Allelic dilution series series of C57BL6/J:CAST/EiJ RNA using Smart-seq3
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB46197 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR6221486.fastq.gz | Fastqsanger.gz | |||
| ERR6221487.fastq.gz | Fastqsanger.gz | |||
| ERR6221488.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 3 of 3 |
Similar Datasets
Project description:Technical control for allelic detection using Smart-seq3. Liver RNA from pure C57BL6/J and CAST/EiJ strains was combined at varying ratios (0:1, 1:7, 1:3, 3:5, 1:1, 5:3, 3:1, 7:1, 1:0) for a total of 200 pg RNA per sample.
2022-02-14 | E-MTAB-10709 | biostudies-arrayexpress
Project description:Allele-sensitive RNA sequencing of single-cells can be used to infer the kinetics of transcriptional bursts in eukaryotic cells. Here, we used the Smart-seq3 protocol to prepare libraries from two 384-well plates of primary mouse fibroblasts. The fibroblasts were derived from tail explants of a male adult mouse (F1 offspring of C57 x CAST cross). The samples were sequenced to high depth using MGI's DNBSEQ G400RS platform using paired-end 100 bp reads.
2021-02-25 | E-MTAB-10148 | biostudies-arrayexpress
Project description:We report dynamics of X-chromosome upregulation (XCU) along X-chromosome inactivation (XCI) in mESCs as they differentiate into EpiSCs. F1 hybrid C57BL6/J × CAST/EiJ male and female mESCs were adapted to 2i/LIF and female cells grown in serum/LIF conditions were differentiated using Fgf2 and Activin A for 1, 2, 4 and 7 days to induce random XCI. scRNA-seq was performed using the Smart-seq3 protocol, providing full-length coverage together with molecular counting using UMIs. Allelic resolution is achieved using strain-specific SNPs in the data. We reveal dynamic balancing of X alleles as cells undergo XCI to compensate dosage imbalances between sexes as well as between X and autosomes. Furthermore, we reveal that female naïve mESCs with two active X chromosomes lack XCU on both alleles which has major implications for reprogramming studies. Finally, we estimate allelic transcriptional burst kinetics from the data and find that progressively increased burst frequencies underlies the XCU process.
2022-02-14 | E-MTAB-9324 | biostudies-arrayexpress
Project description:Single cell sequencing with Smart-seq3 of K562 and mESC cells
| PRJEB67545 | ENA
Project description:We report dynamics of X-chromosome upregulation (XCU) along X-chromosome inactivation (XCI) in mESCs as they differentiate into EpiSCs. F1 hybrid C57BL6/J × CAST/EiJ male and female mESCs were grown in serum/LIF conditions were differentiated using Fgf2 and Activin A for 1, 2, 4 and 7 days to induce random XCI in female cells. Multi-modal single-cell sequencing was performed using scATAC on nuclei and Smart-seq3 to assay chromatin accessibility and poly-A+ RNA expression, respectively. Allelic resolution is achieved using strain-specific SNPs in the data. We reveal dynamic balancing of X alleles as cells undergo XCI to compensate dosage imbalances between sexes as well as between X and autosomes. Furthermore, we reveal that female naïve mESCs with two active X chromosomes lack XCU on both alleles which has major implications for reprogramming studies. Finally, we estimate allelic transcriptional burst kinetics from the data and find that progressively increased burst frequencies underlies the XCU process.
2022-02-14 | E-MTAB-10714 | biostudies-arrayexpress
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-06-24 | GSE270511 | GEO
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-07-02 | GSE270917 | GEO
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-07-02 | GSE270928 | GEO
Project description:Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set.
2021-05-07 | PXD023012 | Pride
Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.
2020-04-14 | E-MTAB-8735 | biostudies-arrayexpress