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Related Experiment Video

Updated: May 1, 2026

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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Validation of noise models for single-cell transcriptomics.

Dominic Grün1, Lennart Kester1, Alexander van Oudenaarden2

  • 11] Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands. [2] University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands. [3].

Nature Methods
|April 22, 2014
PubMed
Summary
This summary is machine-generated.

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Single-cell transcriptomics reveals technical noise sources like sampling and sequencing efficiency. Correcting these reveals that mouse stem cell gene expression varies with culture conditions.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single-cell transcriptomics is a key technology for studying gene expression heterogeneity.
  • Technical variability can obscure biological signals in single-cell data.
  • Understanding noise sources is crucial for accurate interpretation.

Purpose of the Study:

  • To identify and model technical variability in single-cell transcriptomics.
  • To develop methods for correcting technical noise.
  • To investigate the impact of culture conditions on gene expression variability.

Main Methods:

  • Analysis of technical noise sources including sampling noise and sequencing efficiency variations.
  • Development and application of noise models for data correction.

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Related Experiment Videos

Last Updated: May 1, 2026

Transcriptome Analysis of Single Cells
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An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level
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  • Validation using single-molecule Fluorescence In Situ Hybridization (smFISH).
  • Comparison of gene expression variability under different culture conditions.

    • Main Results:

    • Identified sampling noise and global cell-to-cell sequencing efficiency variation as major technical noise sources.
    • Developed noise models that effectively correct for technical variability.
    • Validated model performance using smFISH.
    • Demonstrated that gene expression variability in mouse embryonic stem cells is influenced by culture conditions.

    Conclusions:

    • Accurate modeling and correction of technical noise are essential for robust single-cell transcriptomics analysis.
    • The proposed noise models improve the reliability of gene expression variability assessments.
    • Culture conditions significantly impact the biological variability observed in mouse embryonic stem cells.