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Mus musculus
20 Downloadable Samples
Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
We have analyzed the transcript expression in different LCM-dissected cell layers isolated from mouse retinas adapted to light or dark in order to identify transcripts potentially targetted by retinal microRNAs which are regulated in response to light treatment
Publication Title
Characterizing light-regulated retinal microRNAs reveals rapid turnover as a common property of neuronal microRNAs.
Sample Metadata Fields
Specimen part, Treatment
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
- 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
- 112 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Molecular subtypes of metastatic colorectal cancer are associated with patient response to irinotecan-based therapies.
Sample Metadata Fields
Sex, Age
View Samples