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Related Experiment Video

Updated: Jun 12, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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A protocol for time-resolved transcriptomics through metabolic labeling and combinatorial indexing.

Rory J Maizels1, Daniel M Snell2, James Briscoe2

  • 1The Francis Crick Institute, 1 Midland Road, NW1 1AT London, UK; University College London, London, UK.

STAR Protocols
|October 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized protocol for metabolic labeling of RNA using 4-thiouridine (4sU) combined with single-cell RNA sequencing. This method enhances the study of gene expression dynamics at the single-cell level.

Keywords:
Cell cultureDevelopmental biologyGene ExpressionMolecular BiologyMolecular/Chemical ProbesRNAseqSingle Cell

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Area of Science:

  • Molecular Biology
  • Genomics
  • Biotechnology

Background:

  • Single-cell transcriptomics offers a snapshot of gene expression but struggles to capture dynamic changes.
  • Metabolic labeling with 4-thiouridine (4sU) enables temporal RNA profiling at the single-cell level.
  • Existing protocols may lack optimization for automation and integration with single-cell RNA sequencing.

Purpose of the Study:

  • To present an optimized and automation-friendly protocol for metabolic RNA labeling coupled with single-cell RNA sequencing.
  • To enable the study of gene expression dynamics in individual cells.
  • To provide a robust method for capturing temporal information in RNA expression.

Main Methods:

  • Optimization of 4-thiouridine (4sU) metabolic labeling for nascent RNA.
  • Development of cell fixation and chemical treatment procedures compatible with labeling.
  • Implementation of automated two-level combinatorial indexing for single-cell RNA sequencing.
  • Integration of metabolic labeling with single-cell RNA sequencing workflow.

Main Results:

  • An optimized protocol for 4sU metabolic labeling and single-cell RNA sequencing is described.
  • The protocol is designed to be automation-friendly, facilitating high-throughput applications.
  • The method allows for the capture of temporal gene expression dynamics at the single-cell level.

Conclusions:

  • The presented protocol offers a powerful tool for investigating dynamic gene expression in single cells.
  • This approach overcomes limitations of static transcriptomic snapshots.
  • The automation-friendly design promotes broader accessibility and application in biological research.