Abstract
BackgroundPhenotypically 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.ResultsWe 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.ConclusionOur 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.
Highlights
IntroductionThere is uncertainty as to whether phenotypically identical cells are similar at the underlying transcriptional level or if cellular systems are inherently noisy
We investigated whether observed variations in gene expression levels in single cell samples could be artifacts of the experimental method, how much sampling effects contribute to variability in single cell expression measurements, and, if global amplification techniques can be reliably used for the detection of differences in gene expression among single cells
We conclude 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
Summary
There is uncertainty as to whether phenotypically identical cells are 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. As our ability to investigate molecular mechanisms in biology at finer resolutions improves, there is increasing interest in generating reliable gene expression profiles for smaller biological samples, down to the level of the single cell and potentially subcellular compartments. Single-cell gene expression profiling provides a powerful tool to analyze the composition of complex cell populations [1]. There are many contexts in which the focus is shifting towards understanding the cellular networks of individual cells [2,3] and the similarities and differences between individual cells at the transcriptional and translational level [4,5]
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