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Related Concept Videos

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Related Experiment Video

Updated: Jan 19, 2026

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

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Evaluating stably expressed genes in single cells.

Yingxin Lin1, Shila Ghazanfar1,2, Dario Strbenac1

  • 1School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.

Gigascience
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

We identified stably expressed genes (SEGs) in single cells using a computational framework. These SEGs are more stable than traditional housekeeping genes and useful for single-cell RNA sequencing data normalization.

Keywords:
gene expression variabilityhousekeeping genesscRNA-seqsingle cellsstably expressed genes

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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Area of Science:

  • Transcriptomics
  • Computational Biology
  • Genetics

Background:

  • Single-cell RNA sequencing (scRNA-seq) reveals inherent gene expression noise.
  • Traditionally, housekeeping genes (HKGs) show stable expression at the population level.
  • Identifying stably expressed genes (SEGs) at the single-cell level is crucial.

Purpose of the Study:

  • To evaluate and characterize SEGs identified using a computational framework.
  • To assess the stability and utility of SEGs in single-cell transcriptomics.
  • To determine if SEGs can be identified and reliably quantified at the single-cell level.

Main Methods:

  • Utilized a computational framework to rank gene expression stability in single cells.
  • Analyzed scRNA-seq datasets from early human and mouse development, and the "Mouse Atlas" dataset.
  • Compared the stability of identified SEGs with previously defined HKGs.

Main Results:

  • Gene expression stability indices were reproducible and conserved across species.
  • Identified SEGs demonstrated significantly greater stability than population-level HKGs.
  • SEGs exhibit characteristics similar to HKGs, suggesting roles in essential cellular functions.

Conclusions:

  • SEGs identified via the scSEGIndex framework are robust and conserved.
  • SEGs offer utility for understanding single-cell transcriptome variation and stability.
  • The scSEGIndex framework is integrated into the scMerge R package for practical application in scRNA-seq data analysis.