phosphate is reabsorbed in the proximal tubule. Posttransplant phosphaturia is common and aggravated by sirolimus immunosuppression. The cause of sirolimus induced phosphaturia however remains elusive.
Publication Title
Sirolimus induced phosphaturia is not caused by inhibition of renal apical sodium phosphate cotransporters.
Sample Metadata Fields
Sex, Specimen part
View Samples- Homo sapiens
- 56 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
To identify genes implicated in metastatic colonization of the liver in colorectal cancer, we collected pairs of primary tumors and hepatic metastases before chemotherapy in 13 patients. We compared mRNA expression in the pairs of patients to identify genes deregulated during metastatic evolution. We then validated the identified genes using data obtained by different groups. The 33-gene signature was able to classify 87% of hepatic metastases, 98% of primary tumors, 97% of normal colon mucosa, and 95% of normal liver tissues in six datasets obtained using five different microarray platforms. The identified genes are specific to colon cancer and hepatic metastases since other metastatic locations and hepatic metastases originating from breast cancer were not classified by the signature. Gene Ontology term analysis showed that 50% of the genes are implicated in extracellular matrix remodeling, and more precisely in cell adhesion, extracellular matrix organization and angiogenesis. Because of the high efficiency of the signature to classify colon hepatic metastases, the identified genes represent promising targets to develop new therapies that will specifically affect hepatic metastasis microenvironment.
Publication Title
Specific extracellular matrix remodeling signature of colon hepatic metastases.
Sample Metadata Fields
Sex, Age, Specimen part, Subject
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- 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- Mus musculus
- 36 Downloadable Samples
- Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)
Description
Microglia are tissue macrophages of the central nervous system (CNS) that control tissue homeostasis, and as such they are crucially important for organ integrity. Microglia dysregulation is thought to be causal for a group of neuropsychiatric, neurodegenerative and neuroinflammatory diseases, called microgliopathies. However, how the intracellular stimulation machinery in microglia is controlled is poorly understood. By using expression studies, we identified the ubiquitin-specific protease (Usp) 18 in white matter microglia that essentially contributes to microglial quiescence under homeostatic conditions. We further found that microglial Usp18 negatively regulated the activation of STAT1 and concomitant induction of interferon-induced genes thereby disabling the termination of IFN signalling. Unexpectedly, the Usp18-mediated feedback loop was independent from the catalytic domain of the protease but instead required the interacting region of Ifnar2. Additionally, the absence of Ifnar1 completely rescued microglial activation indicating a tonic IFN signal mediated by receptor interactions under non-diseased conditions. Finally, conditional depletion of Usp18 only in myeloid cells significantly enhanced the disease burden in a mouse model of CNS autoimmunity, increased axonal and myelin damage and determined the spatial distributions of CNS lesions that shared the same STAT1 signature as myeloid cells found in active multiple sclerosis (MS) lesions. These results identify Usp18 as novel negative regulator of microglia activation, demonstrate a protective role of the IFNAR pathway for microglia and establish Usp18 as potential therapeutic target for the treatment of MS.
Publication Title
USP18 lack in microglia causes destructive interferonopathy of the mouse brain.
Sample Metadata Fields
Specimen part
View Samples