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Updated: Jul 4, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
Published on: February 25, 2017
Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among
Tatiana Subkhankulova1, Michael J Gilchrist, Frederick J Livesey
1Gurdon Institute and Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1 QN, UK. subkhankul@hotmail.com
Phenotypically identical cells show significant gene expression differences in vivo. These variations exceed technical noise from global mRNA amplification, revealing inherent cellular system variability.
Area of Science:
- Cellular biology
- Transcriptomics
- Developmental biology
Background:
- Phenotypically identical cells exhibit consistent behaviors, but their underlying transcriptional similarity is debated.
- Distinguishing true biological variation from technical noise is crucial for understanding cellular systems.
- Sampling effects from mRNA amplification can influence measurements of transcript abundance.
Purpose of the Study:
- To investigate transcriptional differences between phenotypically identical cells.
- To differentiate biological noise from technical noise in single-cell gene expression data.
Main Methods:
- Developed mathematical models using single-cell microarray data.
- Performed Monte Carlo simulations to assess technical and sampling effects.
- Compared simulation results with experimental microarray data from neural stem cells.
Main Results:
- The distribution of gene expression ratios between single cells was broader than predicted by sampling effect models.
- Experimental data indicated greater variability than accounted for by technical noise and sampling effects.
Conclusions:
- Significant gene expression differences exist between phenotypically identical cells in vivo.
- These biological variations surpass noise introduced by global mRNA amplification.
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