Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single cell profiling of total RNA using Smart-seq-total

ABSTRACT: We describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and non-coding RNA from a single cell. Built upon the template-switch mechanism, Smart-seq-total bears the key feature of its predecessor, Smart-seq2, namely, the ability to capture full-length transcripts with high yield and quality. It outperforms current poly(A)–independent total RNA-seq protocols by capturing transcripts of a broad size range, thus, allowing us to simultaneously analyze protein-coding, long non-coding, microRNA and other non-coding RNA from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T and MCF7 cells as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. We show that simultaneous measurement of non-coding RNA and mRNA from the same cell enables elucidation of new roles of non-coding RNA throughout essential processes such as cell cycle or lineage commitment. Moreover, we show that cell types can be distinguished based on the abundance of non-coding transcripts only.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE151334 | GEO | 2020/06/01

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Single cell profiling of total RNA using Smart-seq-total

Project description:Single cell profiling of total RNA using Smart-seq-total

| PRJNA635600 | ENA

Single-cell Miniaturized Total RNA-seq (STORM-seq) in cell lines and primary tissues

Project description:Here we introduce STORM-seq, a standardized and generally available approach to profiling total RNA in single cells by miniaturization of the Takara SMART-seq Stranded (SSS) kit. The use of nanoliter-scale reaction volumes allows STORM-seq to efficiently scale to hundreds of high-complexity single cell libraries per run, from dissociated tissues and non-adherent cells, with greater than 99% per cell/library success. The use of ribosomal RNA depletion and random hexamer priming, rather than oligo-dT (poly-A) selection, allows comprehensive, whole-transcriptome analysis in each individual cell. STORM-seq recovers both coding and non-coding transcripts at an equal or greater rate to full volume reactions. Applying the method to cultured K562 cells and primary human fallopian tube epithelial (FTE) cells results in equivalent or better gene detection than manual preparation. Across all transcript biotypes, additional mRNA and lncRNA transcripts are detected beyond SSS or SMART-seq2. STORM-seq libraries from primary human FTE reveal intermediate cell types not observed by other technologies, with clear transitional significance visible upon data integration. STORM-seq provides a scalable, efficient protocol to recover deep whole transcriptomes from individual cells while minimizing costs.

2022-08-05 | GSE181544 | GEO

Comparative total RNA and mRNA sequencing and systems analysis reveals nascent transcriptional response to early HIV-1 infection in a CD4+ T cell line

Project description:To systematically investigate early host transcriptional response to HIV-1 infection, including polyadenylated and non-polyadenylated transcripts, we separately sequenced Total RNAs (Total RNA-seq) from HIV-1-infected SupT1 cells, a human CD4+ T cell line. We show many non-polyadenylated RNAs were also differentially expressed upon HIV-1 infection, as expected only detected by Total RNA-seq. Interestingly, we identified 8 times more differentially expressed genes at 12 hour post-infection by Total RNA-seq than by mRNA-seq, mostly protein-coding genes.

2014-06-10 | GSE53993 | GEO

Comprehensive Identification of Soybean Long Non-coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Project description:Long non-coding RNAs (lncRNAs) are defined as non-protein-coding transcripts that are at least 200 nucleotides long. They are known to play pivotal roles in regulating gene expression, especially during stress responses in plants. We used a large collection of in-house transcriptome data from various soybean (Glycine max and Glycine soja) tissues treated under different conditions to perform a comprehensive identification of soybean lncRNAs. We also retrieved publicly available soybean transcriptome data that were of sufficient quality and sequencing depth to enrich our analysis. In total, RNA-seq data of 332 samples were used for this analysis. An integrated reference-based, de novo transcript assembly was developed that identified ~69,000 lncRNA gene loci. We showed that lncRNAs are distinct from both protein-coding transcripts and genomic background noise in terms of length, number of exons, transposable element composition, and sequence conservation level across legume species. The tissue-specific and time-specific transcriptional responses of the lncRNA genes under some stress conditions may suggest their biological relevance. The transcription start sites of lncRNA gene loci tend to be close to their nearest protein-coding genes, and they may be transcriptionally related to the protein-coding genes, particularly for antisense and intronic lncRNAs. A previously unreported subset of small peptide-coding transcripts was identified from these lncRNA loci via tandem mass spectrometry, which paved the way for investigating their functional roles. Our results also highlight the current inadequacy of the bioinformatic definition of lncRNA, which excludes those lncRNA gene loci with small open reading frames (ORFs) from being regarded as protein-coding.

2020-01-09 | PXD014553 | Pride

Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

Project description:Single-cell RNA-seq is one of the most important and widely used approaches to characterize cell types and to understand major parts of biological systems. As of today, most methods are only able to capture parts of the whole transcriptome, mainly the protein-coding genes, and therefore lack information about non-coding biotypes or full-length transcripts. Here, we present “Vast transcriptome Analysis of Single-cells by dA-tailing (VASA-seq)”, a method for highly-sensitive, full-length and total RNA-seq in single-cells. VASA-seq is compatible with plate-formats for sorting of rare cell populations, and with droplet microfluidics for high-throughput applications and atlasing. We applied VASA-seq to >30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Our in-depth analysis revealed expression of novel cell-type specific non-coding RNA markers, enhanced cell-cycle characterization and alternative splicing patterns. We believe the method and mouse dataset will serve as a useful tool to further investigate the expression of different RNA biotypes and splicing patterns in large datasets.

2021-09-16 | GSE176588 | GEO

Total poly(A)+ RNA sequencing

Project description:Nuclear 3’ to 5’ nuclease RNA exosome plays a key role in quality control and processing of multiple protein-coding and non-coding transcripts made by RNA polymerase II. A mechanistic understanding of exosome function remains a challenge given the large number of RNA species and intervening RNA processing factors. Here we analysed changes in the poly(A)+ RNA proteins interactome provoked by mutations in three distinct subunits of the nuclear RNA exosome. Our data demonstrate a functional connection between Rrp6 and Mtl1 in controlling processing and levels of multiple protein-coding and non-coding transcripts. Furthermore, we show that exosome mutants accumulate components of U1 and U2 snRNPs and show depletion of NTC components from RNA suggesting that the stage prior to the activation of the spliceosome represents a critical quality control step. We have also identified potential new RNA binding factors involved in exosome regulation, including a zinc-finger protein called Mub1 that controls levels of selected transcripts encoding for proteins implicated in stress response. Collectively, our data have provided a global view of RNA metabolism alterations in exosome deficient cells and revealed RNA binding proteins that may act as novel exosome cofactors.

2020-06-15 | GSE149187 | GEO

Total RNA-seq from Drosophila melanogaster.

Project description:Total RNA-seq from Drosophila melanogaster.

| PRJNA687756 | ENA

WT HeLa total RNA-seq (RiboMinus)

Project description:WT HeLa total RNA-seq (RiboMinus)

| PRJNA454025 | ENA

Surface water total RNA-seq

Project description:Surface water total RNA-seq Raw sequence reads

| PRJNA503551 | ENA

Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

Project description:Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

| PRJNA736741 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data