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Related Experiment Video

Updated: Mar 27, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Gene expression variability in clonal populations: Causes and consequences.

Stefanie Roberfroid¹, Jos Vanderleyden¹, Hans Steenackers¹

¹a Department of Microbial and Molecular Systems , Centre of Microbial and Plant Genetics, KU Leuven , Leuven , Belgium.

Critical Reviews in Microbiology

|January 6, 2016

Summary

This summary is machine-generated.

Cellular gene expression varies due to intrinsic and extrinsic factors, influencing clonal populations. New methods are needed to analyze single-cell gene expression, moving beyond population averages.

Keywords:

Bistability phenotypic plasticity single-cell stochasticity

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Molecular Biology
Genetics
Cell Biology

Background:

Cellular variation in gene expression is crucial within clonal populations.
Mechanisms include intrinsic noise, extrinsic noise, bistability, phenotypic plasticity, clonal evolution, and phase variation.

Purpose of the Study:

To provide an overview of mechanisms contributing to gene expression variability in clonal populations.
To discuss adaptations of existing techniques and novel methods for single-cell gene expression analysis.

Main Methods:

Review of gene expression variability mechanisms.
Adaptation of population-based techniques (microarrays, RNA sequencing, qPCR, reporter fusions) for single-cell analysis via isolation, amplification, and microscopy.
Application of selection-based techniques (mutant screenings, IVET, RIVET).
Discussion of differential fluorescence induction (DFI).

Main Results:

Gene expression variability arises from biochemical processes, micro-environmental responses, and genotypic changes.
Traditional methods quantify population averages, necessitating single-cell approaches.
Techniques like single-cell isolation, RNA amplification, microscopy, and specialized protocols (IVET, RIVET, DFI) enable single-cell analysis.

Conclusions:

Understanding gene expression variability is essential for clonal population studies.
Single-cell analysis techniques are critical to capture expression fluctuations missed by population-based methods.
Adapted and novel methods offer powerful tools for dissecting gene expression heterogeneity at the single-cell level.