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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Related Experiment Video

Updated: Jun 15, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

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

Published on: October 25, 2018

Identifying single-cell molecular programs by stochastic profiling.

Kevin A Janes1, Chun-Chao Wang, Karin J Holmberg

  • 1Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. kjanes@virginia.edu

Nature Methods
|March 16, 2010
PubMed
Summary
This summary is machine-generated.

Stochastic profiling reveals hidden gene expression differences in seemingly identical cells. This method identifies co-regulated genes with cell-to-cell variations, uncovering molecular programs without single-cell analysis.

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Last Updated: Jun 15, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

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

Published on: February 25, 2017

Area of Science:

  • Cellular and Molecular Biology
  • Genomics
  • Biotechnology

Background:

  • Cellular morphology can mask significant molecular heterogeneity.
  • Understanding gene expression variability is crucial for tissue function and disease.
  • Existing methods often require individual cell isolation for detailed analysis.

Purpose of the Study:

  • To develop a method for identifying co-regulated, heterogeneously expressed genes in morphologically similar cells.
  • To reveal underlying molecular programs driving cell-to-cell expression differences.
  • To demonstrate the utility of stochastic profiling in a relevant biological context.

Main Methods:

  • Stochastic profiling: repeated, random sampling of small cell populations using laser-capture microdissection.
  • Customized single-cell amplification and transcriptional profiling.
  • Statistical analysis of gene expression fluctuations to identify coexpressed genes.

Main Results:

  • Identified 547 genes with significant cell-to-cell expression differences among 4,557 profiled transcripts.
  • Clustering revealed molecular programs related to protein biosynthesis, oxidative stress, and NF-kappaB signaling.
  • Heterogeneous gene expression patterns were confirmed using RNA fluorescence in situ hybridization.

Conclusions:

  • Stochastic profiling effectively detects single-cell gene expression heterogeneities without measuring individual cells.
  • This technique uncovers functional molecular programs within seemingly uniform cell populations.
  • Provides a powerful approach for studying cellular heterogeneity in various biological systems.