Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Thiol-linked alkylation for the metabolic sequencing of RNA [SLAM-seq in wildtype and Mettl3 knockout mES cells]

ABSTRACT: Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady-state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), an orthogonal chemistry-based epitranscriptomics-sequencing technology that uncovers 4-thiouridine (s4U)-incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM-seq enables rapid access to RNA polymerase II-dependent gene expression dynamics in the context of total RNA. When applied to mouse embryonic stem cells, SLAM-seq provides global and transcript-specific insights into pluripotency-associated gene expression. We validated the method by showing that the RNA-polymerase II-dependent transcriptional output scales with Oct4/Sox2/Nanog-defined enhancer activity; and provides quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N6-methyladenosine. SLAM-seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective, and scalable manner.

ORGANISM(S): Mus musculus

PROVIDER: GSE99974 | GEO | 2017/09/23

SECONDARY ACCESSION(S): PRJNA390265

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Thiol-linked alkylation for the metabolic sequencing of RNA [Primer Extension Assays]

Project description:Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady-state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), an orthogonal chemistry-based epitranscriptomics-sequencing technology that uncovers 4-thiouridine (s4U)-incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM-seq enables rapid access to RNA polymerase II-dependent gene expression dynamics in the context of total RNA. When applied to mouse embryonic stem cells, SLAM-seq provides global and transcript-specific insights into pluripotency-associated gene expression. We validated the method by showing that the RNA-polymerase II-dependent transcriptional output scales with Oct4/Sox2/Nanog-defined enhancer activity; and provides quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N6-methyladenosine. SLAM-seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective, and scalable manner.

2017-09-23 | GSE99969 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [Transcriptional inhibition by Actinomycin D]

2017-09-23 | GSE99975 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [SLAM-seq in wildtype and Xpo5 knockout mES cells]

2017-09-23 | GSE99973 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [RNA-seq and Cap-seq of wildtype mES cells]

2017-09-23 | GSE99971 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [SLAM-seq pulse/chase labeling in wildtype mES cells]

2017-09-23 | GSE99970 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [SLAM-seq of wildtype mES cell RNA +/- iodoacetamide treatment]

2017-09-23 | GSE99977 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [Transcriptional output measurement by SLAM-seq in wildtype mES cells]

2017-09-23 | GSE99972 | GEO

Thiol-linked alkylation for the metabolic sequencing of RNA [Small RNA sequencing in wildtype and Xpo5 knockout mES cells]

2017-09-23 | GSE99976 | GEO

Transcriptome Dynamics in Mouse Amygdala under Acute and Chronic Stress Revealed by Thiol-labeled RNA Sequencing

Project description:Both acute and chronic stress have significant impact on brain functions. The amygdala is essential in mediating stress responses, but how its transcriptomic dynamics change under stress remains elusive. To overcome the difficulties in detecting subtle stress-induced changes by evaluating total RNA using classic RNA sequencing, we conducted thiol-labeled RNA sequencing (SLAM-seq). We injected 4-thiouridine (4sU) into mouse amygdala followed by SLAM-seq to detect nascent mRNA induced by acute and chronic restraint stress, and found that SLAM-seq could label actively transcribed genes in the major neuronal and glial subtypes. Using SLAM-seq, we found that chronic stress led to higher turnover of a group of genes associated with myelination, and this finding is confirmed by immunostaining which showed increased myelination in the chronically stressed amygdala. Additionally, genes detected by SLAM-seq and RNA-seq only partially overlapped, with SLAM-seq particularly sensitive to transcriptional changes in genes with high basal transcription levels. Thus, by applying SLAM-seq in vivo, we obtained a rich dataset of nascently transcribed genes in the amygdala under stress, and revealed distinct transcriptional dynamics associated with acute and chronic stress.

2024-08-14 | GSE274148 | GEO

Improving the study of RNA dynamics through advances in RNA-seq with metabolic labeling and nucleotide-recoding chemistry

Project description:RNA metabolic labeling using 4-thiouridine (s4U) can be used to capture the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s4U-labeled reads in a process we refer to as dropout. Here we show that s4U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotiderecoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s4U from a uridine analog to a cytidine analog and the apparent T-to-C mutations are then used to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibiltiy of NR-seq experiments.

2023-05-25 | GSE233139 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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