PLX061370

GSE115612: scSLAM-seq reveals core features of transcription dynamics in single cells

Organsim mouse
Type RNASEQ
Target gene
Project ARCHS4

Single cell RNA sequencing so far only depicts cellular transcriptomes at a single time point with low temporal resolution for kinetic changes. Here, we present a single-cell approach based on metabolic RNA labelling for combined sequencing of total and newly transcribed RNA as well as computational analysis of the RNA present prior to labelling. We apply it to cytomegalovirus infection of fibroblasts and reveal its potential for delineating alterations in transcriptional activity and decay in single cells under perturbed experimental conditions. SOURCE: Antoine-Emmanuel Saliba (emmanuel.saliba@helmholtz-hzi.de) - Helmholtz Institute for RNA-based Infection Research

View on GEO View in Pluto

Key Features

Enhance your research with our curated data sets and powerful platform features. Pluto Bio makes it simple to find and use the data you need.

Learn More

14K+ Published Experiments

Access an extensive range of curated bioinformatics data sets, including genomic, transcriptomic, and proteomic data.

Easy Data Import

Request imports from GEO or TCGA directly within Pluto Bio. Seamlessly integrate external data sets into your workflow.

Advanced Search Capabilities

Utilize powerful search tools to quickly find the data sets relevant to your research. Filter by type, disease, gene, and more.

Analyze and visualize data for this experiment

Use Pluto's intuitive interface to analyze and visualize data for this experiment. Pluto's platform is equipped with an API & SDKs, making it easy to integrate into your internal bioinformatics processes.

Read about post-pipeline analysis

View QC data and experiment metadata

View quality control data and experiment metadata for this experiment.

Request import of other GEO data

Request imports from GEO or TCGA directly within Pluto Bio.

Chat with our Scientific Insights team