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SRP154576
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
- 7 Downloadable Samples
Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 7 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Pluripotency can be induced in somatic cells by ectopic expression of defined transcription factors, however the identity of epigenetic regulators driving the progression of cellular reprogramming requires further investigation. Here we uncover a non-redundant role for the JmjC-domain-containing protein histone H3 methylated Lys 27 (H3K27) demethylase Utx, as a critical regulator for the induction, but not for the maintenance, of primed and nave pluripotency in mice and in humans. Utx depletion results in aberrant H3K27me3 repressive chromatin demethylation dynamics, which subsequently hampers the reactivation of pluripotency promoting genes during reprogramming. Remarkably, Utx deficient primordial germ cells (PGCs) display a cell autonomous aberrant epigenetic reprogramming in vivo during their embryonic maturation, resulting in the lack of functional contribution to the germ-line lineage.
Publication Title
The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Corrigendum: Deterministic direct reprogramming of somatic cells to pluripotency.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Somatic cells can be directly reprogrammed to pluripotency by exogenous expression of transcription factors, classically Oct4, Sox2, Klf4 and c-Myc. While distinct types of somatic cells can be reprogramed with varying efficiencies and by different modified reprogramming protocols, induced pluripotent stem cell (iPSC) induction remains inefficient and stochastic where a fraction of the cells converts into iPSCs. The nature of rate limiting barrier(s) preventing majority of cells to convert into iPSCs remains elusive. Here we show that neutralizing Mbd3, a core member of the Mbd3/NURD co-repressor and chromatin-remodeling complex, results in deterministic and synchronized reprogramming of multiple differentiated cell types to pluripotency. 100% of Mbd3 depleted mouse and human somatic cells convert into iPSCs after seven days of reprogramming factor induction. Our findings delineate a critical pathway blocking the reestablishment of pluripotency, and offer a novel platform for future dissection of epigenetic dynamics leading to iPSC formation at high resolution.
Publication Title
Deterministic direct reprogramming of somatic cells to pluripotency.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus, Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Derivation of novel human ground state naive pluripotent stem cells.
Sample Metadata Fields
Specimen part, Cell line
View Samples