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Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells.
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.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
Grouping and Classifying Electrophysiologically-Defined Classes of Neocortical Neurons by Single Cell, Whole-Genome Expression Profiling
The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts
Dataset for "A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest"
These are the original datasets underlying the results in the paper "A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest" that were not included directly in the Results section or Supplementary Information. Specifically, they include measurements of pnp4a and tfec expression levels after transcription factor overexpression, and counts in WT and mutant embryos of cells expressing defined marker genes.For details of the methodology used, see the Materials and Methods section of the associated manuscript
Dataset for "A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest"
These are the original datasets underlying the results in the paper "A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest" that were not included directly in the Results section or Supplementary Information. Specifically, they include measurements of pnp4a and tfec expression levels after transcription factor overexpression, and counts in WT and mutant embryos of cells expressing defined marker genes