ArticleBMC genomics2008
Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells.
Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Understanding isoform expression by pairing long-read sequencing with single-cell and spatial transcriptomics.Genome research · 2024Review
- Gene regulatory network stabilized by pervasive weak repressions: microRNA functions revealed by the May-Wigner theory.National science review · 2019Article
- Defining Cell Identity with Single-Cell Omics.Proteomics · 2018Review
- microRNA target prediction programs predict many false positives.Genome research · 2017Article
- Analyses of herpes simplex virus type 1 latency and reactivation at the single cell level using fluorescent reporter mice.The Journal of general virology · 2016Article
- Selection strategies for anticancer antibody discovery: searching off the beaten path.Trends in biotechnology · 2015Review
- Unmasking Upstream Gene Expression Regulators with miRNA-corrected mRNA Data.Bioinformatics and biology insights · 2015Article
- Current challenges in the bioinformatics of single cell genomics.Frontiers in oncology · 2014Review
- Single cell genomics: advances and future perspectives.PLoS genetics · 2014Review
- Patch-clamp recordings and calcium imaging followed by single-cell PCR reveal the developmental profile of 13 genes in iPSC-derived human neurons.Stem cell research · 2014Article
- Article
- High-Throughput Single-Cell Analysis for Wound Healing Applications.Advances in wound care · 2013Review
- Building stem-cell genomics in California and beyond.Nature biotechnology · 2012Article
- A high-throughput DNA methylation analysis of a single cell.Nucleic acids research · 2011Article
- Quantitative prediction of miRNA-mRNA interaction based on equilibrium concentrations.PLoS computational biology · 2011Article
- Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling.Frontiers in molecular neuroscience · 2010Article
- Article
- A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome.BMC genomics · 2008Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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