HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes (scRNA-seq dataset)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA663611 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12646006_1.fastq.gz | Fastqsanger.gz | |||
| SRR12646006_2.fastq.gz | Fastqsanger.gz |
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Project description:Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present HyPR-seq, a method to sensitively quantify the expression of 10 to 100 chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects non-polyadenylated and low-abundance transcripts, and can profile up to 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell type frequencies in tissue using low-abundance marker genes. By sensitively quantifying individual transcripts and directing sequencing power to genes of interest, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome scRNA-seq approaches for these applications. HyPR-seq will be a powerful method for targeted RNA profiling in single cells.
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Project description:Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present HyPR-seq, a method to sensitively quantify the expression of 10 to 100 chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects non-polyadenylated and low-abundance transcripts, and can profile up to 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell type frequencies in tissue using low-abundance marker genes. By sensitively quantifying individual transcripts and directing sequencing power to genes of interest, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome scRNA-seq approaches for these applications. HyPR-seq will be a powerful method for targeted RNA profiling in single cells.
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Project description:Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present HyPR-seq, a method to sensitively quantify the expression of 10 to 100 chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects non-polyadenylated and low-abundance transcripts, and can profile up to 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell type frequencies in tissue using low-abundance marker genes. By sensitively quantifying individual transcripts and directing sequencing power to genes of interest, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome scRNA-seq approaches for these applications. HyPR-seq will be a powerful method for targeted RNA profiling in single cells.
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Project description:HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes (HyPR-seq dataset)
| PRJNA663612 | ENA
Project description:HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes
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Project description:HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes (bulk RNA-seq dataset)
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Project description:Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.
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Project description:This SuperSeries is composed of the SubSeries listed below.
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2009-10-27 | GSE15835 | GEO
Project description:MicroRNAs (miRNAs) have been shown to play an important role in many different cellular, developmental, and physiological processes. Accordingly, numerous methods have been established to identify and quantify miRNAs. The shortness of miRNA sequence results in a high dynamic range of melting temperatures and, moreover, impedes a proper selection of detection probes or optimized PCR primers. While miRNA microarrays allow for massive parallel and accurate relative measurement of all known miRNAs, they have so far been less useful as an assay for absolute quantification. Here, we present a microarray based approach for global and absolute quantification of miRNAs. The method relies on an equimolar pool of about 1000 synthetic miRNAs of known concentration which is used as an universal reference and labeled and hybridized in a dual colour approach on the same array as the sample of interest. Each single miRNA is quantified with respect to the universal reference outbalancing bias related to sequence, labeling, hybridization or signal detection method. We demonstrate the accuracy of the method by various spike in experiments. Further, we quantified miRNA copy numbers in liver samples and CD34(+)CD133(-) hematopoietic stem cells. We analyzed to which extend the universal reference can be used as a tool for the relative quantification of miRNAs across multiple experiments. We compared the results of direct hybridizations i.e. sample vs. sample to those of indirect hybridizations i.e. sample vs. UR. For the direct hybridizations, we hybridized 5µg liver total RNA vs 5 µg brain total RNA (n = 3) and for the indirect hybridization 5 µg liver or brain total RNA vs UR (5 fmol/miRNA) (n = 3). We calculated the so-called re-ratios for the UR experiments by dividing the signal ratios of the liver vs. UR array by the respective brain vs. UR array gaining a liver vs. brain re-ratio. Each RNA sample was mixed with 5 fmol of each of 18 RNA oligonucleotides reverse complement to miRControl 3 probes and subsequently fluorescently labelled. The RNA mix was hybridized in a dual colour approach to microarrays. The mean ratios of all probes were normalized to the median of the ratios detected for the spiked 18 synthetic RNA oligonucleotides reverse complement to miRControl 3 probes.
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