TITLE

Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells

AUTHOR(S)
Subkhankulova, Tatiana; Gilchrist, Michael J.; Livesey, Frederick J.
PUB. DATE
January 2008
SOURCE
BMC Genomics;2008, Vol. 9, Special section p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Phenotypically identical cells demonstrate predictable, robust behaviours. However, there is uncertainty as to whether phenotypically identical cells are equally similar at the underlying transcriptional level or if cellular systems are inherently noisy. To answer this question, it is essential to distinguish between technical noise and true variation in transcript levels. A critical issue is the contribution of sampling effects, introduced by the requirement to globally amplify the single cell mRNA population, to observed measurements of relative transcript abundance. Results: We used single cell microarray data to develop simple mathematical models, ran Monte Carlo simulations of the impact of technical and sampling effects on single cell expression data, and compared these with experimental microarray data generated from single embryonic neural stem cells in vivo. We show that the actual distribution of measured gene expression ratios for pairs of neural stem cells is much broader than that predicted from our sampling effect model. Conclusion: Our results confirm that significant differences in gene expression levels exist between phenotypically identical cells in vivo, and that these differences exceed any noise contribution from global mRNA amplification.
ACCESSION #
38122851

 

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